Intelligent Design The Definitive Source on Intelligent Design

Not a Chance

On August 4th, 2004 an extensive review essay by Dr. Stephen C. Meyer, Director of Discovery Institute's Center for Science & Culture appeared in the Proceedings of the Biological Society of Washington (volume 117, no. 2, pp. 213-239). The Proceedings is a peer-reviewed biology journal published at the National Museum of Natural History at the Smithsonian Institution in Washington D.C.

In the article, entitled "The Origin of Biological Information and the Higher Taxonomic Categories", Dr. Meyer argues that no current materialistic theory of evolution can account for the origin of the information necessary to build novel animal forms. He proposes intelligent design as an alternative explanation for the origin of biological information and the higher taxa.

Due to an unusual number of inquiries about the article, Dr. Meyer, the copyright holder, has decided to make the article available now in HTML format on this website. (Off prints are also available from Discovery Institute by writing to Rob Crowther at: cscinfo@discovery.org. Please provide your mailing address and we will dispatch a copy).

Introduction

In a recent volume of the Vienna Series in a Theoretical Biology (2003), Gerd B. Muller and Stuart Newman argue that what they call the "origination of organismal form" remains an unsolved problem. In making this claim, Muller and Newman (2003:3-10) distinguish two distinct issues, namely, (1) the causes of form generation in the individual organism during embryological development and (2) the causes responsible for the production of novel organismal forms in the first place during the history of life. To distinguish the latter case (phylogeny) from the former (ontogeny), Muller and Newman use the term "origination" to designate the causal processes by which biological form first arose during the evolution of life. They insist that "the molecular mechanisms that bring about biological form in modern day embryos should not be confused" with the causes responsible for the origin (or "origination") of novel biological forms during the history of life (p.3). They further argue that we know more about the causes of ontogenesis, due to advances in molecular biology, molecular genetics and developmental biology, than we do about the causes of phylogenesis--the ultimate origination of new biological forms during the remote past.

In making this claim, Muller and Newman are careful to affirm that evolutionary biology has succeeded in explaining how preexisting forms diversify under the twin influences of natural selection and variation of genetic traits. Sophisticated mathematically-based models of population genetics have proven adequate for mapping and understanding quantitative variability and populational changes in organisms. Yet Muller and Newman insist that population genetics, and thus evolutionary biology, has not identified a specifically causal explanation for the origin of true morphological novelty during the history of life. Central to their concern is what they see as the inadequacy of the variation of genetic traits as a source of new form and structure. They note, following Darwin himself, that the sources of new form and structure must precede the action of natural selection (2003:3)--that selection must act on what already exists. Yet, in their view, the "genocentricity" and "incrementalism" of the neo-Darwinian mechanism has meant that an adequate source of new form and structure has yet to be identified by theoretical biologists. Instead, Muller and Newman see the need to identify epigenetic sources of morphological innovation during the evolution of life. In the meantime, however, they insist neo-Darwinism lacks any "theory of the generative" (p. 7).

As it happens, Muller and Newman are not alone in this judgment. In the last decade or so a host of scientific essays and books have questioned the efficacy of selection and mutation as a mechanism for generating morphological novelty, as even a brief literature survey will establish. Thomson (1992:107) expressed doubt that large-scale morphological changes could accumulate via minor phenotypic changes at the population genetic level. Miklos (1993:29) argued that neo-Darwinism fails to provide a mechanism that can produce large-scale innovations in form and complexity. Gilbert et al. (1996) attempted to develop a new theory of evolutionary mechanisms to supplement classical neo-Darwinism, which, they argued, could not adequately explain macroevolution. As they put it in a memorable summary of the situation: "starting in the 1970s, many biologists began questioning its (neo-Darwinism's) adequacy in explaining evolution. Genetics might be adequate for explaining microevolution, but microevolutionary changes in gene frequency were not seen as able to turn a reptile into a mammal or to convert a fish into an amphibian. Microevolution looks at adaptations that concern the survival of the fittest, not the arrival of the fittest. As Goodwin (1995) points out, 'the origin of species--Darwin's problem--remains unsolved'" (p. 361). Though Gilbert et al. (1996) attempted to solve the problem of the origin of form by proposing a greater role for developmental genetics within an otherwise neo-Darwinian framework,1 numerous recent authors have continued to raise questions about the adequacy of that framework itself or about the problem of the origination of form generally (Webster & Goodwin 1996; Shubin & Marshall 2000; Erwin 2000; Conway Morris 2000, 2003b; Carroll 2000; Wagner 2001; Becker & Lonnig 2001; Stadler et al. 2001; Lonnig & Saedler 2002; Wagner & Stadler 2003; Valentine 2004:189-194).

What lies behind this skepticism? Is it warranted? Is a new and specifically causal theory needed to explain the origination of biological form?

This review will address these questions. It will do so by analyzing the problem of the origination of organismal form (and the corresponding emergence of higher taxa) from a particular theoretical standpoint. Specifically, it will treat the problem of the origination of the higher taxonomic groups as a manifestation of a deeper problem, namely, the problem of the origin of the information (whether genetic or epigenetic) that, as it will be argued, is necessary to generate morphological novelty.

In order to perform this analysis, and to make it relevant and tractable to systematists and paleontologists, this paper will examine a paradigmatic example of the origin of biological form and information during the history of life: the Cambrian explosion. During the Cambrian, many novel animal forms and body plans (representing new phyla, subphyla and classes) arose in a geologically brief period of time. The following information-based analysis of the Cambrian explosion will support the claim of recent authors such as Muller and Newman that the mechanism of selection and genetic mutation does not constitute an adequate causal explanation of the origination of biological form in the higher taxonomic groups. It will also suggest the need to explore other possible causal factors for the origin of form and information during the evolution of life and will examine some other possibilities that have been proposed.

The Cambrian Explosion

The "Cambrian explosion" refers to the geologically sudden appearance of many new animal body plans about 530 million years ago. At this time, at least nineteen, and perhaps as many as thirty-five phyla of forty total (Meyer et al. 2003), made their first appearance on earth within a narrow five- to ten-million-year window of geologic time (Bowring et al. 1993, 1998a:1, 1998b:40; Kerr 1993; Monastersky 1993; Aris-Brosou & Yang 2003). Many new subphyla, between 32 and 48 of 56 total (Meyer et al. 2003), and classes of animals also arose at this time with representatives of these new higher taxa manifesting significant morphological innovations. The Cambrian explosion thus marked a major episode of morphogenesis in which many new and disparate organismal forms arose in a geologically brief period of time.

To say that the fauna of the Cambrian period appeared in a geologically sudden manner also implies the absence of clear transitional intermediate forms connecting Cambrian animals with simpler pre-Cambrian forms. And, indeed, in almost all cases, the Cambrian animals have no clear morphological antecedents in earlier Vendian or Precambrian fauna (Miklos 1993, Erwin et al. 1997:132, Steiner & Reitner 2001, Conway Morris 2003b:510, Valentine et al. 2003:519-520). Further, several recent discoveries and analyses suggest that these morphological gaps may not be merely an artifact of incomplete sampling of the fossil record (Foote 1997, Foote et al. 1999, Benton & Ayala 2003, Meyer et al. 2003), suggesting that the fossil record is at least approximately reliable (Conway Morris 2003b:505).

As a result, debate now exists about the extent to which this pattern of evidence comports with a strictly monophyletic view of evolution (Conway Morris 1998a, 2003a, 2003b:510; Willmer 1990, 2003). Further, among those who accept a monophyletic view of the history of life, debate exists about whether to privilege fossil or molecular data and analyses. Those who think the fossil data provide a more reliable picture of the origin of the Metazoan tend to think these animals arose relatively quickly--that the Cambrian explosion had a "short fuse." (Conway Morris 2003b:505-506, Valentine & Jablonski 2003). Some (Wray et al. 1996), but not all (Ayala et al. 1998), who think that molecular phylogenies establish reliable divergence times from pre-Cambrian ancestors think that the Cambrian animals evolved over a very long period of time--that the Cambrian explosion had a "long fuse." This review will not address these questions of historical pattern. Instead, it will analyze whether the neo-Darwinian process of mutation and selection, or other processes of evolutionary change, can generate the form and information necessary to produce the animals that arise in the Cambrian. This analysis will, for the most part, 2 therefore, not depend upon assumptions of either a long or short fuse for the Cambrian explosion, or upon a monophyletic or polyphyletic view of the early history of life.

Defining Biological Form and Information

Form, like life itself, is easy to recognize but often hard to define precisely. Yet, a reasonable working definition of form will suffice for our present purposes. Form can be defined as the four-dimensional topological relations of anatomical parts. This means that one can understand form as a unified arrangement of body parts or material components in a distinct shape or pattern (topology)--one that exists in three spatial dimensions and which arises in time during ontogeny.

Insofar as any particular biological form constitutes something like a distinct arrangement of constituent body parts, form can be seen as arising from constraints that limit the possible arrangements of matter. Specifically, organismal form arises (both in phylogeny and ontogeny) as possible arrangements of material parts are constrained to establish a specific or particular arrangement with an identifiable three dimensional topography--one that we would recognize as a particular protein, cell type, organ, body plan or organism. A particular "form," therefore, represents a highly specific and constrained arrangement of material components (among a much larger set of possible arrangements).

Understanding form in this way suggests a connection to the notion of information in its most theoretically general sense. When Shannon (1948) first developed a mathematical theory of information he equated the amount of information transmitted with the amount of uncertainty reduced or eliminated in a series of symbols or characters. Information, in Shannon's theory, is thus imparted as some options are excluded and others are actualized. The greater the number of options excluded, the greater the amount of information conveyed. Further, constraining a set of possible material arrangements by whatever process or means involves excluding some options and actualizing others. Thus, to constrain a set of possible material states is to generate information in Shannon's sense. It follows that the constraints that produce biological form also imparted information. Or conversely, one might say that producing organismal form by definition requires the generation of information.

In classical Shannon information theory, the amount of information in a system is also inversely related to the probability of the arrangement of constituents in a system or the characters along a communication channel (Shannon 1948). The more improbable (or complex) the arrangement, the more Shannon information, or information-carrying capacity, a string or system possesses.

Since the 1960s, mathematical biologists have realized that Shannon's theory could be applied to the analysis of DNA and proteins to measure the information-carrying capacity of these macromolecules. Since DNA contains the assembly instructions for building proteins, the information-processing system in the cell represents a kind of communication channel (Yockey 1992:110). Further, DNA conveys information via specifically arranged sequences of nucleotide bases. Since each of the four bases has a roughly equal chance of occurring at each site along the spine of the DNA molecule, biologists can calculate the probability, and thus the information-carrying capacity, of any particular sequence n bases long.

The ease with which information theory applies to molecular biology has created confusion about the type of information that DNA and proteins possess. Sequences of nucleotide bases in DNA, or amino acids in a protein, are highly improbable and thus have large information-carrying capacities. But, like meaningful sentences or lines of computer code, genes and proteins are also specified with respect to function. Just as the meaning of a sentence depends upon the specific arrangement of the letters in a sentence, so too does the function of a gene sequence depend upon the specific arrangement of the nucleotide bases in a gene. Thus, molecular biologists beginning with Crick equated information not only with complexity but also with "specificity," where "specificity" or "specified" has meant "necessary to function" (Crick 1958:144, 153; Sarkar, 1996:191).3 Molecular biologists such as Monod and Crick understood biological information--the information stored in DNA and proteins--as something more than mere complexity (or improbability). Their notion of information associated both biochemical contingency and combinatorial complexity with DNA sequences (allowing DNA's carrying capacity to be calculated), but it also affirmed that sequences of nucleotides and amino acids in functioning macromolecules possessed a high degree of specificity relative to the maintenance of cellular function.

The ease with which information theory applies to molecular biology has also created confusion about the location of information in organisms. Perhaps because the information carrying capacity of the gene could be so easily measured, it has been easy to treat DNA, RNA and proteins as the sole repositories of biological information. Neo-Darwinists in particular have assumed that the origination of biological form could be explained by recourse to processes of genetic variation and mutation alone (Levinton 1988:485). Yet if one understands organismal form as resulting from constraints on the possible arrangements of matter at many levels in the biological hierarchy--from genes and proteins to cell types and tissues to organs and body plans--then clearly biological organisms exhibit many levels of information-rich structure.

Thus, we can pose a question, not only about the origin of genetic information, but also about the origin of the information necessary to generate form and structure at levels higher than that present in individual proteins. We must also ask about the origin of the "specified complexity," as opposed to mere complexity, that characterizes the new genes, proteins, cell types and body plans that arose in the Cambrian explosion. Dembski (2002) has used the term "complex specified information" (CSI) as a synonym for "specified complexity" to help distinguish functional biological information from mere Shannon information--that is, specified complexity from mere complexity. This review will use this term as well.

The Cambrian Information Explosion

The Cambrian explosion represents a remarkable jump in the specified complexity or "complex specified information" (CSI) of the biological world. For over three billions years, the biological realm included little more than bacteria and algae (Brocks et al. 1999). Then, beginning about 570-565 million years ago (mya), the first complex multicellular organisms appeared in the rock strata, including sponges, cnidarians, and the peculiar Ediacaran biota (Grotzinger et al. 1995). Forty million years later, the Cambrian explosion occurred (Bowring et al. 1993). The emergence of the Ediacaran biota (570 mya), and then to a much greater extent the Cambrian explosion (530 mya), represented steep climbs up the biological complexity gradient.

One way to estimate the amount of new CSI that appeared with the Cambrian animals is to count the number of new cell types that emerged with them (Valentine 1995:91-93). Studies of modern animals suggest that the sponges that appeared in the late Precambrian, for example, would have required five cell types, whereas the more complex animals that appeared in the Cambrian (e.g., arthropods) would have required fifty or more cell types. Functionally more complex animals require more cell types to perform their more diverse functions. New cell types require many new and specialized proteins. New proteins, in turn, require new genetic information. Thus an increase in the number of cell types implies (at a minimum) a considerable increase in the amount of specified genetic information. Molecular biologists have recently estimated that a minimally complex single-celled organism would require between 318 and 562 kilobase pairs of DNA to produce the proteins necessary to maintain life (Koonin 2000). More complex single cells might require upward of a million base pairs. Yet to build the proteins necessary to sustain a complex arthropod such as a trilobite would require orders of magnitude more coding instructions. The genome size of a modern arthropod, the fruitfly Drosophila melanogaster, is approximately 180 million base pairs (Gerhart & Kirschner 1997:121, Adams et al. 2000). Transitions from a single cell to colonies of cells to complex animals represent significant (and, in principle, measurable) increases in CSI.

Building a new animal from a single-celled organism requires a vast amount of new genetic information. It also requires a way of arranging gene products--proteins--into higher levels of organization. New proteins are required to service new cell types. But new proteins must be organized into new systems within the cell; new cell types must be organized into new tissues, organs, and body parts. These, in turn, must be organized to form body plans. New animals, therefore, embody hierarchically organized systems of lower-level parts within a functional whole. Such hierarchical organization itself represents a type of information, since body plans comprise both highly improbable and functionally specified arrangements of lower-level parts. The specified complexity of new body plans requires explanation in any account of the Cambrian explosion.

Can neo-Darwinism explain the discontinuous increase in CSI that appears in the Cambrian explosion--either in the form of new genetic information or in the form of hierarchically organized systems of parts? We will now examine the two parts of this question.

Novel Genes and Proteins

Many scientists and mathematicians have questioned the ability of mutation and selection to generate information in the form of novel genes and proteins. Such skepticism often derives from consideration of the extreme improbability (and specificity) of functional genes and proteins.

A typical gene contains over one thousand precisely arranged bases. For any specific arrangement of four nucleotide bases of length n, there is a corresponding number of possible arrangements of bases, 4n. For any protein, there are 20n possible arrangements of protein-forming amino acids. A gene 999 bases in length represents one of 4999 possible nucleotide sequences; a protein of 333 amino acids is one of 20333 possibilities.

Since the 1960s, some biologists have thought functional proteins to be rare among the set of possible amino acid sequences. Some have used an analogy with human language to illustrate why this should be the case. Denton (1986, 309-311), for example, has shown that meaningful words and sentences are extremely rare among the set of possible combinations of English letters, especially as sequence length grows. (The ratio of meaningful 12-letter words to 12-letter sequences is 1/1014, the ratio of 100-letter sentences to possible 100-letter strings is 1/10100.) Further, Denton shows that most meaningful sentences are highly isolated from one another in the space of possible combinations, so that random substitutions of letters will, after a very few changes, inevitably degrade meaning. Apart from a few closely clustered sentences accessible by random substitution, the overwhelming majority of meaningful sentences lie, probabilistically speaking, beyond the reach of random search.

Denton (1986:301-324) and others have argued that similar constraints apply to genes and proteins. They have questioned whether an undirected search via mutation and selection would have a reasonable chance of locating new islands of function--representing fundamentally new genes or proteins--within the time available (Eden 1967, Shutzenberger 1967, Lovtrup 1979). Some have also argued that alterations in sequencing would likely result in loss of protein function before fundamentally new function could arise (Eden 1967, Denton 1986). Nevertheless, neither the extent to which genes and proteins are sensitive to functional loss as a result of sequence change, nor the extent to which functional proteins are isolated within sequence space, has been fully known.

Recently, experiments in molecular biology have shed light on these questions. A variety of mutagenesis techniques have shown that proteins (and thus the genes that produce them) are indeed highly specified relative to biological function (Bowie & Sauer 1989, Reidhaar-Olson & Sauer 1990, Taylor et al. 2001). Mutagenesis research tests the sensitivity of proteins (and, by implication, DNA) to functional loss as a result of alterations in sequencing. Studies of proteins have long shown that amino acid residues at many active positions cannot vary without functional loss (Perutz & Lehmann 1968). More recent protein studies (often using mutagenesis experiments) have shown that functional requirements place significant constraints on sequencing even at non-active site positions (Bowie & Sauer 1989, Reidhaar-Olson & Sauer 1990, Chothia et al. 1998, Axe 2000, Taylor et al. 2001). In particular, Axe (2000) has shown that multiple as opposed to single position amino acid substitutions inevitably result in loss of protein function, even when these changes occur at sites that allow variation when altered in isolation. Cumulatively, these constraints imply that proteins are highly sensitive to functional loss as a result of alterations in sequencing, and that functional proteins represent highly isolated and improbable arrangements of amino acids -arrangements that are far more improbable, in fact, than would be likely to arise by chance alone in the time available (Reidhaar-Olson & Sauer 1990; Behe 1992; Kauffman 1995:44; Dembski 1998:175-223; Axe 2000, 2004). (See below the discussion of the neutral theory of evolution for a precise quantitative assessment.)

Of course, neo-Darwinists do not envision a completely random search through the set of all possible nucleotide sequences--so-called "sequence space." They envision natural selection acting to preserve small advantageous variations in genetic sequences and their corresponding protein products. Dawkins (1996), for example, likens an organism to a high mountain peak. He compares climbing the sheer precipice up the front side of the mountain to building a new organism by chance. He acknowledges that his approach up "Mount Improbable" will not succeed. Nevertheless, he suggests that there is a gradual slope up the backside of the mountain that could be climbed in small incremental steps. In his analogy, the backside climb up "Mount Improbable" corresponds to the process of natural selection acting on random changes in the genetic text. What chance alone cannot accomplish blindly or in one leap, selection (acting on mutations) can accomplish through the cumulative effect of many slight successive steps.

Yet the extreme specificity and complexity of proteins presents a difficulty, not only for the chance origin of specified biological information (i.e., for random mutations acting alone), but also for selection and mutation acting in concert. Indeed, mutagenesis experiments cast doubt on each of the two scenarios by which neo-Darwinists envisioned new information arising from the mutation/selection mechanism (for review, see Lonnig 2001). For neo-Darwinism, new functional genes either arise from non-coding sections in the genome or from preexisting genes. Both scenarios are problematic.

In the first scenario, neo-Darwinists envision new genetic information arising from those sections of the genetic text that can presumably vary freely without consequence to the organism. According to this scenario, non-coding sections of the genome, or duplicated sections of coding regions, can experience a protracted period of "neutral evolution" (Kimura 1983) during which alterations in nucleotide sequences have no discernible effect on the function of the organism. Eventually, however, a new gene sequence will arise that can code for a novel protein. At that point, natural selection can favor the new gene and its functional protein product, thus securing the preservation and heritability of both.

This scenario has the advantage of allowing the genome to vary through many generations, as mutations "search" the space of possible base sequences. The scenario has an overriding problem, however: the size of the combinatorial space (i.e., the number of possible amino acid sequences) and the extreme rarity and isolation of the functional sequences within that space of possibilities. Since natural selection can do nothing to help generate new functional sequences, but rather can only preserve such sequences once they have arisen, chance alone--random variation--must do the work of information generation--that is, of finding the exceedingly rare functional sequences within the set of combinatorial possibilities. Yet the probability of randomly assembling (or "finding," in the previous sense) a functional sequence is extremely small.

Cassette mutagenesis experiments performed during the early 1990s suggest that the probability of attaining (at random) the correct sequencing for a short protein 100 amino acids long is about 1 in 1065 (Reidhaar-Olson & Sauer 1990, Behe 1992:65-69). This result agreed closely with earlier calculations that Yockey (1978) had performed based upon the known sequence variability of cytochrome c in different species and other theoretical considerations. More recent mutagenesis research has provided additional support for the conclusion that functional proteins are exceedingly rare among possible amino acid sequences (Axe 2000, 2004). Axe (2004) has performed site directed mutagenesis experiments on a 150-residue protein-folding domain within a B-lactamase enzyme. His experimental method improves upon earlier mutagenesis techniques and corrects for several sources of possible estimation error inherent in them. On the basis of these experiments, Axe has estimated the ratio of (a) proteins of typical size (150 residues) that perform a specified function via any folded structure to (b) the whole set of possible amino acids sequences of that size. Based on his experiments, Axe has estimated his ratio to be 1 to 1077. Thus, the probability of finding a functional protein among the possible amino acid sequences corresponding to a 150-residue protein is similarly 1 in 1077.

Other considerations imply additional improbabilities. First, new Cambrian animals would require proteins much longer than 100 residues to perform many necessary specialized functions. Ohno (1996) has noted that Cambrian animals would have required complex proteins such as lysyl oxidase in order to support their stout body structures. Lysyl oxidase molecules in extant organisms comprise over 400 amino acids. These molecules are both highly complex (non-repetitive) and functionally specified. Reasonable extrapolation from mutagenesis experiments done on shorter protein molecules suggests that the probability of producing functionally sequenced proteins of this length at random is so small as to make appeals to chance absurd, even granting the duration of the entire universe. (See Dembski 1998:175-223 for a rigorous calculation of this "Universal Probability Bound"; See also Axe 2004.) Yet, second, fossil data (Bowring et al. 1993, 1998a:1, 1998b:40; Kerr 1993; Monatersky 1993), and even molecular analyses supporting deep divergence (Wray et al. 1996), suggest that the duration of the Cambrian explosion (between 5-10 x 106 and, at most, 7 x 107 years) is far smaller than that of the entire universe (1.3-2 x 1010 years). Third, DNA mutation rates are far too low to generate the novel genes and proteins necessary to building the Cambrian animals, given the most probable duration of the explosion as determined by fossil studies (Conway Morris 1998b). As Ohno (1996:8475) notes, even a mutation rate of 10-9 per base pair per year results in only a 1% change in the sequence of a given section of DNA in 10 million years. Thus, he argues that mutational divergence of preexisting genes cannot explain the origin of the Cambrian forms in that time.4

The selection/mutation mechanism faces another probabilistic obstacle. The animals that arise in the Cambrian exhibit structures that would have required many new types of cells, each of which would have required many novel proteins to perform their specialized functions. Further, new cell types require Asystems of proteins that must, as a condition of functioning, act in close coordination with one another. The unit of selection in such systems ascends to the system as a whole. Natural selection selects for functional advantage. But new cell types require whole systems of proteins to perform their distinctive functions. In such cases, natural selection cannot contribute to the process of information generation until after the information necessary to build the requisite system of proteins has arisen. Thus random variations must, again, do the work of information generation--and now not simply for one protein, but for many proteins arising at nearly the same time. Yet the odds of this occurring by chance alone are, of course, far smaller than the odds of the chance origin of a single gene or protein--so small in fact as to render the chance origin of the genetic information necessary to build a new cell type (a necessary but not sufficient condition of building a new body plan) problematic given even the most optimistic estimates for the duration of the Cambrian explosion.

Dawkins (1986:139) has noted that scientific theories can rely on only so much "luck" before they cease to be credible. The neutral theory of evolution, which, by its own logic, prevents natural selection from playing a role in generating genetic information until after the fact, relies on entirely too much luck. The sensitivity of proteins to functional loss, the need for long proteins to build new cell types and animals, the need for whole new systems of proteins to service new cell types, the probable brevity of the Cambrian explosion relative to mutation rates--all suggest the immense improbability (and implausibility) of any scenario for the origination of Cambrian genetic information that relies upon random variation alone unassisted by natural selection.

Yet the neutral theory requires novel genes and proteins to arise--essentially--by random mutation alone. Adaptive advantage accrues after the generation of new functional genes and proteins. Thus, natural selection cannot play a role until new information-bearing molecules have independently arisen. Thus neutral theorists envisioned the need to scale the steep face of a Dawkins-style precipice of which there is no gradually sloping backside--a situation that, by Dawkins' own logic, is probabilistically untenable.

In the second scenario, neo-Darwinists envisioned novel genes and proteins arising by numerous successive mutations in the preexisting genetic text that codes for proteins. To adapt Dawkins's metaphor, this scenario envisions gradually climbing down one functional peak and then ascending another. Yet mutagenesis experiments again suggest a difficulty. Recent experiments show that, even when exploring a region of sequence space populated by proteins of a single fold and function, most multiple-position changes quickly lead to loss of function (Axe 2000). Yet to turn one protein into another with a completely novel structure and function requires specified changes at many sites. Indeed, the number of changes necessary to produce a new protein greatly exceeds the number of changes that will typically produce functional losses. Given this, the probability of escaping total functional loss during a random search for the changes needed to produce a new function is extremely small--and this probability diminishes exponentially with each additional requisite change (Axe 2000). Thus, Axe's results imply that, in all probability, random searches for novel proteins (through sequence space) will result in functional loss long before any novel functional protein will emerge.

Blanco et al. have come to a similar conclusion. Using directed mutagenesis, they have determined that residues in both the hydrophobic core and on the surface of the protein play essential roles in determining protein structure. By sampling intermediate sequences between two naturally occurring sequences that adopt different folds, they found that the intermediate sequences "lack a well defined three-dimensional structure." Thus, they conclude that it is unlikely that a new protein fold via a series of folded intermediates sequences (Blanco et al. 1999:741).

Thus, although this second neo-Darwinian scenario has the advantage of starting with functional genes and proteins, it also has a lethal disadvantage: any process of random mutation or rearrangement in the genome would in all probability generate nonfunctional intermediate sequences before fundamentally new functional genes or proteins would arise. Clearly, nonfunctional intermediate sequences confer no survival advantage on their host organisms. Natural selection favors only functional advantage. It cannot select or favor nucleotide sequences or polypeptide chains that do not yet perform biological functions, and still less will it favor sequences that efface or destroy preexisting function.

Evolving genes and proteins will range through a series of nonfunctional intermediate sequences that natural selection will not favor or preserve but will, in all probability, eliminate (Blanco et al. 1999, Axe 2000). When this happens, selection-driven evolution will cease. At this point, neutral evolution of the genome (unhinged from selective pressure) may ensue, but, as we have seen, such a process must overcome immense probabilistic hurdles, even granting cosmic time.

Thus, whether one envisions the evolutionary process beginning with a noncoding region of the genome or a preexisting functional gene, the functional specificity and complexity of proteins impose very stringent limitations on the efficacy of mutation and selection. In the first case, function must arise first, before natural selection can act to favor a novel variation. In the second case, function must be continuously maintained in order to prevent deleterious (or lethal) consequences to the organism and to allow further evolution. Yet the complexity and functional specificity of proteins implies that both these conditions will be extremely difficult to meet. Therefore, the neo-Darwinian mechanism appears to be inadequate to generate the new information present in the novel genes and proteins that arise with the Cambrian animals.

Novel Body Plans

The problems with the neo-Darwinian mechanism run deeper still. In order to explain the origin of the Cambrian animals, one must account not only for new proteins and cell types, but also for the origin of new body plans. Within the past decade, developmental biology has dramatically advanced our understanding of how body plans are built during ontogeny. In the process, it has also uncovered a profound difficulty for neo-Darwinism.

Significant morphological change in organisms requires attention to timing. Mutations in genes that are expressed late in the development of an organism will not affect the body plan. Mutations expressed early in development, however, could conceivably produce significant morphological change (Arthur 1997:21). Thus, events expressed early in the development of organisms have the only realistic chance of producing large-scale macroevolutionary change (Thomson 1992). As John and Miklos (1988:309) explain, macroevolutionary change requires alterations in the very early stages of ontogenesis.

Yet recent studies in developmental biology make clear that mutations expressed early in development typically have deleterious effects (Arthur 1997:21). For example, when early-acting body plan molecules, or morphogens such as bicoid (which helps to set up the anterior-posterior head-to-tail axis in Drosophila), are perturbed, development shuts down (Nusslein-Volhard & Wieschaus 1980, Lawrence & Struhl 1996, Muller & Newman 2003).5 The resulting embryos die. Moreover, there is a good reason for this. If an engineer modifies the length of the piston rods in an internal combustion engine without modifying the crankshaft accordingly, the engine won't start. Similarly, processes of development are tightly integrated spatially and temporally such that changes early in development will require a host of other coordinated changes in separate but functionally interrelated developmental processes downstream. For this reason, mutations will be much more likely to be deadly if they disrupt a functionally deeply-embedded structure such as a spinal column than if they affect more isolated anatomical features such as fingers (Kauffman 1995:200).

This problem has led to what McDonald (1983) has called "a great Darwinian paradox" (p. 93). McDonald notes that genes that are observed to vary within natural populations do not lead to major adaptive changes, while genes that could cause major changes--the very stuff of macroevolution--apparently do not vary. In other words, mutations of the kind that macroevolution doesn't need (namely, viable genetic mutations in DNA expressed late in development) do occur, but those that it does need (namely, beneficial body plan mutations expressed early in development) apparently don't occur.6 According to Darwin (1859:108) natural selection cannot act until favorable variations arise in a population. Yet there is no evidence from developmental genetics that the kind of variations required by neo-Darwinism--namely, favorable body plan mutations--ever occur.

Developmental biology has raised another formidable problem for the mutation/selection mechanism. Embryological evidence has long shown that DNA does not wholly determine morphological form (Goodwin 1985, Nijhout 1990, Sapp 1987, Muller & Newman 2003), suggesting that mutations in DNA alone cannot account for the morphological changes required to build a new body plan.

DNA helps direct protein synthesis.7 It also helps to regulate the timing and expression of the synthesis of various proteins within cells. Yet, DNA alone does not determine how individual proteins assemble themselves into larger systems of proteins; still less does it solely determine how cell types, tissue types, and organs arrange themselves into body plans (Harold 1995:2774, Moss 2004). Instead, other factors--such as the three-dimensional structure and organization of the cell membrane and cytoskeleton and the spatial architecture of the fertilized egg--play important roles in determining body plan formation during embryogenesis.

For example, the structure and location of the cytoskeleton influence the patterning of embryos. Arrays of microtubules help to distribute the essential proteins used during development to their correct locations in the cell. Of course, microtubules themselves are made of many protein subunits. Nevertheless, like bricks that can be used to assemble many different structures, the tubulin subunits in the cell's microtubules are identical to one another. Thus, neither the tubulin subunits nor the genes that produce them account for the different shape of microtubule arrays that distinguish different kinds of embryos and developmental pathways. Instead, the structure of the microtubule array itself is determined by the location and arrangement of its subunits, not the properties of the subunits themselves. For this reason, it is not possible to predict the structure of the cytoskeleton of the cell from the characteristics of the protein constituents that form that structure (Harold 2001:125).

Two analogies may help further clarify the point. At a building site, builders will make use of many materials: lumber, wires, nails, drywall, piping, and windows. Yet building materials do not determine the floor plan of the house, or the arrangement of houses in a neighborhood. Similarly, electronic circuits are composed of many components, such as resistors, capacitors, and transistors. But such lower-level components do not determine their own arrangement in an integrated circuit. Biological symptoms also depend on hierarchical arrangements of parts. Genes and proteins are made from simple building blocks--nucleotide bases and amino acids--arranged in specific ways. Cell types are made of, among other things, systems of specialized proteins. Organs are made of specialized arrangements of cell types and tissues. And body plans comprise specific arrangements of specialized organs. Yet, clearly, the properties of individual proteins (or, indeed, the lower-level parts in the hierarchy generally) do not fully determine the organization of the higher-level structures and organizational patterns (Harold 2001:125). It follows that the genetic information that codes for proteins does not determine these higher-level structures either.

These considerations pose another challenge to the sufficiency of the neo-Darwinian mechanism. Neo-Darwinism seeks to explain the origin of new information, form, and structure as a result of selection acting on randomly arising variation at a very low level within the biological hierarchy, namely, within the genetic text. Yet major morphological innovations depend on a specificity of arrangement at a much higher level of the organizational hierarchy, a level that DNA alone does not determine. Yet if DNA is not wholly responsible for body plan morphogenesis, then DNA sequences can mutate indefinitely, without regard to realistic probabilistic limits, and still not produce a new body plan. Thus, the mechanism of natural selection acting on random mutations in DNA cannot in principle generate novel body plans, including those that first arose in the Cambrian explosion.

Of course, it could be argued that, while many single proteins do not by themselves determine cellular structures and/or body plans, proteins acting in concert with other proteins or suites of proteins could determine such higher-level form. For example, it might be pointed out that the tubulin subunits (cited above) are assembled by other helper proteins--gene products--called Microtubule Associated Proteins (MAPS). This might seem to suggest that genes and gene products alone do suffice to determine the development of the three-dimensional structure of the cytoskeleton.

Yet MAPS, and indeed many other necessary proteins, are only part of the story. The location of specified target sites on the interior of the cell membrane also helps to determine the shape of the cytoskeleton. Similarly, so does the position and structure of the centrosome which nucleates the microtubules that form the cytoskeleton. While both the membrane targets and the centrosomes are made of proteins, the location and form of these structures is not wholly determined by the proteins that form them. Indeed, centrosome structure and membrane patterns as a whole convey three-dimensional structural information that helps determine the structure of the cytoskeleton and the location of its subunits (McNiven & Porter 1992:313-329). Moreover, the centrioles that compose the centrosomes replicate independently of DNA replication (Lange et al. 2000:235-249, Marshall & Rosenbaum 2000:187-205). The daughter centriole receives its form from the overall structure of the mother centriole, not from the individual gene products that constitute it (Lange et al. 2000). In ciliates, microsurgery on cell membranes can produce heritable changes in membrane patterns, even though the DNA of the ciliates has not been altered (Sonneborn 1970:1-13, Frankel 1980:607-623; Nanney 1983:163-170). This suggests that membrane patterns (as opposed to membrane constituents) are impressed directly on daughter cells. In both cases, form is transmitted from parent three-dimensional structures to daughter three-dimensional structures directly and is not wholly contained in constituent proteins or genetic information (Moss 2004).

Thus, in each new generation, the form and structure of the cell arises as the result of both gene products and preexisting three-dimensional structure and organization. Cellular structures are built from proteins, but proteins find their way to correct locations in part because of preexisting three-dimensional patterns and organization inherent in cellular structures. Preexisting three-dimensional form present in the preceding generation (whether inherent in the cell membrane, the centrosomes, the cytoskeleton or other features of the fertilized egg) contributes to the production of form in the next generation. Neither structural proteins alone, nor the genes that code for them, are sufficient to determine the three-dimensional shape and structure of the entities they form. Gene products provide necessary, but not sufficient conditions, for the development of three-dimensional structure within cells, organs and body plans (Harold 1995:2767). But if this is so, then natural selection acting on genetic variation alone cannot produce the new forms that arise in history of life.

Self-Organizational Models

Of course, neo-Darwinism is not the only evolutionary theory for explaining the origin of novel biological form. Kauffman (1995) doubts the efficacy of the mutation/selection mechanism. Nevertheless, he has advanced a self-organizational theory to account for the emergence of new form, and presumably the information necessary to generate it. Whereas neo-Darwinism attempts to explain new form as the consequence of selection acting on random mutation, Kauffman suggests that selection acts, not mainly on random variations, but on emergent patterns of order that self-organize via the laws of nature.

Kauffman (1995:47-92) illustrates how this might work with various model systems in a computer environment. In one, he conceives a system of buttons connected by strings. Buttons represent novel genes or gene products; strings represent the law-like forces of interaction that obtain between gene products-i.e., proteins. Kauffman suggests that when the complexity of the system (as represented by the number of buttons and strings) reaches a critical threshold, new modes of organization can arise in the system "for free"--that is, naturally and spontaneously--after the manner of a phase transition in chemistry.

Another model that Kauffman develops is a system of interconnected lights. Each light can flash in a variety of states--on, off, twinkling, etc. Since there is more than one possible state for each light, and many lights, there are a vast number of possible states that the system can adopt. Further, in his system, rules determine how past states will influence future states. Kauffman asserts that, as a result of these rules, the system will, if properly tuned, eventually produce a kind of order in which a few basic patterns of light activity recur with greater-than-random frequency. Since these actual patterns of light activity represent a small portion of the total number of possible states in which the system can reside, Kauffman seems to imply that self-organizational laws might similarly result in highly improbable biological outcomes--perhaps even sequences (of bases or amino acids) within a much larger sequence space of possibilities.

Do these simulations of self-organizational processes accurately model the origin of novel genetic information? It is hard to think so.

First, in both examples, Kauffman presupposes but does not explain significant sources of preexisting information. In his buttons-and-strings system, the buttons represent proteins, themselves packets of CSI, and the result of preexisting genetic information. Where does this information come from? Kauffman (1995) doesn't say, but the origin of such information is an essential part of what needs to be explained in the history of life. Similarly, in his light system, the order that allegedly arises for "for free" actually arises only if the programmer of the model system "tunes" it in such a way as to keep it from either (a) generating an excessively rigid order or (b) developing into chaos (pp. 86-88). Yet this necessary tuning involves an intelligent programmer selecting certain parameters and excluding others--that is, inputting information.

Second, Kauffman's model systems are not constrained by functional considerations and thus are not analogous to biological systems. A system of interconnected lights governed by pre-programmed rules may well settle into a small number of patterns within a much larger space of possibilities. But because these patterns have no function, and need not meet any functional requirements, they have no specificity analogous to that present in actual organisms. Instead, examination of Kauffman's (1995) model systems shows that they do not produce sequences or systems characterized by specified complexity, but instead by large amounts of symmetrical order or internal redundancy interspersed with aperiodicity or (mere) complexity (pp. 53, 89, 102). Getting a law-governed system to generate repetitive patterns of flashing lights, even with a certain amount of variation, is clearly interesting, but not biologically relevant. On the other hand, a system of lights flashing the title of a Broadway play would model a biologically relevant self-organizational process, at least if such a meaningful or functionally specified sequence arose without intelligent agents previously programming the system with equivalent amounts of CSI. In any case, Kauffman's systems do not produce specified complexity, and thus do not offer promising models for explaining the new genes and proteins that arose in the Cambrian.

Even so, Kauffman suggests that his self-organizational models can specifically elucidate aspects of the Cambrian explosion. According to Kauffman (1995:199-201), new Cambrian animals emerged as the result of "long jump" mutations that established new body plans in a discrete rather than gradual fashion. He also recognizes that mutations affecting early development are almost inevitably harmful. Thus, he concludes that body plans, once established, will not change, and that any subsequent evolution must occur within an established body plan (Kauffman 1995:201). And indeed, the fossil record does show a curious (from a neo-Darwinian point of view) top-down pattern of appearance, in which higher taxa (and the body plans they represent) appear first, only later to be followed by the multiplication of lower taxa representing variations within those original body designs (Erwin et al. 1987, Lewin 1988, Valentine & Jablonski 2003:518). Further, as Kauffman expects, body plans appear suddenly and persist without significant modification over time.

But here, again, Kauffman begs the most important question, which is: what produces the new Cambrian body plans in the first place? Granted, he invokes "long jump mutations" to explain this, but he identifies no specific self-organizational process that can produce such mutations. Moreover, he concedes a principle that undermines the plausibility of his own proposal. Kauffman acknowledges that mutations that occur early in development are almost inevitably deleterious. Yet developmental biologists know that these are the only kind of mutations that have a realistic chance of producing large-scale evolutionary change--i.e., the big jumps that Kauffman invokes. Though Kauffman repudiates the neo-Darwinian reliance upon random mutations in favor of self-organizing order, in the end, he must invoke the most implausible kind of random mutation in order to provide a self-organizational account of the new Cambrian body plans. Clearly, his model is not sufficient.

Punctuated Equilibrium

Of course, still other causal explanations have been proposed. During the 1970s, the paleontologists Eldredge and Gould (1972) proposed the theory of evolution by punctuated equilibrium in order to account for a pervasive pattern of "sudden appearance" and "stasis" in the fossil record. Though advocates of punctuated equilibrium were mainly seeking to describe the fossil record more accurately than earlier gradualist neo-Darwinian models had done, they did also propose a mechanism--known as species selection--by which the large morphological jumps evident in fossil record might have been produced. According to punctuationalists, natural selection functions more as a mechanism for selecting the fittest species rather than the most-fit individual among a species. Accordingly, on this model, morphological change should occur in larger, more discrete intervals than it would given a traditional neo-Darwinian understanding.

Despite its virtues as a descriptive model of the history of life, punctuated equilibrium has been widely criticized for failing to provide a mechanism sufficient to produce the novel form characteristic of higher taxonomic groups. For one thing, critics have noted that the proposed mechanism of punctuated evolutionary change simply lacked the raw material upon which to work. As Valentine and Erwin (1987) note, the fossil record fails to document a large pool of species prior to the Cambrian. Yet the proposed mechanism of species selection requires just such a pool of species upon which to act. Thus, they conclude that the mechanism of species selection probably does not resolve the problem of the origin of the higher taxonomic groups (p. 96).8 Further, punctuated equilibrium has not addressed the more specific and fundamental problem of explaining the origin of the new biological information (whether genetic or epigenetic) necessary to produce novel biological form. Advocates of punctuated equilibrium might assume that the new species (upon which natural selection acts) arise by known microevolutionary processes of speciation (such as founder effect, genetic drift or bottleneck effect) that do not necessarily depend upon mutations to produce adaptive changes. But, in that case, the theory lacks an account of how the specifically higher taxa arise. Species selection will only produce more fit species. On the other hand, if punctuationalists assume that processes of genetic mutation can produce more fundamental morphological changes and variations, then their model becomes subject to the same problems as neo-Darwinism (see above). This dilemma is evident in Gould (2002:710) insofar as his attempts to explain adaptive complexity inevitably employ classical neo-Darwinian modes of explanation.9

Structuralism

Another attempt to explain the origin of form has been proposed by the structuralists such as Gerry Webster and Brian Goodwin (1984, 1996). These biologists, drawing on the earlier work of D'Arcy Thompson (1942), view biological form as the result of structural constraints imposed upon matter by morphogenetic rules or laws. For reasons similar to those discussed above, the structuralists have insisted that these generative or morphogenetic rules do not reside in the lower level building materials of organisms, whether in genes or proteins. Webster and Goodwin (1984:510-511) further envisioned morphogenetic rules or laws operating ahistorically, similar to the way in which gravitational or electromagnetic laws operate. For this reason, structuralists see phylogeny as of secondary importance in understanding the origin of the higher taxa, though they think that transformations of form can occur. For structuralists, constraints on the arrangement of matter arise not mainly as the result of historical contingencies--such as environmental changes or genetic mutations--but instead because of the continuous ahistorical operation of fundamental laws of form--laws that organize or inform matter.

While this approach avoids many of the difficulties currently afflicting neo-Darwinism (in particular those associated with its "genocentricity"), critics (such as Maynard Smith 1986) of structuralism have argued that the structuralist explanation of form lacks specificity. They note that structuralists have been unable to say just where laws of form reside--whether in the universe, or in every possible world, or in organisms as a whole, or in just some part of organisms. Further, according to structuralists, morphogenetic laws are mathematical in character. Yet, structuralists have yet to specify the mathematical formulae that determine biological forms.

Others (Yockey 1992; Polanyi 1967, 1968; Meyer 2003) have questioned whether physical laws could in principle generate the kind of complexity that characterizes biological systems. Structuralists envision the existence of biological laws that produce form in much the same way that physical laws produce form. Yet the forms that physicists regard as manifestations of underlying laws are characterized by large amounts of symmetric or redundant order, by relatively simple patterns such as vortices or gravitational fields or magnetic lines of force. Indeed, physical laws are typically expressed as differential equations (or algorithms) that almost by definition describe recurring phenomena--patterns of compressible "order" not "complexity" as defined by algorithmic information theory (Yockey 1992:77-83). Biological forms, by contrast, manifest greater complexity and derive in ontogeny from highly complex initial conditions--i.e., non-redundant sequences of nucleotide bases in the genome and other forms of information expressed in the complex and irregular three-dimensional topography of the organism or the fertilized egg. Thus, the kind of form that physical laws produce is not analogous to biological form--at least not when compared from the standpoint of (algorithmic) complexity. Further, physical laws lack the information content to specify biology systems. As Polyanyi (1967, 1968) and Yockey (1992:290) have shown, the laws of physics and chemistry allow, but do not determine, distinctively biological modes of organization. In other words, living systems are consistent with, but not deducible, from physical-chemical laws (1992:290).

Of course, biological systems do manifest some reoccurring patterns, processes and behaviors. The same type of organism develops repeatedly from similar ontogenetic processes in the same species. Similar processes of cell division reoccur in many organisms. Thus, one might describe certain biological processes as law-governed. Even so, the existence of such biological regularities does not solve the problem of the origin of form and information, since the recurring processes described by such biological laws (if there be such laws) only occur as the result of preexisting stores of (genetic and/or epigenetic) information and these information-rich initial conditions impose the constraints that produce the recurring behavior in biological systems. (For example, processes of cell division recur with great frequency in organisms, but depend upon information-rich DNA and proteins molecules.) In other words, distinctively biological regularities depend upon preexisting biological information. Thus, appeals to higher-level biological laws presuppose, but do not explain, the origination of the information necessary to morphogenesis.

Thus, structuralism faces a difficult in principle dilemma. On the one hand, physical laws produce very simple redundant patterns that lack the complexity characteristic of biological systems. On the other hand, distinctively biological laws--if there are such laws--depend upon preexisting information-rich structures. In either case, laws are not good candidates for explaining the origination of biological form or the information necessary to produce it.

Cladism: An Artifact of Classification?

Some cladists have advanced another approach to the problem of the origin of form, specifically as it arises in the Cambrian. They have argued that the problem of the origin of the phyla is an artifact of the classification system, and therefore, does not require explanation. Budd and Jensen (2000), for example, argue that the problem of the Cambrian explosion resolves itself if one keeps in mind the cladistic distinction between "stem" and "crown" groups. Since crown groups arise whenever new characters are added to simpler more ancestral stem groups during the evolutionary process, new phyla will inevitably arise once a new stem group has arisen. Thus, for Budd and Jensen what requires explanation is not the crown groups corresponding to the new Cambrian phyla, but the earlier more primitive stem groups that presumably arose deep in the Proterozoic. Yet since these earlier stem groups are by definition less derived, explaining them will be considerably easier than explaining the origin of the Cambrian animals de novo. In any case, for Budd and Jensen the explosion of new phyla in the Cambrian does not require explanation. As they put it, "given that the early branching points of major clades is an inevitable result of clade diversification, the alleged phenomenon of the phyla appearing early and remaining morphologically static is not seen to require particular explanation" (Budd & Jensen 2000:253).

While superficially plausible, perhaps, Budd and Jensen's attempt to explain away the Cambrian explosion begs crucial questions. Granted, as new characters are added to existing forms, novels morphology and greater morphological disparity will likely result. But what causes new characters to arise? And how does the information necessary to produce new characters originate? Budd and Jensen do not specify. Nor can they say how derived the ancestral forms are likely to have been, and what processes, might have been sufficient to produce them. Instead, they simply assume the sufficiency of known neo-Darwinian mechanisms (Budd & Jensen 2000:288). Yet, as shown above, this assumption is now problematic. In any case, Budd and Jensen do not explain what causes the origination of biological form and information.

Convergence and Teleological Evolution

More recently, Conway Morris (2000, 2003c) has suggested another possible explanation based on the tendency for evolution to converge on the same structural forms during the history of life. Conway Morris cites numerous examples of organisms that possess very similar forms and structures, even though such structures are often built from different material substrates and arise (in ontogeny) by the expression of very different genes. Given the extreme improbability of the same structures arising by random mutation and selection in disparate phylogenies, Conway Morris argues that the pervasiveness of convergent structures suggests that evolution may be in some way "channeled" toward similar functional and/or structural endpoints. Such an end-directed understanding of evolution, he admits, raises the controversial prospect of a teleological or purposive element in the history of life. For this reason, he argues that the phenomenon of convergence has received less attention than it might have otherwise. Nevertheless, he argues that just as physicists have reopened the question of design in their discussions of anthropic fine-tuning, the ubiquity of convergent structures in the history of life has led some biologists (Denton 1998) to consider extending teleological thinking to biology. And, indeed, Conway Morris himself intimates that the evolutionary process might be "underpinned by a purpose" (2000:8, 2003b:511).

Conway Morris, of course, considers this possibility in relation to a very specific aspect of the problem of organismal form, namely, the problem of explaining why the same forms arise repeatedly in so many disparate lines of decent. But this raises a question. Could a similar approach shed explanatory light on the more general causal question that has been addressed in this review? Could the notion of purposive design help provide a more adequate explanation for the origin of organismal form generally? Are there reasons to consider design as an explanation for the origin of the biological information necessary to produce the higher taxa and their corresponding morphological novelty?

The remainder of this review will suggest that there are such reasons. In so doing, it may also help explain why the issue of teleology or design has reemerged within the scientific discussion of biological origins (Denton 1986, 1998; Thaxton et al. 1992; Kenyon & Mills 1996: Behe 1996, 2004; Dembski 1998, 2002, 2004; Conway Morris 2000, 2003a, 2003b, Lonnig 2001; Lonnig & Saedler 2002; Nelson & Wells 2003; Meyer 2003, 2004; Bradley 2004) and why some scientists and philosophers of science have considered teleological explanations for the origin of form and information despite strong methodological prohibitions against design as a scientific hypothesis (Gillespie 1979, Lenior 1982:4).

First, the possibility of design as an explanation follows logically from a consideration of the deficiencies of neo-Darwinism and other current theories as explanations for some of the more striking "appearances of design" in biological systems. Neo-Darwinists such as Ayala (1994:5), Dawkins (1986:1), Mayr (1982:xi-xii) and Lewontin (1978) have long acknowledged that organisms appear to have been designed. Of course, neo-Darwinists assert that what Ayala (1994:5) calls the "obvious design" of living things is only apparent since the selection/mutation mechanism can explain the origin of complex form and organization in living systems without an appeal to a designing agent. Indeed, neo-Darwinists affirm that mutation and selection--and perhaps other similarly undirected mechanisms--are fully sufficient to explain the appearance of design in biology. Self-organizational theorists and punctuationalists modify this claim, but affirm its essential tenet. Self-organization theorists argue that natural selection acting on self organizing order can explain the complexity of living things--again, without any appeal to design. Punctuationalists similarly envision natural selection acting on newly arising species with no actual design involved.

And clearly, the neo-Darwinian mechanism does explain many appearances of design, such as the adaptation of organisms to specialized environments that attracted the interest of 19th century biologists. More specifically, known microevolutionary processes appear quite sufficient to account for changes in the size of Galapagos finch beaks that have occurred in response to variations in annual rainfall and available food supplies (Weiner 1994, Grant 1999).

But does neo-Darwinism, or any other fully materialistic model, explain all appearances of design in biology, including the body plans and information that characterize living systems? Arguably, biological forms--such as the structure of a chambered nautilus, the organization of a trilobite, the functional integration of parts in an eye or molecular machine--attract our attention in part because the organized complexity of such systems seems reminiscent of our own designs. Yet, this review has argued that neo-Darwinism does not adequately account for the origin of all appearances of design, especially if one considers animal body plans, and the information necessary to construct them, as especially striking examples of the appearance of design in living systems. Indeed, Dawkins (1995:11) and Gates (1996:228) have noted that genetic information bears an uncanny resemblance to computer software or machine code. For this reason, the presence of CSI in living organisms, and the discontinuous increases of CSI that occurred during events such as the Cambrian explosion, appears at least suggestive of design.

Does neo-Darwinism or any other purely materialistic model of morphogenesis account for the origin of the genetic and other forms of CSI necessary to produce novel organismal form? If not, as this review has argued, could the emergence of novel information-rich genes, proteins, cell types and body plans have resulted from actual design, rather than a purposeless process that merely mimics the powers of a designing intelligence? The logic of neo-Darwinism, with its specific claim to have accounted for the appearance of design, would itself seem to open the door to this possibility. Indeed, the historical formulation of Darwinism in dialectical opposition to the design hypothesis (Gillespie 1979), coupled with the neo-Darwinism's inability to account for many salient appearances of design including the emergence of form and information, would seem logically to reopen the possibility of actual (as opposed to apparent) design in the history of life.

A second reason for considering design as an explanation for these phenomena follows from the importance of explanatory power to scientific theory evaluation and from a consideration of the potential explanatory power of the design hypothesis. Studies in the methodology and philosophy of science have shown that many scientific theories, particularly in the historical sciences, are formulated and justified as inferences to the best explanation (Lipton 1991:32-88, Brush 1989:1124-1129, Sober 2000:44). Historical scientists, in particular, assess or test competing hypotheses by evaluating which hypothesis would, if true, provide the best explanation for some set of relevant data (Meyer 1991, 2002; Cleland 2001:987-989, 2002:474-496).10 Those with greater explanatory power are typically judged to be better, more probably true, theories. Darwin (1896:437) used this method of reasoning in defending his theory of universal common descent. Moreover, contemporary studies on the method of "inference to the best explanation" have shown that determining which among a set of competing possible explanations constitutes the best depends upon judgments about the causal adequacy, or "causal powers," of competing explanatory entities (Lipton 1991:32-88). In the historical sciences, uniformitarian and/or actualistic (Gould 1965, Simpson 1970, Rutten 1971, Hooykaas 1975) canons of method suggest that judgments about causal adequacy should derive from our present knowledge of cause and effect relationships. For historical scientists, "the present is the key to the past" means that present experience-based knowledge of cause and effect relationships typically guides the assessment of the plausibility of proposed causes of past events.

Yet it is precisely for this reason that current advocates of the design hypothesis want to reconsider design as an explanation for the origin of biological form and information. This review, and much of the literature it has surveyed, suggests that four of the most prominent models for explaining the origin of biological form fail to provide adequate causal explanations for the discontinuous increases of CSI that are required to produce novel morphologies. Yet, we have repeated experience of rational and conscious agents--in particular ourselves--generating or causing increases in complex specified information, both in the form of sequence-specific lines of code and in the form of hierarchically arranged systems of parts.

In the first place, intelligent human agents--in virtue of their rationality and consciousness--have demonstrated the power to produce information in the form of linear sequence-specific arrangements of characters. Indeed, experience affirms that information of this type routinely arises from the activity of intelligent agents. A computer user who traces the information on a screen back to its source invariably comes to a mind--that of a software engineer or programmer. The information in a book or inscriptions ultimately derives from a writer or scribe--from a mental, rather than a strictly material, cause. Our experience-based knowledge of information-flow confirms that systems with large amounts of specified complexity (especially codes and languages) invariably originate from an intelligent source from a mind or personal agent. As Quastler (1964) put it, the "creation of new information is habitually associated with conscious activity" (p. 16). Experience teaches this obvious truth.

Further, the highly specified hierarchical arrangements of parts in animal body plans also suggest design, again because of our experience of the kinds of features and systems that designers can and do produce. At every level of the biological hierarchy, organisms require specified and highly improbable arrangements of lower-level constituents in order to maintain their form and function. Genes require specified arrangements of nucleotide bases; proteins require specified arrangements of amino acids; new cell types require specified arrangements of systems of proteins; body plans require specialized arrangements of cell types and organs. Organisms not only contain information-rich components (such as proteins and genes), but they comprise information-rich arrangements of those components and the systems that comprise them. Yet we know, based on our present experience of cause and effect relationships, that design engineers--possessing purposive intelligence and rationality--have the ability to produce information-rich hierarchies in which both individual modules and the arrangements of those modules exhibit complexity and specificity--information so defined. Individual transistors, resistors, and capacitors exhibit considerable complexity and specificity of design; at a higher level of organization, their specific arrangement within an integrated circuit represents additional information and reflects further design. Conscious and rational agents have, as part of their powers of purposive intelligence, the capacity to design information-rich parts and to organize those parts into functional information-rich systems and hierarchies. Further, we know of no other causal entity or process that has this capacity. Clearly, we have good reason to doubt that mutation and selection, self-organizational processes or laws of nature, can produce the information-rich components, systems, and body plans necessary to explain the origination of morphological novelty such as that which arises in the Cambrian period.

There is a third reason to consider purpose or design as an explanation for the origin of biological form and information: purposive agents have just those necessary powers that natural selection lacks as a condition of its causal adequacy. At several points in the previous analysis, we saw that natural selection lacked the ability to generate novel information precisely because it can only act after new functional CSI has arisen. Natural selection can favor new proteins, and genes, but only after they perform some function. The job of generating new functional genes, proteins and systems of proteins therefore falls entirely to random mutations. Yet without functional criteria to guide a search through the space of possible sequences, random variation is probabilistically doomed. What is needed is not just a source of variation (i.e., the freedom to search a space of possibilities) or a mode of selection that can operate after the fact of a successful search, but instead a means of selection that (a) operates during a search--before success--and that (b) is guided by information about, or knowledge of, a functional target.

Demonstration of this requirement has come from an unlikely quarter: genetic algorithms. Genetic algorithms are programs that allegedly simulate the creative power of mutation and selection. Dawkins and Kuppers, for example, have developed computer programs that putatively simulate the production of genetic information by mutation and natural selection (Dawkins 1986:47-49, Kuppers 1987:355-369). Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion. As Berlinski (2000) has argued, genetic algorithms need something akin to a "forward looking memory" in order to succeed. Yet such foresighted selection has no analogue in nature. In biology, where differential survival depends upon maintaining function, selection cannot occur before new functional sequences arise. Natural selection lacks foresight.

What natural selection lacks, intelligent selection--purposive or goal-directed design--provides. Rational agents can arrange both matter and symbols with distant goals in mind. In using language, the human mind routinely "finds" or generates highly improbable linguistic sequences to convey an intended or preconceived idea. In the process of thought, functional objectives precede and constrain the selection of words, sounds and symbols to generate functional (and indeed meaningful) sequences from among a vast ensemble of meaningless alternative combinations of sound or symbol (Denton 1986:309-311). Similarly, the construction of complex technological objects and products, such as bridges, circuit boards, engines and software, result from the application of goal-directed constraints (Polanyi 1967, 1968). Indeed, in all functionally integrated complex systems where the cause is known by experience or observation, design engineers or other intelligent agents applied boundary constraints to limit possibilities in order to produce improbable forms, sequences or structures. Rational agents have repeatedly demonstrated the capacity to constrain the possible to actualize improbable but initially unrealized future functions. Repeated experience affirms that intelligent agents (minds) uniquely possess such causal powers.

Analysis of the problem of the origin of biological information, therefore, exposes a deficiency in the causal powers of natural selection that corresponds precisely to powers that agents are uniquely known to possess. Intelligent agents have foresight. Such agents can select functional goals before they exist. They can devise or select material means to accomplish those ends from among an array of possibilities and then actualize those goals in accord with a preconceived design plan or set of functional requirements. Rational agents can constrain combinatorial space with distant outcomes in mind. The causal powers that natural selection lacks--almost by definition--are associated with the attributes of consciousness and rationality--with purposive intelligence. Thus, by invoking design to explain the origin of new biological information, contemporary design theorists are not positing an arbitrary explanatory element unmotivated by a consideration of the evidence. Instead, they are positing an entity possessing precisely the attributes and causal powers that the phenomenon in question requires as a condition of its production and explanation.

Conclusion

An experience-based analysis of the causal powers of various explanatory hypotheses suggests purposive or intelligent design as a causally adequate--and perhaps the most causally adequate--explanation for the origin of the complex specified information required to build the Cambrian animals and the novel forms they represent. For this reason, recent scientific interest in the design hypothesis is unlikely to abate as biologists continue to wrestle with the problem of the origination of biological form and the higher taxa.


Literature Cited

Adams, M. D. Et alia. 2000. The genome sequence of Drosophila melanogaster.--Science 287:2185-2195.

Aris-Brosou, S., & Z. Yang. 2003. Bayesian models of episodic evolution support a late Precambrian explosive diversification of the Metazoa.--Molecular Biology and Evolution 20:1947-1954.

Arthur, W. 1997. The origin of animal body plans. Cambridge University Press, Cambridge, United Kingdom.

Axe, D. D. 2000. Extreme functional sensitivity to conservative amino acid changes on enzyme exteriors.--Journal of Molecular Biology 301(3):585-596.

______. 2004. Estimating the prevalence of protein sequences adopting functional enzyme folds.--Journal of Molecular Biology (in press).

Ayala, F. 1994. Darwin's revolution. Pp. 1-17 in J. Campbell and J. Schopf, eds., Creative evolution?! Jones and Bartlett Publishers, Boston, Massachusetts.

______. A. Rzhetsky, & F. J. Ayala. 1998. Origin of the metazoan phyla: molecular clocks confirm paleontological estimates--Proceedings of the National Academy of Sciences USA. 95:606-611.

Becker, H., & W. Lonnig, 2001. Transposons: eukaryotic. Pp. 529-539 in Nature encyclopedia of life sciences, vol. 18. Nature Publishing Group, London, United Kingdom.

Behe, M. 1992. Experimental support for regarding functional classes of proteins to be highly isolated from each other. Pp. 60-71 in J. Buell and V. Hearn, eds., Darwinism: science or philosophy? Foundation for Thought and Ethics, Richardson, Texas.

______. 1996. Darwin's black box. The Free Press, New York.

______. 2004. Irreducible complexity: obstacle to Darwinian evolution. Pp. 352-370 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Benton, M., & F. J. Ayala. 2003. Dating the tree of life--Science 300:1698-1700.

Berlinski, D. 2000. "On assessing genetic algorithms." Public lecture. Conference: Science and evidence of design in the universe. Yale University, November 4, 2000.

Blanco, F., I. Angrand, & L. Serrano. 1999. Exploring the confirmational properties of the sequence space between two proteins with different folds: an experimental study.--Journal of Molecular Biology 285:741-753.

Bowie, J., & R. Sauer. 1989. Identifying determinants of folding and activity for a protein of unknown sequences: tolerance to amino acid substitution.--Proceedings of the National Academy of Sciences, U.S.A. 86:2152-2156.

Bowring, S. A., J. P. Grotzinger, C. E. Isachsen, A. H. Knoll, S. M. Pelechaty, & P. Kolosov. 1993. Calibrating rates of early Cambrian evolution.--Science 261:1293-1298.

______. 1998a. A new look at evolutionary rates in deep time: Uniting paleontology and high-precision geochronology.--GSA Today 8:1-8.

______. 1998b. Geochronology comes of age.--Geotimes 43:36-40.

Bradley, W. 2004. Information, entropy and the origin of life. Pp. 331-351 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Brocks, J. J., G. A. Logan, R. Buick, & R. E. Summons. 1999. Archean molecular fossils and the early rise of eukaryotes.--Science 285:1033-1036.

Brush, S. G. 1989. Prediction and theory evaluation: the case of light bending.--Science 246:1124-1129.

Budd, G. E. & S. E. Jensen. 2000. A critical reappraisal of the fossil record of the bilaterial phyla.--Biological Reviews of the Cambridge Philosophical Society 75:253-295.

Carroll, R. L. 2000. Towards a new evolutionary synthesis.--Trends in Ecology and Evolution 15:27-32.

Cleland, C. 2001. Historical science, experimental science, and the scientific method.--Geology 29:987-990.

______. 2002. Methodological and epistemic differences between historical science and experimental science.--Philosophy of Science 69:474-496.

Chothia, C., I. Gelfland, & A. Kister. 1998. Structural determinants in the sequences of immunoglobulin variable domain.--Journal of Molecular Biology 278:457-479.

Conway Morris, S. 1998a. The question of metazoan monophyly and the fossil record.--Progress in Molecular and Subcellular Biology 21:1-9.

______. 1998b. Early Metazoan evolution: Reconciling paleontology and molecular biology.--American Zoologist 38 (1998):867-877.

______. 2000. Evolution: bringing molecules into the fold.--Cell 100:1-11.

______. 2003a. The Cambrian "explosion" of metazoans. Pp. 13-32 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

______. 2003b. Cambrian "explosion" of metazoans and molecular biology: would Darwin be satisfied?--International Journal of Developmental Biology 47(7-8):505-515.

______. 2003c. Life's solution: inevitable humans in a lonely universe. Cambridge University Press, Cambridge, United Kingdom.

Crick, F. 1958. On protein synthesis.--Symposium for the Society of Experimental Biology. 12(1958):138-163.

Darwin, C. 1859. On the origin of species. John Murray, London, United Kingdom.

______. 1896. Letter to Asa Gray. P. 437 in F. Darwin, ed., Life and letters of Charles Darwin, vol. 1., D. Appleton, London, United Kingdom.

Davidson, E. 2001. Genomic regulatory systems: development and evolution. Academic Press, New York, New York.

Dawkins, R. 1986. The blind watchmaker. Penguin Books, London, United Kingdom.

______. 1995. River out of Eden. Basic Books, New York.

______. 1996. Climbing Mount Improbable. W. W. Norton & Company, New York.

Dembski, W. A. 1998. The design inference. Cambridge University Press, Cambridge, United Kingdom.

______. 2002. No free lunch: why specified complexity cannot be purchased without intelligence. Rowman & Littlefield, Lanham, Maryland.

______. 2004. The logical underpinnings of intelligent design. Pp. 311-330 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Denton, M. 1986. Evolution: a theory in crisis. Adler & Adler, London, United Kingdom.

______. 1998. Nature's density. The Free Press, New York.

Eden, M. 1967. The inadequacies of neo-Darwinian evolution as a scientific theory. Pp. 5-12 in P. S. Morehead and M. M. Kaplan, eds., Mathematical challenges to the Darwinian interpretation of evolution. Wistar Institute Symposium Monograph, Allen R. Liss, New York.

Eldredge, N., & S. J. Gould. 1972. Punctuated equilibria: an alternative to phyletic gradualism. Pp. 82-115 in T. Schopf, ed., Models in paleobiology. W. H. Freeman, San Francisco.

Erwin, D. H. 1994. Early introduction of major morphological innovations.--Acta Palaeontologica Polonica 38:281-294.

______. 2000. Macroevolution is more than repeated rounds of microevolution.--Evolution & Development 2:78-84.

______. 2004. One very long argument.--Biology and Philosophy 19:17-28.

______, J. Valentine, & D. Jablonski. 1997. The origin of animal body plans.--American Scientist 85:126-137.

______, ______, & J. J. Sepkoski. 1987. A comparative study of diversification events: the early Paleozoic versus the Mesozoic.--Evolution 41:1177-1186.

Foote, M. 1997. Sampling, taxonomic description, and our evolving knowledge of morphological diversity.--Paleobiology 23:181-206.

______, J. P. Hunter, C. M. Janis, & J. J. Sepkoski. 1999. Evolutionary and preservational constraints on origins of biologic groups: Divergence times of eutherian mammals.--Science 283:1310-1314.

Frankel, J. 1980. Propagation of cortical differences in tetrahymena.--Genetics 94:607-623.

Gates, B. 1996. The road ahead. Blue Penguin, Boulder, Colorado.
Gerhart, J., & M. Kirschner. 1997. Cells, embryos, and evolution. Blackwell Science, London, United Kingdom.

Gibbs, W. W. 2003. The unseen genome: gems among the junk.--Scientific American. 289:46-53.

Gilbert, S. F., J. M. Opitz, & R. A. Raff. 1996. Resynthesizing evolutionary and developmental biology.--Developmental Biology 173:357-372.

Gillespie, N. C. 1979. Charles Darwin and the problem of creation. University of Chicago Press, Chicago.

Goodwin, B. C. 1985. What are the causes of morphogenesis?--BioEssays 3:32-36.

______. 1995. How the leopard changed its spots: the evolution of complexity. Scribner's, New York, New York.

Gould, S. J. 1965. Is uniformitarianism necessary?--American Journal of Science 263:223-228.

Gould, S. J. 2002. The structure of evolutionary theory. Harvard University Press, Cambridge, Massachusetts.

Grant, P. R. 1999. Ecology and evolution of Darwin's finches. Princeton University Press, Princeton, New Jersey.

Grimes, G. W., & K. J. Aufderheide. 1991. Cellular aspects of pattern formation: the problem of assembly. Monographs in Developmental Biology, vol. 22. Karger, Baseline, Switzerland.

Grotzinger, J. P., S. A. Bowring, B. Z. Saylor, & A. J. Kaufman. 1995. Biostratigraphic and geochronologic constraints on early animal evolution.--Science 270:598-604.

Harold, F. M. 1995. From morphogenes to morphogenesis.--Microbiology 141:2765-2778.

______. 2001. The way of the cell: molecules, organisms, and the order of life. Oxford University Press, New York.

Hodge, M. J. S. 1977. The structure and strategy of Darwin's long argument.--British Journal for the History of Science 10:237-245.

Hooykaas, R. 1975. Catastrophism in geology, its scientific character in relation to actualism and uniformitarianism. Pp. 270-316 in C. Albritton, ed., Philosophy of geohistory (1785-1970). Dowden, Hutchinson & Ross, Stroudsburg, Pennsylvania.

John, B., & G. Miklos. 1988. The eukaryote genome in development and evolution. Allen & Unwinding, London, United Kingdom.

Kauffman, S. 1995. At home in the universe. Oxford University Press, Oxford, United Kingdom.

Kenyon, D., & G. Mills. 1996. The RNA world: a critique.--Origins & Design 17(1):9-16.

Kerr, R. A. 1993. Evolution's Big Bang gets even more explosive.-- Science 261:1274-1275.

Kimura, M. 1983. The neutral theory of molecular evolution. Cambridge University Press, Cambridge, United Kingdom.

Koonin, E. 2000. How many genes can make a cell?: the minimal genome concept.--Annual Review of Genomics and Human Genetics 1:99-116.

Kuppers, B. O. 1987. On the prior probability of the existence of life. Pp. 355-369 in L. Kruger et al., eds., The probabilistic revolution. M.I.T. Press, Cambridge, Massachusetts.

Lange, B. M. H., A. J. Faragher, P. March, & K. Gull. 2000. Centriole duplication and maturation in animal cells. Pp. 235-249 in R. E. Palazzo and G. P. Schatten, eds., The centrosome in cell replication and early development. Current Topics in Developmental Biology, vol. 49. Academic Press, San Diego.

Lawrence, P. A., & G. Struhl. 1996. Morphogens, compartments and pattern: lessons from Drosophila?--Cell 85:951-961.

Lenior, T. 1982. The strategy of life. University of Chicago Press, Chicago.

Levinton, J. 1988. Genetics, paleontology, and macroevolution. Cambridge University Press, Cambridge, United Kingdom.

______. 1992. The big bang of animal evolution.--Scientific American 267:84-91.

Lewin, R. 1988. A lopsided look at evolution.--Science 241:292.

Lewontin, R. 1978. Adaptation. Pp. 113-125 in Evolution: a Scientific American book. W. H. Freeman & Company, San Francisco.

Lipton, P. 1991. Inference to the best explanation. Routledge, New York.

Lonnig, W. E. 2001. Natural selection. Pp. 1008-1016 in W. E. Craighead and C. B. Nemeroff, eds., The Corsini encyclopedia of psychology and behavioral sciences, 3rd edition, vol. 3. John Wiley & Sons, New York.

______, & H. Saedler. 2002. Chromosome rearrangements and transposable elements.--Annual Review of Genetics 36:389-410.

Lovtrup, S. 1979. Semantics, logic and vulgate neo-darwinism.--Evolutionary Theory 4:157-172.

Marshall, W. F. & J. L. Rosenbaum. 2000. Are there nucleic acids in the centrosome? Pp. 187-205 in R. E. Palazzo and G. P. Schatten, eds., The centrosome in cell replication and early development. Current Topics in Developmental Biology, vol. 49. San Diego, Academic Press.

Maynard Smith, J. 1986. Structuralism versus selection--is Darwinism enough? Pp. 39-46 in S. Rose and L. Appignanesi, eds., Science and Beyond. Basil Blackwell, London, United Kingdom.

Mayr, E. 1982. Foreword. Pp. xi-xii in M. Ruse, Darwinism defended. Pearson Addison Wesley, Boston, Massachusetts.

McDonald, J. F. 1983. The molecular basis of adaptation: a critical review of relevant ideas and observations.--Annual Review of Ecology and Systematics 14:77-102.

McNiven, M. A. & K. R. Porter. 1992. The centrosome: contributions to cell form. Pp. 313-329 in V. I. Kalnins, ed., The centrosome. Academic Press, San Diego.

Meyer, S. C. 1991. Of clues and causes: a methodological interpretation of origin of life studies. Unpublished doctoral dissertation, University of Cambridge, Cambridge, United Kingdom.

______. 1998. DNA by design: an inference to the best explanation for the origin of biological information.--Rhetoric & Public Affairs, 1(4):519-555.

______. The scientific status of intelligent design: The methodological equivalence of naturalistic and non-naturalistic origins theories. Pp. 151-211 in Science and evidence for design in the universe. Proceedings of the Wethersfield Institute. Ignatius Press, San Francisco.

______. 2003. DNA and the origin of life: information, specification and explanation. Pp. 223-285 in J. A. Campbell and S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing, Michigan.

______. 2004. The Cambrian information explosion: evidence for intelligent design. Pp. 371-391 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

______, M. Ross, P. Nelson, & P. Chien. 2003. The Cambrian explosion: biology's big bang. Pp. 323-402 in J. A. Campbell & S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing. See also Appendix C: Stratigraphic first appearance of phyla body plans, pp. 593-598.

Miklos, G. L. G. 1993. Emergence of organizational complexities during metazoan evolution: perspectives from molecular biology, palaeontology and neo-Darwinism.--Mem. Ass. Australas. Palaeontols, 15:7-41.

Monastersky, R. 1993. Siberian rocks clock biological big bang.--Science News 144:148.

Moss, L. 2004. What genes can't do. The M.I.T. Press, Cambridge, Massachusetts.

Muller, G. B. & S. A. Newman. 2003. Origination of organismal form: the forgotten cause in evolutionary theory. Pp. 3-12 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

Nanney, D. L. 1983. The ciliates and the cytoplasm.--Journal of Heredity, 74:163-170.

Nelson, P., & J. Wells. 2003. Homology in biology: problem for naturalistic science and prospect for intelligent design. Pp. 303-322 in J. A. Campbell and S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing.

Nijhout, H. F. 1990. Metaphors and the role of genes in development.--BioEssays 12:441-446.

Nusslein-Volhard, C., & E. Wieschaus. 1980. Mutations affecting segment number and polarity in Drosophila.--Nature 287:795-801.

Ohno, S. 1996. The notion of the Cambrian pananimalia genome.--Proceedings of the National Academy of Sciences, U.S.A. 93:8475-8478.

Orgel, L. E., & F. H. Crick. 1980. Selfish DNA: the ultimate parasite.--Nature 284:604-607.

Perutz, M. F., & H. Lehmann. 1968. Molecular pathology of human hemoglobin.--Nature 219:902-909.

Polanyi, M. 1967. Life transcending physics and chemistry.--Chemical and Engineering News, 45(35):54-66.

______. 1968. Life's irreducible structure.--Science 160:1308-1312, especially p. 1309.

Pourquie, O. 2003. Vertebrate somitogenesis: a novel paradigm for animal segmentation?--International Journal of Developmental Biology 47(7-8):597-603.

Quastler, H. 1964. The emergence of biological organization. Yale University Press, New Haven, Connecticut.

Raff, R. 1999. Larval homologies and radical evolutionary changes in early development, Pp. 110-121 in Homology. Novartis Symposium, vol. 222. John Wiley & Sons, Chichester, United Kingdom.

Reidhaar-Olson, J., & R. Sauer. 1990. Functionally acceptable solutions in two alpha-helical regions of lambda repressor.--Proteins, Structure, Function, and Genetics, 7:306-316.

Rutten, M. G. 1971. The origin of life by natural causes. Elsevier, Amsterdam.

Sapp, J. 1987. Beyond the gene. Oxford University Press, New York.

Sarkar, S. 1996. Biological information: a skeptical look at some central dogmas of molecular biology. Pp. 187-233 in S. Sarkar, ed., The philosophy and history of molecular biology: new perspectives. Kluwer Academic Publishers, Dordrecht.

Schutzenberger, M. 1967. Algorithms and the neo-Darwinian theory of evolution. Pp. 73-75 in P. S. Morehead and M. M. Kaplan, eds., Mathematical challenges to the Darwinian interpretation of evolution. Wistar Institute Symposium Monograph. Allen R. Liss, New York.

Shannon, C. 1948. A mathematical theory of communication.--Bell System Technical Journal 27:379-423, 623-656.

Shu, D. G., H. L. Loud, S. Conway Morris, X. L. Zhang, S. X. Hu, L. Chen, J. Han, M. Zhu, Y. Li, & L. Z. Chen. 1999. Lower Cambrian vertebrates from south China.--Nature 402:42-46.

Shubin, N. H., & C. R. Marshall. 2000. Fossils, genes, and the origin of novelty. Pp. 324-340 in Deep time. The Paleontological Society.

Simpson, G. 1970. Uniformitarianism: an inquiry into principle, theory, and method in geohistory and biohistory. Pp. 43-96 in M. K. Hecht and W. C. Steered, eds., Essays in evolution and genetics in honor of Theodosius Dobzhansky. Appleton-Century-Crofts, New York.

Sober, E. 2000. The philosophy of biology, 2nd edition. Westview Press, San Francisco.

Sonneborn, T. M. 1970. Determination, development, and inheritance of the structure of the cell cortex. In Symposia of the International Society for Cell Biology 9:1-13.

Sole, R. V., P. Fernandez, & S. A. Kauffman. 2003. Adaptive walks in a gene network model of morphogenesis: insight into the Cambrian explosion.--International Journal of Developmental Biology 47(7-8):685-693.

Stadler, B. M. R., P. F. Stadler, G. P. Wagner, & W. Fontana. 2001. The topology of the possible: formal spaces underlying patterns of evolutionary change.--Journal of Theoretical Biology 213:241-274.

Steiner, M., & R. Reitner. 2001. Evidence of organic structures in Ediacara-type fossils and associated microbial mats.--Geology 29(12):1119-1122.

Taylor, S. V., K. U. Walter, P. Kast, & D. Hilvert. 2001. Searching sequence space for protein catalysts.--Proceedings of the National Academy of Sciences, U.S.A. 98:10596-10601.

Thaxton, C. B., W. L. Bradley, & R. L. Olsen. 1992. The mystery of life's origin: reassessing current theories. Lewis and Stanley, Dallas, Texas.

Thompson, D. W. 1942. On growth and form, 2nd edition. Cambridge University Press, Cambridge, United Kingdom.

Thomson, K. S. 1992. Macroevolution: The morphological problem.--American Zoologist 32:106-112.

Valentine, J. W. 1995. Late Precambrian bilaterians: grades and clades. Pp. 87-107 in W. M. Fitch and F. J. Ayala, eds., Temporal and mode in evolution: genetics and paleontology 50 years after Simpson. National Academy Press, Washington, D.C.

______. 2004. On the origin of phyla. University of Chicago Press, Chicago, Illinois.

______, & D. H. Erwin, 1987. Interpreting great developmental experiments: the fossil record. Pp. 71-107 in R. A. Raff and E. C. Raff, eds., Development as an evolutionary process. Alan R. Liss, New York.

______, & D. Jablonski. 2003. Morphological and developmental macroevolution: a paleontological perspective.--International Journal of Developmental Biology 47:517-522.

Wagner, G. P. 2001. What is the promise of developmental evolution? Part II: A causal explanation of evolutionary innovations may be impossible.--Journal of Experimental Zoology (Mol. Dev. Evol.) 291:305-309.

______, & P. F. Stadler. 2003. Quasi-independence, homology and the Unity-C of type: a topological theory of characters.--Journal of Theoretical Biology 220:505-527.

Webster, G., & B. Goodwin. 1984. A structuralist approach to morphology.--Rivista di Biologia 77:503-10.

______, & ______. 1996. Form and transformation: generative and relational principles in biology. Cambridge University Press, Cambridge, United Kingdom.

Weiner, J. 1994. The beak of the finch. Vintage Books, New York.

Willmer, P. 1990. Invertebrate relationships: patterns in animal evolution. Cambridge University Press, Cambridge, United Kingdom.

______. 2003. Convergence and homoplasy in the evolution of organismal form. Pp. 33-50 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

Woese, C. 1998. The universal ancestor.--Proceedings of the National Academy of Sciences, U.S.A. 95:6854-6859.

Wray, G. A., J. S. Levinton, & L. H. Shapiro. 1996. Molecular evidence for deep Precambrian divergences among metazoan phyla.--Science 274:568-573.

Yockey, H. P. 1978. A calculation of the probability of spontaneous biogenesis by information theory.--Journal of Theoretical Biology 67:377-398.

______, 1992. Information theory and molecular biology, Cambridge University Press, Cambridge, United Kingdom.

End Notes


En Español (PDF)

PROCEEDINGS OF THE BIOLOGICAL SOCIETY OF WASHINGTON
117(2):213-239. 2004

The origin of biological information and the higher taxonomic categories
Stephen C. Meyer

(more…)
Abstract: Conservation of information theorems indicate that any search algorithm performs, on average, as well as random search without replacement unless it takes advantage of problem-specific information about the search target or the search-space structure. Combinatorics shows that even a moderately sized search requires problem-specific information to be successful. Computers, despite their speed in performing queries, are completely inadequate for resolving even moderately sized search problems without accurate information to guide them. We propose three measures to characterize the information required for successful search: 1) endogenous information, which measures the difficulty of finding a target using random search; 2) exogenous information, which measures the difficulty that remains in finding a target once a search takes advantage of problem-specific information; and 3) active information, which, as the difference between endogenous and exogenous information, measures the contribution of problem-specific information for successfully finding a target. This paper develops a methodology based on these information measures to gauge the effectiveness with which problem-specific information facilitates successful search. It then applies this methodology to various search tools widely used in evolutionary search. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans ( Volume: 39, Issue: 5, Sept. 2009 )
(more…)

The Current Landscape

In December of 2004, the renowned British philosopher Antony Flew made worldwide news when he repudiated a lifelong commitment to atheism, citing, among other factors, evidence of intelligent design in the DNA molecule. In that same month, the American Civil Liberties Union filed suit to prevent a Dover, Pennsylvania school district from informing its students that they could learn about the theory of intelligent design from a supplementary science textbook in their school library. The following February, The Wall Street Journal (Klinghoffer 2005) reported that an evolutionary biologist at the Smithsonian Institution with two doctorates had been punished for publishing a peer-reviewed scientific article making a case for intelligent design.

Since 2005, the theory of intelligent design has been the focus of a frenzy of international media coverage, with prominent stories appearing in The New York Times, Nature, The London Times, The Independent (London), Sekai Nippo (Tokyo), The Times of India, Der Spiegel, The Jerusalem Post and Time magazine, to name just a few. And recently, a major conference about intelligent design was held in Prague (attended by some 700 scientists, students and scholars from Europe, Africa and the United States), further signaling that the theory of intelligent design has generated worldwide interest.

But what is this theory of intelligent design, and where did it come from? And why does it arouse such passion and inspire such apparently determined efforts to suppress it?

According to a spate of recent media reports, intelligent design is a new “faith-based” alternative to evolution – one based on religion rather than scientific evidence. As the story goes, intelligent design is just biblical creationism repackaged by religious fundamentalists in order to circumvent a 1987 United States Supreme Court prohibition against teaching creationism in the U.S. public schools. Over the past two years, major newspapers, magazines and broadcast outlets in the United States and around the world have repeated this trope.

But is it accurate? As one of the architects of the theory of intelligent design and the director of a research center that supports the work of scientists developing the theory, I know that it isn't.

The modern theory of intelligent design was not developed in response to a legal setback for creationists in 1987. Instead, it was first proposed in the late 1970s and early 1980s by a group of scientists – Charles Thaxton, Walter Bradley and Roger Olson – who were trying to account for an enduring mystery of modern biology: the origin of the digital information encoded along the spine of the DNA molecule. Thaxton and his colleagues came to the conclusion that the information-bearing properties of DNA provided strong evidence of a prior but unspecified designing intelligence. They wrote a book proposing this idea in 1984, three years before the U.S. Supreme Court decision (in Edwards v. Aguillard) that outlawed the teaching of creationism.

Earlier in the 1960s and 1970s, physicists had already begun to reconsider the design hypothesis. Many were impressed by the discovery that the laws and constants of physics are improbably “finely-tuned” to make life possible. As British astrophysicist Fred Hoyle put it, the fine-tuning of the laws and constants of physics suggested that a designing intelligence “had monkeyed with physics” for our benefit.

Contemporary scientific interest in the design hypothesis not only predates the U.S. Supreme Court ruling against creationism, but the formal theory of intelligent design is clearly different than creationism in both its method and content. The theory of intelligent design, unlike creationism, is not based upon the Bible. Instead, it is based on observations of nature which the theory attempts to explain based on what we know about the cause and effect structure of the world and the patterns that generally indicate intelligent causes. Intelligent design is an inference from empirical evidence, not a deduction from religious authority.

The propositional content of the theory of intelligent design also differs from that of creationism. Creationism or Creation Science, as defined by the U.S. Supreme Court, defends a particular reading of the book of Genesis in the Bible, typically one that asserts that the God of the Bible created the earth in six literal twenty-four hour periods a few thousand years ago. The theory of intelligent design does not offer an interpretation of the book of Genesis, nor does it posit a theory about the length of the Biblical days of creation or even the age of the earth. Instead, it posits a causal explanation for the observed complexity of life.

But if the theory of intelligent design is not creationism, what is it? Intelligent design is an evidence-based scientific theory about life's origins that challenges strictly materialistic views of evolution. According to Darwinian biologists such as Oxford's Richard Dawkins (1986: 1), livings systems “give the appearance of having been designed for a purpose.” But, for modern Darwinists, that appearance of design is entirely illusory. Why? According to neo-Darwinism, wholly undirected processes such as natural selection and random mutations are fully capable of producing the intricate designed-like structures in living systems. In their view, natural selection can mimic the powers of a designing intelligence without itself being directed by an intelligence of any kind.

In contrast, the theory of intelligent design holds that there are tell-tale features of living systems and the universe – for example, the information-bearing properties of DNA, the miniature circuits and machines in cells and the fine tuning of the laws and constants of physics – that are best explained by an intelligent cause rather than an undirected material process. The theory does not challenge the idea of “evolution” defined as either change over time or common ancestry, but it does dispute Darwin's idea that the cause of biological change is wholly blind and undirected. Either life arose as the result of purely undirected material processes or a guiding intelligence played a role. Design theorists affirm the latter option and argue that living organisms look designed because they really were designed.

A Brief History of the Design Argument

By making a case for design based on observations of natural phenomena, advocates of the contemporary theory of intelligent design have resuscitated the classical design argument. Prior to the publication of The Origin of Species by Charles Darwin in 1859, many Western thinkers, for over two thousand years, had answered the question “how did life arise?” by invoking the activity of a purposeful designer. Design arguments based on observations of the natural world were made by Greek and Roman philosophers such as Plato (1960: 279) and Cicero (1933: 217), by Jewish philosophers such as Maimonides and by Christian thinkers such as Thomas Aquinas1 (see Hick 1970: 1).

The idea of design also figured centrally in the modern scientific revolution (1500-1700). As historians of science (see Gillespie 1987: 1-49) have often pointed out, many of the founders of early modern science assumed that the natural world was intelligible precisely because they also assumed that it had been designed by a rational mind. In addition, many individual scientists – Johannes Kepler in astronomy (see Kepler 1981: 93-103; Kepler 1995: 170, 240),2 John Ray in biology (see Ray 1701) and Robert Boyle in chemistry (see Boyle 1979: 172) – made specific design arguments based upon empirical discoveries in their respective fields. This tradition attained an almost majestic rhetorical quality in the writing of Sir Isaac Newton, who made both elegant and sophisticated design arguments based upon biological, physical and astronomical discoveries. Writing in the General Scholium to the Principia, Newton (1934: 543-44) suggested that the stability of the planetary system depended not only upon the regular action of universal gravitation, but also upon the very precise initial positioning of the planets and comets in relation to the sun. As he explained:

[T]hough these bodies may, indeed, continue in their orbits by the mere laws of gravity, yet they could by no means have at first derived the regular position of the orbits themselves from those laws [...] [Thus] [t]his most beautiful system of the sun, planets and comets, could only proceed from the counsel and dominion of an intelligent and powerful Being.

Or as he wrote in the Opticks:

How came the Bodies of Animals to be contrived with so much Art, and for what ends were their several parts? Was the Eye contrived without Skill in Opticks, and the Ear without Knowledge of Sounds? [...] And these things being rightly dispatch’d, does it not appear from Phænomena that there is a Being incorporeal, living, intelligent, omnipresent [...]. (Newton 1952: 369-70.)

Scientists continued to make such design arguments well into the early nineteenth century, especially in biology. By the later part of the 18th century, however, some enlightenment philosophers began to express skepticism about the design argument. In particular, David Hume, in his Dialogues Concerning Natural Religion (1779), argued that the design argument depended upon a flawed analogy with human artifacts. He admitted that artifacts derive from intelligent artificers, and that biological organisms have certain similarities to complex human artifacts. Eyes and pocket watches both depend upon the functional integration of many separate and specifically configured parts. Nevertheless, he argued, biological organisms also differ from human artifacts – they reproduce themselves, for example – and the advocates of the design argument fail to take these dissimilarities into account. Since experience teaches that organisms always come from other organisms, Hume argued that analogical argument really ought to suggest that organisms ultimately come from some primeval organism (perhaps a giant spider or vegetable), not a transcendent mind or spirit.

Despite this and other objections, Hume’s categorical rejection of the design argument did not prove entirely decisive with either theistic or secular philosophers. Thinkers as diverse as the Scottish Presbyterian Thomas Reid (1981: 59), the Enlightenment deist Thomas Paine (1925: 6) and the rationalist philosopher Immanuel Kant, continued to affirm3 various versions of the design argument after the publication of Hume’s Dialogues. Moreover, with the publication of William Paley’s Natural Theology, science-based design arguments would achieve new popularity, both in Britain and on the continent. Paley (1852: 8-9) catalogued a host of biological systems that suggested the work of a superintending intelligence. Paley argued that the astonishing complexity and superb adaptation of means to ends in such systems could not originate strictly through the blind forces of nature, any more than could a complex machine such as a pocket watch. Paley also responded directly to Hume’s claim that the design inference rested upon a faulty analogy. A watch that could reproduce itself, he argued, would constitute an even more marvelous effect than one that could not. Thus, for Paley, the differences between artifacts and organisms only seemed to strengthen the conclusion of design. And indeed, despite the widespread currency of Hume’s objections, many scientists continued to find Paley’s watch-to-watchmaker reasoning compelling well into 19th century.

Darwin and the Eclipse of Design

Acceptance of the design argument began to abate during the late 19th century with the emergence of increasingly powerful materialistic explanations of apparent design in biology, particularly Charles Darwin’s theory of evolution by natural selection. Darwin argued in 1859 that living organisms only appeared to be designed. To make this case, he proposed a concrete mechanism, natural selection acting on random variations, that could explain the adaptation of organisms to their environment (and other evidences of apparent design) without actually invoking an intelligent or directing agency. Darwin saw that natural forces would accomplish the work of a human breeder and thus that blind nature could come to mimic, over time, the action of a selecting intelligence – a designer. If the origin of biological organisms could be explained naturalistically,4 as Darwin (1964: 481-82) argued, then explanations invoking an intelligent designer were unnecessary and even vacuous.

Thus, it was not ultimately the arguments of the philosophers that destroyed the popularity of the design argument, but a scientific theory of biological origins. This trend was reinforced by the emergence of other fully naturalistic origins scenarios in astronomy, cosmology and geology. It was also reinforced (and enabled) by an emerging positivistic tradition in science that increasingly sought to exclude appeals to supernatural or intelligent causes from science by definition (see Gillespie 1979: 41-66, 82-108 for a discussion of this methodological shift). Natural theologians such as Robert Chambers, Richard Owen and Asa Gray, writing just prior to Darwin, tended to oblige this convention by locating design in the workings of natural law rather than in the complex structure or function of particular objects. While this move certainly made the natural theological tradition more acceptable to shifting methodological canons in science, it also gradually emptied it of any distinctive empirical content, leaving it vulnerable to charges of subjectivism and vacuousness. By locating design more in natural law and less in complex contrivances that could be understood by direct comparison to human creativity, later British natural theologians ultimately made their research program indistinguishable from the positivistic and fully naturalistic science of the Darwinians (Dembski 1996). As a result, the notion of design, to the extent it maintained any intellectual currency, soon became relegated to a matter of subjective belief. One could still believe that a mind superintended over the regular law-like workings of nature, but one might just as well assert that nature and its laws existed on their own. Thus, by the end of the nineteenth century, natural theologians could no longer point to any specific artifact of nature that required intelligence as a necessary explanation. As a result, intelligent design became undetectable except “through the eyes of faith.”

Though the design argument in biology went into retreat after the publication of The Origin, it never quite disappeared. Darwin was challenged by several leading scientists of his day, most forcefully by the great Harvard naturalist Louis Agassiz, who argued that the sudden appearance of the first complex animal forms in the Cambrian fossil record pointed to “an intellectual power” and attested to “acts of mind.” Similarly, the co-founder of the theory of evolution by natural selection, Alfred Russel Wallace (1991: 33-34), argued that some things in biology were better explained by reference to the work of a “Higher intelligence” than by reference to Darwinian evolution. There seemed to him “to be evidence of a Power” guiding the laws of organic development “in definite directions and for special ends.” As he put it, “[S]o far from this view being out of harmony with the teachings of science, it has a striking analogy with what is now taking place in the world.” And in 1897, Oxford scholar F.C.S. Schiller argued that “it will not be possible to rule out the supposition that the process of Evolution may be guided by an intelligent design” (Schiller 1903: 141).

This continued interest in the design hypothesis was made possible in part because the mechanism of natural selection had a mixed reception in the immediate post-Darwinian period. As the historian of biology Peter Bowler (1986: 44-50) has noted, classical Darwinism entered a period of eclipse during the late 19th and early 20th centuries mainly because Darwin lacked an adequate theory for the origin and transmission of new heritable variation. Natural selection, as Darwin well understood, could accomplish nothing without a steady supply of genetic variation, the ultimate source of new biological structure. Nevertheless, both the blending theory of inheritance that Darwin had assumed and the classical Mendelian genetics that soon replaced it, implied limitations on the amount of genetic variability available to natural selection. This in turn implied limits on the amount of novel structure that natural selection could produce.

By the late 1930s and 1940s, however, natural selection was revived as the main engine of evolutionary change as developments in a number of fields helped to clarify the nature of genetic variation. The resuscitation of the variation / natural selection mechanism by modern genetics and population genetics became known as the neo-Darwinian synthesis. According to the new synthetic theory of evolution, the mechanism of natural selection acting upon random variations (especially including small-scale mutations) sufficed to account for the origin of novel biological forms and structures. Small-scale “microevolutionary” changes could be extrapolated indefinitely to account for large-scale “macroevolutionary” development. With the revival of natural selection, the neo-Darwinists would assert, like Darwinists before them, that they had found a “designer substitute” that could explain the appearance of design in biology as the result of an entirely undirected natural process.5 As Harvard evolutionary biologist Ernst Mayr (1982: xi-xii) has explained, “[T]he real core of Darwinism [...] is the theory of natural selection. This theory is so important for the Darwinian because it permits the explanation of adaptation, the ‘design’ of the natural theologian, by natural means.” By the centennial celebration of Darwin’s Origin of Species in 1959, it was assumed by many scientists that natural selection could fully explain the appearance of design and that, consequently, the design argument in biology was dead.

Problems with the Neo-Darwinian Synthesis

Since the late 1960s, however, the modern synthesis that emerged during the 1930s, 1940s and 1950s has begun to unravel in the face of new developments in paleontology, systematics, molecular biology, genetics and developmental biology. Since then a series of technical articles and books – including such recent titles as Evolution: a Theory in Crisis (1986) by Michael Denton, Darwinism: The Refutation of a Myth (1987) by Soren Lovtrup, The Origins of Order (1993) by Stuart A. Kauffman, How The Leopard Changed Its Spots (1994) by Brian C. Goodwin, Reinventing Darwin (1995) by Niles Eldredge, The Shape of Life (1996) by Rudolf A. Raff, Darwin’s Black Box (1996) by Michael Behe, The Origin of Animal Body Plans (1997) by Wallace Arthur, Sudden Origins: Fossils, Genes, and the Emergence of Species (1999) by Jeffrey H. Schwartz – have cast doubt on the creative power of neo-Darwinism’s mutation/selection mechanism. As a result, a search for alternative naturalistic mechanisms of innovation has ensued with, as yet, no apparent success or consensus. So common are doubts about the creative capacity of the selection / mutation mechanism, neo-Darwinism’s “designer substitute,” that prominent spokesmen for evolutionary theory must now periodically assure the public that “just because we don’t know how evolution occurred, does not justify doubt about whether it occurred.”6 As Niles Eldredge (1982: 508-9) wrote, “Most observers see the current situation in evolutionary theory – where the object is to explain how, not if, life evolves – as bordering on total chaos.” Or as Stephen Gould (1980: 119-20) wrote, “The neoDarwinism synthesis is effectively dead, despite its continued presence as textbook orthodoxy.” (See also Müller and Newman 2003: 3-12.)

Soon after Gould and Eldredge acknowledged these difficulties, the first important books (Thaxton, et al. 1984; Denton 1985) advocating the idea of intelligent design as an alternative to neo-Darwinism began to appear in the United States and Britain.7 But the scientific antecedents of the modern theory of intelligent design can be traced back to the beginning of the molecular biological revolution. In 1953 when Watson and Crick elucidated the structure of the DNA molecule, they made a startling discovery. The structure of DNA allows it to store information in the form of a four-character digital code. (See Figure 1). Strings of precisely sequenced chemicals called nucleotide bases store and transmit the assembly instructions – the information – for building the crucial protein molecules and machines the cell needs to survive.

Francis Crick later developed this idea with his famous "sequence hypothesis" according to which the chemical constituents in DNA function like letters in a written language or symbols in a computer code. Just as English letters may convey a particular message depending on their arrangement, so too do certain sequences of chemical bases along the spine of a DNA molecule convey precise instructions for building proteins. The arrangement of the chemical characters determines the function of the sequence as a whole. Thus, the DNA molecule has the same property of “sequence specificity” or “specified complexity” that characterizes codes and language. As Richard Dawkins has acknowledged, “the machine code of the genes is uncannily computer-like” (Dawkins 1995: 11). As Bill Gates has noted, “DNA is like a computer program but far, far more advanced than any software ever created” (Gates 1995:188). After the early 1960s, further discoveries made clear that the digital information in DNA and RNA is only part of a complex information processing system – an advanced form of nanotechnology that both mirrors and exceeds our own in its complexity, design logic and information storage density.

Thus, even as the design argument was being declared dead at the Darwinian centennial at the close of the 1950s, evidence that many scientists would later see as pointing to design was being uncovered in the nascent discipline of molecular biology. In any case, discoveries in this field would soon generate a growing rumble of voices dissenting from neo-Darwinism. In By Design, a history of the current design controversy, journalist Larry Witham (2003) traces the immediate roots of the theory of intelligent design in biology to the 1960s, at which time developments in molecular biology were generating new problems for the neo-Darwinian synthesis. At this time, mathematicians, engineers and physicists were beginning to express doubts that random mutations could generate the genetic information needed to produce crucial evolutionary transitions in the time available to the evolutionary process. Among the most prominent of these skeptical scientists were several from the Massachusetts Institute of Technology.

Digital Information
Figure 1

These researchers might have gone on talking among themselves about their doubts but for an informal gathering of mathematicians and biologists in Geneva in the mid-1960s at the home of MIT physicist Victor Weisskopf. During a picnic lunch the discussion turned to evolution, and the mathematicians expressed surprise at the biologists’ confidence in the power of mutations to assemble the genetic information necessary to evolutionary innovation. Nothing was resolved during the argument that ensued, but those present found the discussion stimulating enough that they set about organizing a conference to probe the issue further. This gathering occurred at the Wistar Institute in Philadelphia in the spring of 1966 and was chaired by Sir Peter Medawar, Nobel Laureate and director of North London’s Medical Research Council's laboratories. In his opening remarks at the meeting, he said that the “immediate cause of this conference is a pretty widespread sense of dissatisfaction about what has come to be thought of as the accepted evolutionary theory in the English-speaking world, the so-called neo-Darwinian theory” (Taylor 1983: 4).

The mathematicians were now in the spotlight and they took the opportunity to argue that neo-Darwinism faced a formidable combinatorial problem (see Moorhead and Kaplan 1967 for the seminar proceedings).8 In their view, the ratio of the number of functional genes and proteins, on the one hand, to the enormous number of possible sequences corresponding to a gene or protein of a given length, on the other, seemed so small as to preclude the origin of genetic information by a random mutational search. A protein one hundred amino acids in length represents an extremely unlikely occurrence. There are roughly 10130 possible amino acid sequences of this length, if one considers only the 20 protein-forming acids as possibilities. The vast majority of these sequences – it was (correctly) assumed – perform no biological function (see Axe 2004: 1295-1314 for a rigorous experimental evaluation of the rarity of functional proteins within the “sequence space” of possible combinations). Would an undirected search through this enormous space of possible sequences have a realistic chance of finding a functional sequence in the time allotted for crucial evolutionary transitions? To many of the Wistar mathematicians and physicists, the answer seemed clearly ‘no.’ Distinguished French mathematician M. P. Schützenberger (1967: 73-5) noted that in human codes, randomness is never the friend of function, much less of progress. When we make changes randomly to computer programs, “we find that we have no chance (i.e. less than 1/101000) even to see what the modified program would compute: it just jams.” MIT’s Murray Eden illustrated with reference to an imaginary library evolving by random changes to a single phrase: “Begin with a meaningful phrase, retype it with a few mistakes, make it longer by adding letters, and rearrange subsequences in the string of letters; then examine the result to see if the new phrase is meaningful. Repeat until the library is complete” (Eden 1967: 110). Would such an exercise have a realistic chance of succeeding, even granting it billions of years? At Wistar, the mathematicians, physicists and engineers argued that it would not. And they insisted that a similar problem confronts any mechanism that relies on random mutations to search large combinatorial spaces for sequences capable of performing novel function – even if, as is the case in biology, some mechanism of selection can act after the fact to preserve functional sequences once they have arisen.

Just as the mathematicians at Wistar were casting doubt on the idea that chance (i.e., random mutations) could generate genetic information, another leading scientist was raising questions about the role of law-like necessity. In 1967 and 1968, the Hungarian chemist and philosopher of science Michael Polanyi published two articles suggesting that the information in DNA was “irreducible” to the laws of physics and chemistry (Polanyi 1967: 21; Polanyi 1968: 1308-12). In these papers, Polanyi noted that the DNA conveys information in virtue of very specific arrangements of the nucleotide bases (that is, the chemicals that function as alphabetic or digital characters) in the genetic text. Yet, Polanyi also noted the laws of physics and chemistry allow for a vast number of other possible arrangements of these same chemical constituents. Since chemical laws allow a vast number of possible arrangements of nucleotide bases, Polanyi reasoned that no specific arrangement was dictated or determined by those laws. Indeed, the chemical properties of the nucleotide bases allow them to attach themselves interchangeably at any site on the (sugar-phosphate) backbone of the DNA molecule. (See Figure 1). Thus, as Polanyi (1968: 1309) noted, “As the arrangement of a printed page is extraneous to the chemistry of the printed page, so is the base sequence in a DNA molecule extraneous to the chemical forces at work in the DNA molecule.” Polanyi argued that it is precisely this chemical indeterminacy that allows DNA to store information and which also shows the irreducibility of that information to physical-chemical laws or forces. As he explained:

Suppose that the actual structure of a DNA molecule were due to the fact that the bindings of its bases were much stronger than the bindings would be for any other distribution of bases, then such a DNA molecule would have no information content. Its code-like character would be effaced by an overwhelming redundancy. [...] Whatever may be the origin of a DNA configuration, it can function as a code only if its order is not due to the forces of potential energy. It must be as physically indeterminate as the sequence of words is on a printed page (Polanyi 1968:1309).

The Mystery of Life’s Origin

As more scientists began to express doubts about the ability of undirected processes to produce the genetic information necessary to living systems, some began to consider an alternative approach to the problem of the origin of biological form and information. In 1984, after seven years of writing and research, chemist Charles Thaxton, polymer scientist Walter Bradley and geochemist Roger Olsen published a book proposing “an intelligent cause” as an explanation for the origin of biological information. The book was titled The Mystery of Life’s Origin and was published by The Philosophical Library, then a prestigious New York scientific publisher that had previously published more than twenty Nobel laureates.

Thaxton, Bradley and Olsen’s work directly challenged reigning chemical evolutionary explanations of the origin-of-life, and old scientific paradigms do not, to borrow from a Dylan Thomas poem, “go gently into that good night.” Aware of the potential opposition to their ideas, Thaxton flew to California to meet with one of the world’s top chemical evolutionary theorists, San Francisco State University biophysicist Dean Kenyon, co-author of a leading monograph on the subject, Biochemical Predestination. Thaxton wanted to talk with Kenyon to ensure that Mystery’s critiques of leading origin-of-life theories (including Kenyon’s), were fair and accurate. But Thaxton also had a second and more audacious motive: he planned to ask Kenyon to write the foreword to the book, even though Mystery critiqued the very originof-life theory that had made Kenyon famous in his field.

One can imagine how such a meeting might have unfolded, with Thaxton’s bold plan quietly dying in a corner of Kenyon’s office as the two men came to loggerheads over their competing theories. But fortunately for Thaxton, things went better than expected. Before he had worked his way around to making his request, Kenyon volunteered for the job, explaining that he had been moving toward Thaxton’s position for some time (Charles Thaxton, interview by Jonathan Witt, August 16, 2005; Jon Buell, interview by Jonathan Witt, September 21, 2005).

Kenyon’s bestselling origin-of-life text, Biochemical Predestination, had outlined what was then arguably the most plausible evolutionary account of how a living cell might have organized itself from chemicals in the “primordial soup.” Already by the 1970s, however, Kenyon was questioning his own hypothesis. Experiments (some performed by Kenyon himself) increasingly suggested that simple chemicals do not arrange themselves into complex information-bearing molecules such as proteins and DNA without guidance from human investigators. Thaxton, Bradley and Olsen appealed to this fact in constructing their argument, and Kenyon found their case both well-reasoned and well-researched. In the foreword he went on to pen, he described The Mystery of Life’s Origin as “an extraordinary new analysis of an age-old question” (Kenyon 1984: v).

The book eventually became the best-selling advanced college-level work on chemical evolution, with sales fueled by endorsements from leading scientists such as Kenyon, Robert Shapiro and Robert Jastrow and by favorable reviews in prestigious journals such as the Yale Journal of Biology and Medicine.9 Others dismissed the work as going beyond science.

What was their idea, and why did it generate interest among leading scientists? First, Mystery critiqued all of the current, purely materialistic explanations for the origin of life. In the process, they showed that the famous Miller-Urey experiment did not simulate early Earth conditions, that the existence of an early Earth pre-biotic soup was a myth, that important chemical evolutionary transitions were subject to destructive interfering cross-reactions, and that neither chance nor energy-flow could account for the information in biopolymers such as proteins and DNA. But it was in the book’s epilogue that the three scientists proposed a radically new hypothesis. There they suggested that the information-bearing properties of DNA might point to an intelligent cause. Drawing on the work of Polanyi and others, they argued that chemistry and physics alone couldn’t produce information any more than ink and paper could produce the information in a book. Instead, they argued that our uniform experience suggests that information is the product of an intelligent cause:

We have observational evidence in the present that intelligent investigators can (and do) build contrivances to channel energy down nonrandom chemical pathways to bring about some complex chemical synthesis, even gene building. May not the principle of uniformity then be used in a broader frame of consideration to suggest that DNA had an intelligent cause at the beginning? (Thaxton et al. 1984: 211.)

Mystery also made the radical claim that intelligent causes could be legitimately considered as scientific hypotheses within the historical sciences, a mode of inquiry they called origins science.

Their book marked the beginning of interest in the theory of intelligent design in the United States, inspiring a generation of younger scholars (see Denton 1985; Denton 1986; Kenyon and Mills 1996: 9-16; Behe 2004: 352-370; Dembski 2002; Dembski 2004: 311-330; Morris 2000: 1-11; Morris 2003a: 13-32; Morris 2003b: 505-515; Lönnig 2001; Lönnig and Saedler 2002: 389-410; Nelson and Wells 2003: 303-322; Meyer 2003a: 223-285; Meyer 2003b: 371391; Bradley 2004: 331-351) to investigate the question of whether there is actual design in living organisms rather than, as neo-Darwinian biologists and chemical evolutionary theorists had long claimed, the mere appearance of design. At the time the book appeared, I was working as a geophysicist for the Atlantic Richfield Company in Dallas where Charles Thaxton happened to live. I later met him at a scientific conference and became intrigued with the radical idea he was developing about DNA. I began dropping by his office after work to discuss the arguments made in his book. Intrigued, but not yet fully convinced, the next year I left my job as a geophysicist to pursue a Ph.D. at The University of Cambridge in the history and philosophy of science. During my Ph.D. research, I investigated several questions that had emerged in my discussions with Thaxton. What methods do scientists use to study biological origins? Is there a distinctive method of historical scientific inquiry? After completing my Ph.D., I would take up another question: Could the argument from DNA to design be formulated as a rigorous historical scientific argument?

Of Clues and Causes

During my Ph.D. research at Cambridge, I found that historical sciences (such as geology, paleontology and archeology) do employ a distinctive method of inquiry. Whereas many scientific fields involve an attempt to discover universal laws, historical scientists attempt to infer past causes from present effects. As Stephen Gould (1986: 61) put it, historical scientists are trying to “infer history from its results.” Visit the Royal Tyrrell Museum in Alberta, Canada and you will find there a beautiful reconstruction of the Cambrian seafloor with its stunning assemblage of phyla. Or read the fourth chapter of Simon Conway Morris’s book on the Burgess Shale and you will be taken on a vivid guided tour of that long-ago place. But what Morris (1998: 63-115) and the museum scientists did in both cases was to imaginatively reconstruct the ancient Cambrian site from an assemblage of present-day fossils. In other words, paleontologists infer a past situation or cause from present clues.

A key figure in elucidating the special nature of this mode of reasoning was a contemporary of Darwin, polymath William Whewell, master of Trinity College, Cambridge and best known for two books about the nature of science, History of the Inductive Sciences (1837) and The Philosophy of the Inductive Sciences (1840). Whewell distinguished inductive sciences like mechanics (physics) from what he called palaetiology – historical sciences that are defined by three distinguishing features. First, the palaetiological or historical sciences have a distinctive object: to determine “ancient condition[s]” (Whewell 1857, vol. 3: 397) or past causal events. Second, palaetiological sciences explain present events (“manifest effects”) by reference to past (causal) events rather than by reference to general laws (though laws sometimes play a subsidiary role). And third, in identifying a “more ancient condition,” Whewell believed palaetiology utilized a distinctive mode of reasoning in which past conditions were inferred from "manifest effects" using generalizations linking present clues with past causes (Whewell 1840, vol. 2: 121-22, 101-103).

Inference to the Best Explanation

This type of inference is called abductive reasoning. It was first described by the American philosopher and logician C.S. Peirce. He noted that, unlike inductive reasoning, in which a universal law or principle is established from repeated observations of the same phenomena, and unlike deductive reasoning, in which a particular fact is deduced by applying a general law or rule to another particular fact or case, abductive reasoning infers unseen facts, events or causes in the past from clues or facts in the present.

As Peirce himself showed, however, there is a problem with abductive reasoning. Consider the following syllogism:

If it rains, the streets will get wet.
The streets are wet.
Therefore, it rained.

This syllogism infers a past condition (i.e., that it rained) but it commits a logical fallacy known as affirming the consequent. Given that the street is wet (and without additional evidence to decide the matter), one can only conclude that perhaps it rained. Why? Because there are many other possible ways by which the street may have gotten wet. Rain may have caused the streets to get wet; a street cleaning machine might have caused them to get wet; or an uncapped fire hydrant might have done so. It can be difficult to infer the past from the present because there are many possible causes of a given effect.

Peirce’s question was this: how is it that, despite the logical problem of affirming the consequent, we nevertheless frequently make reliable abductive inferences about the past? He noted, for example, that no one doubts the existence of Napoleon. Yet we use abductive reasoning to infer Napoleon’s existence. That is, we must infer his past existence from present effects. But despite our dependence on abductive reasoning to make this inference, no sane or educated person would doubt that Napoleon Bonaparte actually lived. How could this be if the problem of affirming the consequent bedevils our attempts to reason abductively? Peirce’s answer was revealing: "Though we have not seen the man [Napoleon], yet we cannot explain what we have seen without" the hypothesis of his existence (Peirce, 1932, vol. 2: 375). Peirce's words imply that a particular abductive hypothesis can be strengthened if it can be shown to explain a result in a way that other hypotheses do not, and that it can be reasonably believed (in practice) if it explains in a way that no other hypotheses do. In other words, an abductive inference can be enhanced if it can be shown that it represents the best or the only adequate explanation of the "manifest effects" (to use Whewell's term).

As Peirce pointed out, the problem with abductive reasoning is that there is often more than one cause that can explain the same effect. To address this problem, pioneering geologist Thomas Chamberlain (1965: 754-59) delineated a method of reasoning that he called “the method of multiple working hypotheses.” Geologists and other historical scientists use this method when there is more than one possible cause or hypothesis to explain the same evidence. In such cases, historical scientists carefully weigh the evidence and what they know about various possible causes to determine which best explains the clues before them. In modern times, contemporary philosophers of science have called this the method of inference to the best explanation. That is, when trying to explain the origin of an event or structure in the past, historical scientists compare various hypotheses to see which would, if true, best explain it. They then provisionally affirm that hypothesis that best explains the data as the most likely to be true.

Causes Now in Operation

But what constitutes the best explanation for the historical scientist? My research showed that among historical scientists it’s generally agreed that best doesn’t mean ideologically satisfying or mainstream; instead, best generally has been taken to mean, first and foremost, most causally adequate. In other words, historical scientists try to identify causes that are known to produce the effect in question. In making such determinations, historical scientists evaluate hypotheses against their present knowledge of cause and effect; causes that are known to produce the effect in question are judged to be better causes than those that are not. For instance, a volcanic eruption is a better explanation for an ash layer in the earth than an earthquake because eruptions have been observed to produce ash layers, whereas earthquakes have not.

This brings us to the great geologist Charles Lyell, a figure who exerted a tremendous influence on 19th century historical science generally and on Charles Darwin specifically. Darwin read Lyell’s magnum opus, The Principles of Geology, on the voyage of the Beagle and later appealed to its uniformitarian principles to argue that observed micro-evolutionary processes of change could be used to explain the origin of new forms of life. The subtitle of Lyell’s Principles summarized the geologist’s central methodological principle: “Being an Attempt to Explain the Former Changes of the Earth's Surface, by Reference to Causes now in Operation.” Lyell argued that when historical scientists are seeking to explain events in the past, they should not invoke unknown or exotic causes, the effects of which we do not know, but instead they should cite causes that are known from our uniform experience to have the power to produce the effect in question (i.e., “causes now in operation”).

Darwin subscribed to this methodological principle. His term for a “presently acting cause” was a vera causa, that is, a true or actual cause. In other words, when explaining the past, historical scientists should seek to identify established causes – causes known to produce the effect in question. For example, Darwin tried to show that the process of descent with modification was the vera causa of certain kinds of patterns found among living organisms. He noted that diverse organisms share many common features. He called these homologies and noted that we know from experience that descendents, although they differ from their ancestors, also resemble them in many ways, usually more closely than others who are more distantly related. So he proposed descent with modification as a vera causa for homologous structures. That is, he argued that our uniform experience shows that the process of descent with modification from a common ancestor is “causally adequate” or capable of producing homologous features.

And Then There Was One

Contemporary philosophers agree that causal adequacy is the key criteria by which competing hypotheses are adjudicated, but they also show that this process leads to secure inferences only where it can be shown that there is just one known cause for the evidence in question. Philosophers of science Michael Scriven and Elliot Sober, for example, point out that historical scientists can make inferences about the past with confidence when they discover evidence or artifacts for which there is only one cause known to be capable of producing them. When historical scientists infer to a uniquely plausible cause, they avoid the fallacy of affirming the consequent and the error of ignoring other possible causes with the power to produce the same effect. It follows that the process of determining the best explanation often involves generating a list of possible hypotheses, comparing their known or theoretically plausible causal powers with respect to the relevant data, and then like a detective attempting to identify the murderer, progressively eliminating potential but inadequate explanations until, finally, one remaining causally adequate explanation can be identified as the best. As Scriven (1966: 250) explains, such abductive reasoning (or what he calls “Reconstructive causal analysis”) “proceeds by the elimination of possible causes,” a process that is essential if historical scientists are to overcome the logical limitations of abductive reasoning.

The matter can be framed in terms of formal logic. As C.S. Peirce noted, arguments of the form:

if X, then Y
Y
therefore X

commit the fallacy of affirming the consequent. Nevertheless, as Michael Scriven (1959: 480), Elliot Sober (1988: 1-5), W.P. Alston (1971: 23) and W.B. Gallie (1959: 392) have observed, such arguments can be restated in a logically acceptable form if it can be shown that Y has only one known cause (i.e., X) or that X is a necessary condition (or cause) of Y. Thus, arguments of the form:

X is antecedently necessary to Y,
Y exists,
Therefore, X existed

are accepted as logically valid by philosophers and persuasive by historical and forensic scientists. Scriven especially emphasized this point: if scientists can discover an effect for which there is only one plausible cause, they can infer the presence or action of that cause in the past with great confidence. For instance, the archaeologist who knows that human scribes are the only known cause of linguistic inscriptions will infer scribal activity upon discovering tablets containing ancient writing.

In many cases, of course, the investigator will have to work his way to a unique cause one painstaking step at a time. For instance, both wind shear and compressor blade failure could explain an airline crash, but the forensic investigator will want to know which one did, or if the true cause lies elsewhere. Ideally, the investigator will be able to discover some crucial piece of evidence or suite of evidences for which there is only one known cause, allowing him to distinguish between competing explanations and eliminate every explanation but the correct one.

In my study of the methods of the historical sciences, I found that historical scientists, like detectives and forensic experts, routinely employ this type of abductive and eliminative reasoning in their attempts to infer the best explanation.10 In fact, Darwin himself employed this method in The Origin of Species. There he argued for his theory of Universal Common Descent, not because it could predict future outcomes under controlled experimental conditions, but because it could explain already known facts better than rival hypotheses. As he explained in a letter to Asa Gray:

I [...] test this hypothesis [Universal Common Descent] by comparison with as many general and pretty well-established propositions as I can find – in geographical distribution, geological history, affinities &c., &c. And it seems to me that, supposing that such a hypothesis were to explain such general propositions, we ought, in accordance with the common way of following all sciences, to admit it till some better hypothesis be found out. (Darwin 1896, vol. 1: 437.)

DNA by Design: Developing the Argument from Information

What does this investigation into the nature of historical scientific reasoning have to do with intelligent design, the origin of biological information and the mystery of life’s origin? For me, it was critically important to deciding whether the design hypothesis could be formulated as a rigorous scientific explanation as opposed to just an intriguing intuition. I knew from my study of origin-of-life research that the central question facing scientists trying to explain the origin of the first life was this: how did the sequence-specific digital information (stored in DNA and RNA) necessary to building the first cell arise? As Bernd-Olaf Küppers (1990: 170-172) put it, “the problem of the origin of life is clearly basically the equivalent to the problem of the origin of biological information.” My study of the methodology of the historical sciences then led me to ask a series of questions: What is the presently acting cause of the origin of digital information? What is the vera causa of such information? Or: what is the “only known cause” of this effect? Whether I used Lyell’s, Darwin’s or Scriven’s terminology, the question was the same: what type of cause has demonstrated the power to generate information? Based upon both common experience and my knowledge of the many failed attempts to solve the problem with “unguided” pre-biotic simulation experiments and computer simulations, I concluded that there is only one sufficient or “presently acting” cause of the origin of such functionally-specified information. And that cause is intelligence. In other words, I concluded, based on our experience-based understanding of the cause-and-effect structure of the world, that intelligent design is the best explanation for the origin of the information necessary to build the first cell. Ironically, I discovered that if one applies Lyell’s uniformitarian method – a practice much maligned by young earth creationists – to the question of the origin of biological information, the evidence from molecular biology supports a new and rigorous scientific argument to design.

What is Information?

In order to develop this argument and avoid equivocation, it was necessary to carefully define what type of information was present in the cell (and what type of information might, based upon our uniform experience, indicate the prior action of a designing intelligence). Indeed, part of the historical scientific method of reasoning involves first defining what philosophers of science call the explanandum – the entity that needs to be explained. As the historian of biology Harmke Kamminga (1986: 1) has observed, “At the heart of the problem of the origin of life lies a fundamental question: What is it exactly that we are trying to explain the origin of?” Contemporary biology had shown that the cell was, among other things, a repository of information. For this reason, origin-of-life studies had focused increasingly on trying to explain the origin of that information. But what kind of information is present in the cell? This was an important question to answer because the term “information” can be used to denote several theoretically distinct concepts.

In developing a case for design from the information-bearing properties of DNA, it was necessary to distinguish two key notions of information from one another: mere information carrying capacity, on the one hand, and functionally-specified information, on the other. It was important to make this distinction because the kind of information that is present in DNA (like the information present in machine code or written language) has a feature that the wellknown Shannon theory of information does not encompass or describe.

During the 1940s, Claude Shannon at Bell Laboratories developed a mathematical theory of information (1948: 379–423, 623–56) that equated the amount of information transmitted with the amount of uncertainty reduced or eliminated by a series of symbols or characters (Dretske, 1981: 6–10). In Shannon’s theory, the more improbable an event the more uncertainty it eliminates, and thus, the more information it conveys. Shannon generalized this relationship by stating that the amount of information conveyed by an event is inversely proportional to the prior probability of its occurrence. The greater the number of possibilities, the greater the improbability of any one being actualized, and thus the more information is transmitted when a particular possibility occurs.11

Shannon’s theory applies easily to sequences of alphabetic symbols or characters that function as such. Within a given alphabet of x possible characters, the occurrence or placement of a specific character eliminates x-1 other possibilities and thus a corresponding amount of uncertainty. Or put differently, within any given alphabet or ensemble of x possible characters (where each character has an equi-probable chance of occurring), the probability of any one character occurring is 1/x. In systems where the value of x can be known (or estimated), as in a code or language, mathematicians can easily generate quantitative estimates of informationcarrying capacity. The greater the number of possible characters at each site, and the longer the sequence of characters, the greater is the information-carrying capacity – or Shannon information – associated with the sequence.

The way that nucleotide bases in DNA function as alphabetic or digital characters enabled molecular biologists to calculate the information-carrying capacity of those molecules using the new formalism of Shannon’s theory. Since at any given site along the DNA backbone any one of four nucleotide bases may occur with equal probability (Küppers, 1987: 355-369), the probability of the occurrence of a specific nucleotide at that site equals 1/4 or .25. The information-carrying capacity of a sequence of a specific length n can then be calculated using

Shannon’s familiar expression (I = –log2p) once one computes a probability value (p) for the occurrence of a particular sequence n nucleotides long where p = (1/4)n. The probability value thus yields a corresponding measure of information-carrying capacity for a sequence of n nucleotide bases (Schneider 1997: 427-441; Yockey 1992: 246-258).

Though Shannon’s theory and equations provided a powerful way to measure the amount of information that could be transmitted across a communication channel, it had important limits. In particular, it did not and could not distinguish merely improbable (or complex) sequences of symbols from those that conveyed a message or performed a function. As Warren Weaver made clear in 1949, “The word information in this theory is used in a special mathematical sense that must not be confused with its ordinary usage. In particular, information must not be confused with meaning.” (Shannon and Weaver 1949: 8.) Information theory could measure the information-carrying capacity of a given sequence of symbols, but it could not distinguish the presence of a meaningful or functional arrangement of symbols from a random sequence.

As scientists applied Shannon information theory to biology it enabled them to render rough quantitative measures of the information-carrying capacity (or brute complexity or improbability) of DNA sequences and their corresponding proteins. As such, information theory did help to refine biologists’ understanding of one important feature of the crucial biomolecular components on which life depends: DNA and proteins are highly complex, and quantifiably so. Nevertheless, the ease with which information theory applied to molecular biology (to measure information-carrying capacity) created confusion about the sense in which DNA and proteins contain “information.”

Information theory strongly suggested that DNA and proteins possess vast informationcarrying capacities, as defined by Shannon’s theory. When molecular biologists have described DNA as the carrier of hereditary information, however, they have meant much more than that technically limited term information. Instead, leading molecular biologists defined biological information so as to incorporate the notion of specificity of function (as well as complexity) as early as 1958 (Crick, 1958: 144, 153). Molecular biologists such as Monod and Crick understood biological information – the information stored in DNA and proteins – as something more than mere complexity (or improbability). Crick and Monod also recognized that sequences of nucleotides and amino acids in functioning bio-macromolecules possessed a high degree of specificity relative to the maintenance of cellular function. As Crick explained in 1958, “By information I mean the specification of the amino acid sequence in protein [...] Information means here the precise determination of sequence, either of bases in the nucleic acid or on amino acid residues in the protein (1958: 144, 153).”

Since the late 1950s, biologists have equated the “precise determination of sequence” with the extra-information-theoretic property of “specificity” or “specification.” Biologists have defined specificity tacitly as ‘necessary to achieving or maintaining function.’ They have determined that DNA base sequences are specified, not by applying information theory, but by making experimental assessments of the function of those sequences within the overall apparatus of gene expression (Judson,1979: 470-487). Similar experimental considerations established the functional specificity of proteins.

In developing an argument for intelligent design based upon the information present in DNA and other bio-macromolecules, I emphasized that the information in these molecules was functionally-specified and complex, not just complex. Indeed, to avoid equivocation, it was necessary to distinguish:

“information content” from mere “information carrying capacity,”
“specified information” from mere “Shannon information”
“specified complexity” from mere “complexity.”

The first of the two terms in each of these couplets refer to sequences in which the function of the sequence depends upon the precise sequential arrangements of the constituent characters or parts, whereas second terms refer to sequences that do not necessarily perform functions or convey meaning at all. The second terms refer to sequences that may be merely improbable or complex; the first terms refer to sequences that are both complex and functionallyspecified.

In developing an argument for intelligent design from the information-bearing properties of DNA, I acknowledged that merely complex or improbable phenomena or sequences might arise by undirected natural processes. Nevertheless, I argued – based upon our uniform experience – that sequences that are both complex and functionally-specified (rich in information content or specified information) invariably arise only from the activity of intelligent agents. Thus, I argued that the presence of specified information provides a hallmark or signature of a designing intelligence. In making these analytical distinctions in order to apply them to an analysis of biological systems, I was greatly assisted in my conversations and collaboration with William Dembski who was at the same time (1992-1997) developing a general theory of design detection which I discuss in detail below.

In the years that followed, I published a series of papers (see Meyer 1998a: 519-56; Meyer 1998b, 117-143; Meyer 2000a: 30-38; Meyer 2003a: 225-285) arguing that intelligent design provides a better explanation than competing chemical evolutionary models for the origin of the biological information. To make this argument, I followed the standard method of historical scientific reasoning that I had studied in doctoral work. In particular, I evaluated the causal adequacy of various naturalistic explanations for the origin of biological information including those based on chance, law-like necessities and the combination of the two. In each case, I showed (or the scientific literature showed) that such naturalistic models failed to explain the origin of specified information (or specified complexity or information content) starting from purely physical / chemical antecedents. Instead, I argued, based on our experience, that there is a cause – namely, intelligence – that is known to be capable of producing such information. As the pioneering information theorist Henry Quastler (1964: 16) pointed out, “Information habitually arises from conscious activity.” Moreover, based upon our experience (and the findings of contemporary origin-of-life research) it is clear that intelligent design or agency is the only type of cause known to produce large amounts of specified information. Therefore, I argued that the theory of intelligent design provides the best explanation for the information necessary to build the first life.12

Darwin on Trial and Philip Johnson

While I was still studying historical scientific reasoning in Cambridge in 1987, I had a fateful meeting with a prominent University of California, Berkeley law professor named Phillip Johnson, whose growing interest in the subject of biological origins would transform the contours of the debate over evolution. Johnson and I met at a small Greek restaurant on Free School Lane next to the Old Cavendish Laboratory in Cambridge. The meeting had been arranged by a fellow graduate student who knew Johnson from Berkeley. My friend had told me only that Johnson was “a quirky but brilliant law professor” who “was on sabbatical studying torts,” and he “had become obsessed with evolution.” “Would you talk to him?” he asked. His description and the tone of his request led me to expect a very different figure than the one I encountered. Though my own skepticism about Darwinism had been well cemented by this time, I knew enough of the stereotypical evolution-basher to be skeptical that a late-in-career nonscientist could have stumbled onto an original critique of contemporary Darwinian theory.

Only later did I learn of Johnson’s intellectual pedigree: Harvard B.A., top of his class University of Chicago law-school graduate, law clerk for Supreme Court Chief Justice Earl Warren, leading constitutional scholar, occupant of a distinguished chair at University of California, Berkeley. In Johnson, I encountered a man of supple and prodigious intellect who seemed in short order to have found the pulse of the origins issue. Johnson told me that his doubts about Darwinism had started with a visit to the British Natural History Museum, where he learned about the controversy that had raged there earlier in the 1980s. At that time, the museum paleontologists presented a display describing Darwin’s theory as “one possible explanation” of origins. A furor ensued, resulting in the removal of the display when the editors of the prestigious journal Nature and others in the scientific establishment denounced the museum for its ambivalence about accepted fact. Intrigued by the response to such an apparently innocuous exhibit, Johnson decided to investigate further.

Soon thereafter, as Johnson was still casting about for a research topic early in his sabbatical year in London, he stepped off the bus and followed his usual route to his visiting faculty office. Along the way, he passed by a large science bookstore and, glancing in, noticed a pair of books about evolution, The Blind Watchmaker by Richard Dawkins and Evolution: A Theory in Crisis by Michael Denton. Historian of science Thomas Woodward recounts the episode:

His curiosity aroused, he entered the store, picked up copies of both books from a table near the door, and studied the dust jacket blurbs. The two biologists were apparently driving toward diametrically opposite conclusions. Sensing a delicious scientific dialectic, he bought both books and tucked them under his arm as he continued on to his office. (Woodward 2003: 69.)

The rest, as they say, is history. Johnson began to read whatever he could find on the issue: Gould, Ruse, Ridley, Dawkins, Denton and many others. What he read made him even more suspicious of evolutionary orthodoxy. “Something about the Darwinists’ rhetorical style,” he told me later, “made me think they had something to hide.”

An extensive examination of evolutionary literature confirmed this suspicion. Darwinist polemic revealed a surprising reliance upon arguments that seemed to assume rather than demonstrate the central claim of neo-Darwinism, namely, that life had evolved via a strictly undirected natural process. Johnson also observed an interesting contrast between biologists' technical papers and their popular defenses of evolutionary theory. He discovered that biologists acknowledged many significant difficulties with both standard and newer evolutionary models when writing in scientific journals. Yet, when defending basic Darwinist commitments (such as the common ancestry of all life and the creative power of the natural selection / mutation mechanism) in popular books or textbooks, Darwinists employed an evasive and moralizing rhetorical style to minimize problems and belittle critics. Johnson began to wonder why, given mounting difficulties, Darwinists remained so confident that all organisms had evolved naturally from simpler forms.

In the book Darwin on Trial, Johnson (1991) argued that evolutionary biologists remain confident about neo-Darwinism, not because empirical evidence generally supports the theory, but instead because their perception of the rules of scientific procedure virtually prevent them from considering any alternative view. Johnson cited, among other things, a communiqué from the National Academy of Sciences (NAS) issued to the Supreme Court during the Louisiana “creation science” trial. The NAS insisted that “the most basic characteristic of science” is a “reliance upon naturalistic explanations.”

While Johnson accepted this convention, called “methodological naturalism,” as an accurate description of how much of science operates, he argued that treating it as a normative rule when seeking to establish that natural processes alone produced life assumes the very point that neo-Darwinists are trying to establish. Johnson reminded readers that Darwinism does not just claim that evolution (in the sense of change over time) has occurred. Instead, it purports to establish that the major innovations in the history of life arose by purely natural mechanisms – that is, without any intelligent direction or design. Thus, Johnson distinguished the various meanings of the term “evolution” (such as change over time or common ancestry) from the central claim of Darwinism, namely, the claim that a purely undirected and unguided process had produced the appearance of design in living organisms. Following Richards Dawkins, the staunch modern defender of Darwinism, Johnson called this latter idea “the Blind Watchmaker thesis” to make clear that Darwinism as a theory is incompatible with the design hypothesis. In any case, he argued, modern Darwinists refuse to consider the possibility of design because they think the rules of science forbid it.

Yet if the design hypothesis must be denied consideration from the outset, and if, as the U.S. National Academy of Sciences also asserted, exclusively negative argumentation against evolutionary theory is “unscientific,” then Johnson (1991: 8) observed that “the rules of argument. [...] make it impossible to question whether what we are being told about evolution is really true.” Defining opposing positions out of existence “may be one way to win an argument,” but, said Johnson, it scarcely suffices to demonstrate the superiority of a protected theory.

When I first met Johnson at the aforementioned Greek restaurant it was not long after he had started his investigation of Darwinism. Nevertheless, we came to an immediate meeting of minds, albeit from different starting points. Johnson saw that, as matter of logic, the convention of methodological naturalism forced scientists into a question-begging affirmation of the proposition that life and humankind had arisen “by a purposeless and natural process that did not have him in mind,” as the neo-Darwinist George Gaylord Simpson (1967: 45) had phrased it. For my part, I had come to question methodological naturalism because it seemed to prevent historical scientists from considering all the possible hypotheses that might explain the evidence – despite a clear methodological desideratum to do otherwise. How could an historical scientist claim that he or she had inferred the best explanation if the causal adequacy of some hypotheses were arbitrarily excluded from consideration? For the method of multiple competing hypotheses to work, hypotheses must be allowed to compete without artificial restrictions on the competition.

In any case, when Darwin on Trial was published in 1991 it created a minor media sensation with magazines and newspapers all over America either reviewing the book or profiling the eccentric Berkeley professor who had dared to take on Darwin. Major science journals including Nature, Science and Scientific American also reviewed Darwin on Trial. The reviews, including one by Stephen J. Gould, were uniformly critical and even hostile. Yet these reviews helped publicize Johnson’s critique and attracted many scientists who shared Johnson’s skepticism about neo-Darwinism. This allowed Johnson to do something that, until that time, hadn’t been done: to bring together dissenting scientists from around the world.

Darwin’s Black Box and Michael Behe

One of those scientists, a tenured biochemist at Lehigh University, Michael Behe, had come to doubt Darwinian evolution in the same way that Johnson had – by reading Denton’s Evolution: A Theory in Crisis. Behe was a Roman Catholic and had been raised to accept Darwinism as the way God chose to create life. Thus, he had no theological objections to Darwinian evolution. For years he had accepted it without questioning. When he finished Denton’s book, he still had no theological objections to evolution, but he did have serious scientific doubts. He soon began to investigate what the evidence from his own field of biochemistry had to say about the plausibility of the neo-Darwinian mechanism. Although he saw no reason to doubt that natural selection could produce relatively minor biological changes, he became extremely skeptical that the Darwinian mechanism could produce the kind of functionally integrated complexity that characterizes the inner workings of the cell. Intelligent design, he concluded, must also have played a role.

As his interest grew, he began teaching a freshman course on the evolution controversy. Later in 1992, he wrote a letter to Science defending Johnson’s new book after it had been panned in the review that appeared there. When Johnson saw the letter in Science, he contacted Behe and eventually invited him to a symposium at Southern Methodist University in Texas, where Johnson debated the Darwinist philosopher of science Michael Ruse. The meeting was significant for two reasons. First, as Behe (2006: 37-47) explained, the scientists skeptical of Darwin who were present at the debate were able to experience what they already believed intellectually – they had strong arguments that could withstand high-level scrutiny from their peers. Second, at SMU, many of the leaders of the intelligent design research community would meet together for the first time in one place. Before, we had each been solitary skeptics, unsure of how to proceed against an entrenched scientific paradigm. Now we understood that we were part of an interdisciplinary intellectual community. After the symposium, Johnson arranged a larger meeting the following year for a core group of dissidents at Pajaro Dunes, California (shown in the film Unlocking the Mystery of Life). There we talked science and strategy and, at Johnson’s prompting, joined an e-mail listserv so that we would remain in contact and hone our ideas. At Pajaro Dunes, “the movement” congealed.

Behe, in particular, used the new listserv to test and refine the various arguments for a book he was working on. Within three years, Darwin’s Black Box appeared with The Free Press, a major New York trade publisher. The book went on to sell a quarter million copies.

In Darwin’s Black Box, Behe pointed out that over the last 30 years, biologists have discovered an exquisite world of nanotechnology within living cells – complex circuits, molecular motors and other miniature machines. For example, bacterial cells are propelled by tiny rotary engines called flagellar motors that rotate at speeds up to 100,000 rpm. These engines look as if they were designed by the Mazda corporation, with many distinct mechanical parts (made of proteins) including rotors, stators, O-rings, bushings, U-joints and drive shafts. (See Figure 2). Behe noted that the flagellar motor depends on the coordinated function of 30 protein parts. Remove one of these necessary proteins and the rotary motor simply doesn't work. The motor is, in Behe's terminology, “irreducibly complex.”

This, he argued, creates a problem for the Darwinian mechanism. Natural selection preserves or “selects” functional advantages. If a random mutation helps an organism survive, it can be preserved and passed on to the next generation. Yet the flagellar motor does not function unless all of its thirty parts are present. Thus, natural selection can “select” or preserve the motor once it has arisen as a functioning whole, but it can't produce the motor in a step-bystep Darwinian fashion.

Natural selection purportedly builds complex systems from simpler structures by preserving a series of intermediate structures, each of which must perform some function. In the case of the flagellar motor, most of the critical intermediate stages – like the 29 or 28-part version of the flagellar motor – perform no function for natural selection to preserve. This leaves the origin of the flagellar motor, and many complex cellular machines, unexplained by the mechanism – natural selection – that Darwin specifically proposed to replace the design hypothesis.

Is there a better explanation? Based upon our uniform experience, we know of only one type of cause that produces irreducibly complex systems – namely, intelligence. Indeed, whenever we encounter such complex systems – whether integrated circuits or internal combustion engines – and we know how they arose, invariably a designing intelligence played a role.

Flagellar Motor
Figure 2

The strength of Behe's argument can be judged in part by the responses of his critics. The neo-Darwinists have had ten years to respond and have so far mustered only vague stories about natural selection building irreducibly complex systems (like the flagellar motor) by “coopting” simpler functional parts from other systems. For example, some of Behe’s critics, such as Kenneth Miller of Brown University, have suggested that the flagellar motor might have arisen from the functional parts of other simpler systems or from simpler subsystems of the motor. He and others have pointed to a tiny molecular syringe called a type III secretory system (or TTSS) – that is sometimes found in bacteria without the other parts of the flagellar motor present – to illustrate this possibility. Since the type III secretory system is made of ten or so proteins that are also found in the thirty-protein motor, and since this tiny pump does perform a function, Professor Miller (2004: 81-97) has intimated13 that the bacterial flagellar motor might have arisen from this smaller pump.

While it’s true that the type III secretory system can function separately from the other parts of the flagellar motor, attempts to explain the origin of the flagellar motor by co-option of the TTSS face at least three key difficulties. First, the other twenty or so proteins in the flagellar motor are unique to it and are not found in any other bacterium. This raises the question: from where were these other protein parts co-opted? Second, as microbiologist Scott Minnich (Minnich and Meyer 2004: 295-304) of the University of Idaho points out, even if all the genes and protein parts were somehow available to make a flagellar motor during the evolution of life, the parts would need to be assembled in a specific temporal sequence similar to the way an automobile is assembled in factory. Yet, in order to choreograph the assembly of the flagellar motor, present-day bacteria need an elaborate system of genetic instructions as well as many other protein machines to regulate the timing of the expression of these assembly instructions. Arguably, this system is itself irreducibly complex. Thus, advocates of cooption tacitly presuppose the need for the very thing that the co-option hypotheses seek to explain: a functionally interdependent system of proteins (and genes). Co-option only explains irreducible complexity by presupposing irreducible complexity. Third, analyses of the gene sequences of the two systems (Saier 2004: 113-115) suggest that the flagellar motor arose first and the pump came later. In other words, if anything, the syringe evolved from the motor, not the motor from the syringe. (See Behe 2006b: 255-272 for Behe’s response to his critics.)

An Institutional Home

In the same year, 1996, that Behe’s book appeared, the Center for Science and Culture was launched as part of the Seattle-based Discovery Institute. The Center began with a research fellowship program to support the research of scientists and scholars such as Michael Behe, Jonathan Wells and David Berlinski who were challenging neo-Darwinism or developing the alternative theory of intelligent design. The Center has now become the institutional hub for an international groups of scientists and scholars who are challenging scientific materialism or developing the theory of intelligent design.

William Dembski and The Design Inference

One of the first Center-supported research projects was completed two years later when mathematician and probability theorist William Dembski (1998) completed a monograph for Cambridge University Press titled The Design Inference. In this book, Dembski argued that rational agents often infer or detect the prior activity of other designing minds by the character of the effects they leave behind. Archaeologists assume, for example, that rational agents produced the inscriptions on the Rosetta Stone. Insurance fraud investigators detect certain “cheating patterns” that suggest intentional manipulation of circumstances rather than natural disasters. Cryptographers distinguish between random signals and those that carry encoded messages. Dembski’s work showed that recognizing the activity of intelligent agents constitutes a common and fully rational mode of inference.

More importantly, Dembski’s work explicated criteria by which rational agents recognize the effects of other rational agents, and distinguish them from the effects of natural causes. He argued that systems or sequences that have the joint properties of “high complexity” (or low probability) and “specification” invariably result from intelligent causes, not chance or physical-chemical laws (see Dembski 1998: 36-66). Dembski noted that complex sequences are those that exhibit an irregular and improbable arrangement that defies expression by a simple rule or algorithm. According to Dembski, a specification, on the other hand, is a match or correspondence between a physical system or sequence and a set of independent functional requirements or constraints. To illustrate these concepts (of complexity and specification), consider the following three sets of symbols:

“inetehnsdysk]idfawqnz,mfdifhsnmcpew,ms.s/a”
“Time and tide waits for no man.”
“ABABABABABABABABABABABABAB”

Both the first and second sequences shown above are complex because both defy reduction to a simple rule. Each represents a highly irregular, aperiodic and improbable sequence of symbols. The third sequence is not complex, but is instead highly ordered and repetitive. Of the two complex sequences, only one exemplifies a set of independent functional requirements – i.e., is specified. English has a number of such functional requirements. For example, to convey meaning in English one must employ existing conventions of vocabulary (associations of symbol sequences with particular objects, concepts or ideas) and existing conventions of syntax and grammar (such as “every sentence requires a subject and a verb”). When arrangements of symbols “match” or utilize existing vocabulary and grammatical conventions (i.e., functional requirements), communication can occur. Such arrangements exhibit “specification.” The second sequence (“Time and tide waits for no man”) clearly exhibits such a match between itself and the preexisting requirements of vocabulary and grammar. It has employed these conventions to express a meaningful idea.

Of the three sequences above only the second (“Time and tide waits for no man”) manifests both the jointly necessary indicators of a designed system. The third sequence lacks complexity, though it does exhibit a simple periodic pattern, a specification of sorts. The first sequence is complex, but not specified as we have seen. Only the second sequence exhibits both complexity and specification. Thus, according to Dembski’s theory, only the second sequence, but not the first and third, implicates an intelligent cause – as indeed our intuition tells us. (See Dembski 1998).

As it turns out, these criteria are equivalent (or “isomorphic”) to the notion of specified complexity or information content. Thus, Dembski’s work suggested that “high information content” or “specified information” or “specified complexity” indicates prior intelligent activity. This theoretical insight comported with common, as well as scientific, experience. Few rational people would, for example, attribute hieroglyphic inscriptions to natural forces such as wind or erosion; instead, they would immediately recognize the activity of intelligent agents. Dembski’s work shows why: Our reasoning involves a comparative evaluation process that he represents with a device he calls “the explanatory filter.” The filter outlines a formal method by which scientists (as well as ordinary people) decide among three different types of explanations: chance, necessity and design. (See Figure 3). His “explanatory filter” constituted, in effect, a scientific method for detecting the effects of intelligence.

Explanatory Filter
Figure 3

Dembski’s academic credentials were impeccable, and since the book had been published after a rigorous peer review process as part of the prestigious Cambridge University Press monograph series, his argument was difficult to ignore. Dembski’s formal method also reinforced the argument that I was making simultaneously, namely, that the specified information in DNA is best explained by reference to an intelligent cause rather than by reference to chance, necessity or a combination of the two (Meyer 1998a; Meyer 1998b; Meyer 2003a; Meyer et al., 2003.) Indeed, the coding regions of the nucleotide base sequences in DNA manifest both complexity and specification just as does the second of the three symbol strings in the preceding illustration.

Design Beyond Biology

Meanwhile, the fledgling Center for Science and Culture was working with scientists and scholars around the world to develop the case for intelligent design not only in biology but also in the physical sciences. Since then, its fellows have written more than sixty books and hundreds of articles (including many peer-reviewed scientific articles challenging Darwinian evolution or, in some cases, explicitly arguing for intelligent design [see Meyer 2004: 213239; see http://www.discovery.org/csc for other peer-reviewed books and articles supporting intelligent design]), and have appeared on hundreds of television and radio broadcasts, many of them national or international. In addition, the center co-produced four science documentaries and helped improve science education policy in seven states and in the U.S. Congress. As a result of these efforts, the work of the center has generated an international discussion about the growing evidence for design in nature.

Since so much of the intelligent design debate concerns biology, many journalists covering the debate – particularly those guided by boilerplate of the 1925 Scopes Monkey Trial and its Hollywood embodiment, Inherit the Wind – fail to mention that the theory of intelligent design is larger than biology. In recent decades, molecular and cell biology have provided powerful evidence of design, but so too have chemistry, astronomy and physics.

Consider, for example, the role that physics has played in reviving the case for intelligent design. Since Fred Hoyle’s prediction and discovery of the resonance levels of Carbon in 1954 (Hoyle 1954: 121-146), physicists have discovered that the existence of life in the universe depends upon a number of precisely balanced physical factors (see Giberson 1997: 63-90; Yates, 1997: 91-104). The constants of physics, the initial conditions of the universe and many other of its contingent features appear delicately balanced to allow for the possibility of life. Even very slight alterations in the values of many independent factors such as the expansion rate of the universe, the speed of light, the precise strength of gravitational or electromagnetic attraction, would render life impossible. Physicists now refer to these factors as “anthropic coincidences” and to the fortunate convergence of all these coincidences as the “fine-tuning of the universe.” Many have noted that this fine-tuning strongly suggests design by a pre-existent intelligence. As physicist Paul Davies (1988: 203) has put it, “The impression of design is overwhelming.”

To see why, consider the following illustration. Imagine a cosmic explorer has just stumbled into the control room for the whole universe. There he discovers an elaborate “universe creating machine,” with rows and rows of dials each with many possible settings. As he investigates, he learns that each dial represents some particular parameter that has to be calibrated with a precise value in order to create a universe in which life can survive. One dial represents the possible settings for the strong nuclear force, one for the gravitational constant, one for Planck’s constant, one for the speed of light, one for the ratio of the neutron mass to the proton mass, one for the strength of electromagnetic attraction and so on. As our cosmic explorer examines the dials, he finds that the dials can be easily spun to different settings – that they could have been set otherwise. Moreover, he determines by careful calculation (he is a physicist) that even slight alterations in any of the dial settings would alter the architecture of the universe such that life would cease to exist. Yet for some reason each dial sits with just the exact value necessary to keep the universe running – like an already-opened bank safe with multiple dials in which every dial is found with just the just the right value. What should one infer about how these dial settings came to be set?

Not surprisingly, many physicists have been asking the same question about the anthropic coincidences. And for many,14 the design hypothesis seems the most obvious and intuitively plausible answer to this question. As George Greenstein (1988: 26-27) muses, “the thought insistently arises that some supernatural agency, or rather Agency, must be involved.” As Fred Hoyle (1982: 16) commented, “a commonsense interpretation of the facts suggests that a superintellect has monkeyed with physics, as well as chemistry and biology, and that there are no blind forces worth speaking about in nature.” Or as he put it in his book The Intelligent Universe, “A component has evidently been missing from cosmological studies. The origin of the Universe, like the solution of the Rubik cube, requires an intelligence” (Hoyle 1983: 189). Many physicists now concur. They would argue that – in effect – the dials in the cosmic control room appear finely-tuned because someone carefully set them that way.

In the 2004 book The Privileged Planet, astronomer Guillermo Gonzalez and philosopher Jay Richards extended this fine-tuning argument to planet earth (Gonzalez and Richards 2004). They showed first that the Earth’s suitability as a habitable planet depends on a host of very improbable conditions – conditions so improbable in fact as to call into question the widespread assumption that habitable planets are common in our galaxy or even the universe. Further, by drawing on a host of recent astronomical discoveries, Gonzalez and Richards also showed that the set of improbable conditions that render the earth habitable also make it an optimal place for observing the cosmos and making various scientific discoveries. As they put it, habitability correlates with discoverability. They argued that the best explanation for this correlation is that the earth was intelligently designed to be a habitable planet and a platform for making scientific discovery. The Privileged Planet makes a nuanced and cumulative argument15 – one that resists easy summation, but their groundbreaking advance of the finetuning argument for design was persuasive enough that such scientists as Cambridge’s Simon Conway Morris and Harvard’s Owen Gingerich endorsed the book, and David Hughes (2005: 113), a vice-president of the Royal Astronomical Society, gave it an enthusiastic review in the pages of The Observatory.

Three Philosophical Objections

On this and other fronts, advocates of the theory of intelligent design have stirred up debate at the highest levels of the scientific community. In response opponents have often responded with philosophical rather than evidential objections. The three of the most common are: (1) that the theory of intelligent design is an argument from ignorance, (2) that it represents the same kind of fallacious argument from analogy that David Hume criticized in the 18th century and (3) that the theory of intelligent design is not “scientific.” Let us examine each of these arguments in turn.

An Argument from Knowledge

Opponents of intelligent design frequently characterize the theory as an argument from ignorance. According to this criticism anyone who makes a design inference from the presence of information or irreducible complexity in the biological world uses our present ignorance of an adequate materialistic cause of these phenomena as the sole basis for inferring an intelligent cause. Since, the objection goes, ‘design advocates can’t imagine a natural process that can produce biological information or irreducibly complex systems, they resort to invoking the mysterious notion of intelligent design.’ In this view, intelligent design functions not as an explanation, but as a placeholder for ignorance.

On the contrary, the arguments for intelligent design described in this essay do not constitute fallacious arguments from ignorance. Arguments from ignorance occur when evidence against a proposition is offered as the sole grounds for accepting another, alternative proposition. The inferences and arguments to design made by contemporary design theorists don’t commit this fallacy. True, the design arguments employed by contemporary advocates of intelligent design do depend in part upon negative assessments of the causal adequacy of competing materialistic hypotheses. And clearly, the lack of an adequate materialistic cause does provide part of the grounds for inferring design from information or irreducibly complex structures in the cell. Nevertheless, this lack is only part of the basis for inferring design. Advocates of the theory of intelligent design also infer design because we know that intelligent agents can and do produce information-rich and irreducibly complex systems. In other words, we have positive experience-based knowledge of an alternative cause that is sufficient to have produced such effects. That cause is intelligence. Thus, design theorists infer design not just because natural processes do not or cannot explain the origin of specified information or irreducible complexity in biological systems, but also because we know based upon our uniform experience that only intelligent agents produce these effects. In other words, biological systems manifest distinctive and positive hallmarks of intelligent design – ones that in any other realm of experience would trigger the recognition of an intelligent cause.

Thus, Michael Behe has inferred design not only because the mechanism of natural selection cannot (in his judgment) produce “irreducibly complex” systems, but also because in our experience “irreducible complexity” is a feature of systems known always to result from intelligent design. That is, whenever we see systems that have the feature of irreducible complexity and we know the causal story about how such systems originated, invariably “intelligent design” played a role in the origin of such systems. Thus, Behe infers intelligent design as the best explanation for the origin of irreducible complexity in cellular molecular motors and circuits based upon what we know, not what we do not know, about the causal powers of intelligent agents and natural processes, respectively.

Similarly, the “specified complexity” or “specified information” of DNA implicates a prior intelligent cause, not only because (as I have argued) materialistic scenarios based upon chance, necessity and the combination of the two fail to explain the origin of such information, but also because we know that intelligent agents can and do produce information of this kind. In other words, we have positive experience-based knowledge of an alternative cause that is sufficient to have produced such effects, namely, intelligence. To quote Henry Quastler again, “Information habitually arises from conscious activity” (Quastler 1964: 16). For this reason, specified information also constitutes a distinctive hallmark (or signature) of intelligence. Indeed, in all cases where we know the causal origin of such information, experience has shown that intelligent design played a causal role. Thus, when we encounter such information in the bio-macromolecules necessary to life, we may infer – based upon our knowledge of established cause-effect relationships (i.e., “presently acting causes”) – that an intelligent cause operated in the past to produce the information necessary to the origin of life.

Thus, contemporary design advocates employ the standard uniformitarian method of reasoning used in all historical sciences. That contemporary arguments for design necessarily include critical evaluations of the causal adequacy of competing hypotheses is entirely appropriate. All historical scientists must compare causal adequacy of competing hypotheses in order to make a judgment as to which hypothesis is best. We would not say, for example, that an archeologist had committed a “scribe of the gaps” fallacy simply because – after rejecting the hypothesis that an ancient hieroglyphic inscription was caused by a sand storm – he went on to conclude that the inscription had been produced by a human scribe. Instead, we recognize that the archeologist has made an inference based upon his experience-based knowledge that information-rich inscriptions invariably arise from intelligent causes, not solely upon his judgment that there are no suitably efficacious natural causes that could explain the inscription.

Not Analogy but Identity

Nor does the design argument from biological information depend on the analogical reasoning that Hume critiqued since it does not depend upon assessments of degree of similarity. The argument does not depend upon the similarity of DNA to a computer program or human language but upon the presence of an identical feature (“information” defined as “complexity and specification”) in both DNA and all other designed systems, languages or artifacts. For this reason, the design argument from biological information does not represent an argument from analogy of the sort that Hume criticized, but an “inference to the best explanation.” Such arguments turn not on assessments of the degree of similarity between effects, but instead on an assessment of the adequacy of competing possible causes for the same effect. Because we know intelligent agents can (and do) produce complex and functionally specified sequences of symbols and arrangements of matter (information so defined), intelligent agency qualifies as a sufficient causal explanation for the origin of this effect. In addition, since naturalistic scenarios have proven universally inadequate for explaining the origin of such information, mind or creative intelligence now stands as the best explanation for the origin of this feature of living systems.

But Is It Science?

Of course, many simply refuse to consider the design hypothesis on grounds that it does not qualify as “scientific.” Such critics (see Ruse 1988: 103) affirm the extra-evidential principle mentioned above known as methodological naturalism or methodological materialism. Methodological naturalism asserts that, as a matter of definition, for a hypothesis, theory or explanation to qualify as “scientific,” it must invoke only materialistic entities. Thus, critics say, the theory of intelligent design does not qualify. Yet, even if one grants this definition, it does not follow that some nonscientific (as defined by methodological naturalism) or metaphysical hypothesis couldn’t constitute a better, more causally adequate, explanation of some phenomena than competing materialistic hypotheses. Design theorists argue that, whatever its classification, the design hypothesis does constitute a better explanation than its materialistic rivals for the origin of biological information, irreducibly complex systems and the fine-tuning of the constants of physics. Surely, simply classifying an argument as “not scientific” does not refute it.

In any case, methodological materialism now lacks justification as a normative definition of science. First, attempts to justify methodological materialism by reference to metaphysically neutral (that is, non-question begging) demarcation criteria have failed (see Meyer 2000b; Meyer 2000c; Laudan 2000a: 337-50; Laudan 2000b: 351-355; Plantinga 1986a: 1826; Plantinga 1986b: 22-34). Second, to assert methodological naturalism as a normative principle for all of science has a negative effect on the practice of certain scientific disciplines, especially those in the historical sciences. In origin-of-life research, for example, methodological materialism artificially restricts inquiry and prevents scientists from considering some hypotheses that might provide the best, most causally adequate explanations. To be a truthseeking endeavor, the question that origin-of-life researchers must address is not “Which materialistic scenario seems most adequate?” but rather “What actually caused life to arise on Earth?” Clearly, it’s at least logically possibly that the answer to the latter question is this: “Life was designed by an intelligent agent that existed before the advent of humans.” If one accepts methodological naturalism as normative, however, scientists may never consider the design hypothesis as possibly true. Such an exclusionary logic diminishes the significance of any claim of theoretical superiority for any remaining hypothesis and raises the possibility that the best “scientific” explanation (as defined by methodological naturalism) may not be the best in fact.

As many historians and philosophers of science now recognize, scientific theory-evaluation is an inherently comparative enterprise. Theories that gain acceptance in artificially constrained competitions can claim to be neither ‘most probably true’ nor ‘most empirically adequate.’ At best, such theories can be considered the ‘most probably true or adequate among an artificially limited set of options.’ Thus, an openness to the design hypothesis would seem necessary to any fully rational historical science – that is, to one that seeks the truth, “no holds barred” (Bridgman 1955: 535). An historical science committed to following the evidence wherever it leads will not exclude hypotheses a priori on metaphysical grounds. Instead, it will employ only metaphysically neutral criteria – such as explanatory power and causal adequacy – to evaluate competing hypotheses. This more open (and seemingly rational) approach to scientific theory evaluation suggests the theory of intelligent design as the best, most causally adequate explanation for the origin of certain features of the natural world, especially including the origin of the specified information necessary to build the first living organism.

Conclusion

Of course, many continue to dismiss intelligent design as nothing but “religion masquerading as science.” They point to the theory’s obviously friendly implications for theistic belief as a justification for classifying and dismissing the theory as “religion.” But such critics confuse the implications of the theory of intelligent design with its evidential basis. The theory of intelligent design may well have theistic implications. But that is not grounds for dismissing it. Scientific theories must be judged by their ability to explain evidence, not by whether they have undesirable implications. Those who say otherwise flout logic and overlook the clear testimony of the history of science. For example, many scientists initially rejected the Big Bang theory because it seemed to challenge the idea of an eternally self-existent universe and pointed to the need for a transcendent cause of matter, space and time. But scientists eventually accepted the theory despite such apparently unpleasant implications because the evidence strongly supported it. Today a similar metaphysical prejudice confronts the theory of intelligent design. Nevertheless, it too must be evaluated on the basis of the evidence, not our philosophical preferences or concerns about its possible religious implications. As Professor Flew, the long-time atheistic philosopher who has come to accept the case for design, advises: we must “follow the evidence wherever it leads.”

Acknowledgement: The author would like to acknowledge the assistance of Dr. Jonathan Witt in the preparation of parts of this article.


Endnotes

  1. Aquinas used the argument from design as one of his proofs for the existence of God.
  2. Kepler’s belief that the work of God is evident in nature is illustrated by his statement in the Harmonies of the World that God “the light of nature promote[s] in us the desire for the light of grace, that by its means [God] ma[y] transport us into the light of glory” (Kepler 1995: 240. See also Kline 1980: 39).
  3. Kant sought to limit the scope of the design argument, but did not reject it wholesale. Though he rejected the argument as a proof of the transcendent and omnipotent God of Judeo-Christian theology, he still accepted that it could establish the reality of a powerful and intelligent author of the world. In his words, “physical-theological argument can indeed lead us to the point of admiring the greatness, wisdom, power, etc., of the Author of the world, but can take us no further” (Kant 1963: 523).
  4. The effort to explain biological organisms was reinforced by a trend in science to provide fully naturalistic accounts for other phenomena such as the precise configuration of the planets in the solar system (Laplace) and the origin of geological features (Lyell and Hutton). It was also reinforced (and in large part made possible) by an emerging positivistic tradition in science that increasingly sought to exclude appeals to supernatural or intelligent causes from science by definition (see Gillespie 1987: 1-49).
  5. “[T]he fact of evolution was not generally accepted until a theory had been put forward to suggest how evolution had occurred, and in particular how organisms could become adapted to their environment; in the absence of such a theory, adaptation suggested design, and so implied a creator. It was this need which Darwin's theory of natural selection satisfied” (Smith, 1975: 30).
  6. “There is absolutely no disagreement among professional biologists on the fact that evolution has occurred. [...] But the theory of how evolution occurs is quite another matter, and is the subject of intense dispute” (Futuyma 1985: 3-13). Of course, to admit that natural selection cannot explain the appearance of design is in effect to admit that it has failed to perform the role that is claimed for it as a “designer substitute.”
  7. Note that similar developments were already taking place in Germany, starting with W.-E. Lönnig’s Auge – widerlegt Zufalls-Evolution [=The Eye Disproves Accidental Evolution] (Stuttgart: Selbstverlag, 1976) and Henning Kahle's book, Evolution – Irrweg moderner Wissenschaft? [=Evolution – Error of Modern Science?] (Bielefeld: Moderner Buch Service, 1980).
  8. Commenting on events at this symposium, mathematician David Berlinski writes, “However it may operate in life, randomness in language is the enemy of order, a way of annihilating meaning. And not only in language, but in any language-like system—computer programs, for example. The alien influence of randomness in such systems was first noted by the distinguished French mathematician M. P. Schützenberger, who also marked the significance of this circumstance for evolutionary theory.
  9. For instance, it also received praise in the Journal of College Science Teaching and in a major review essay by Klaus Dose, “The Origin of Life: More Questions than Answers,” Interdisciplinary Science Reviews, 13.4, 1988.
  10. Gian Capretti (1983: 143) has developed the implications of Peircian abduction. Capretti and others explore the use of abductive reasoning by Sherlock Holmes in detective fiction of Sir Arthur Conan Doyle. Capretti attributes the success of Holmesian abductive “reconstructions” to a willingness to employ a method of “progressively eliminating hypotheses.”
  11. Moreover, information increases as improbabilities multiply. The probability of getting four heads in a row when flipping a fair coin is 1/2 X 1/2 X 1/2 X 1/2 or (1/2)4. Thus, the probability of attaining a specific sequence of heads and/or tails decreases exponentially as the number of trials increases. The quantity of information increases correspondingly. Even so, information theorists found it convenient to measure information additively rather than multiplicatively. Thus, the common mathematical expression (I = –log2p) for calculating information converts probability values into informational measures through a negative logarithmic function, where the negative sign expresses an inverse relationship between information and probability.
  12. I later extended this information argument to an analysis of the geologically-sudden appearance of animal body plans that occurred in the Cambrian period. In a peer-reviewed article published in 2004 with the Proceedings of the Biological Society of Washington, a journal published out of the Smithsonian Institution, I argued that intelligent design provided the best explanation of the quantum increase in biological information that was necessary to build the Cambrian animals. In constructing this case, I again self-consciously followed the method of multiple competing hypotheses by showing that neither neoDarwinian mechanism, nor structuralism, nor self-organizational models nor other materialistic models offered an adequate causal explanation for the origin of the Cambrian explosion in biological form and information (see Meyer 2004: 213-239; Meyer et al. 2003). Instead, I argued that, based upon our uniform and repeated experience, only intelligent agency (mind, not a material process) has demonstrated the power to produce the large amounts of specified information such as that which arose with the Cambrian animals.
  13. Kenneth Miller carefully avoids saying that the bacterial flagellar motor actually did evolve from the type III secretory system. Instead, he insists that the TTSS simply refutes Behe’s claim that the flagellar motor is irreducibly complex. But as Behe has made clear his definition of “irreducible complexity” (IC) does not entail the claim that the parts of an irreducibly complex system perform no other function, only that the loss of parts from an irreducibly complex system destroys the function of that system. Systems that are IC even by this less restrictive definition still pose formidable obstacles to co-option scenarios, even granting that some of their parts may have had some other selectable function in the past. For co-option scenarios to be plausible, natural selection must build complex systems from simpler structures by preserving a series of intermediate structures, each of which must perform some function. For this reason, it is not enough for advocates of co-option to point to a single possible ancestral structure, but instead they must show that a plausible series of such structures existed and could have maintained function at each stage. In the case of the flagellar motor, co-option scenarios lack such plausibility in part because experimental research has shown that the presumptively precedent stages to a fully functional flagellar motor (for example, the 29, 28 and 27—part versions of the flagellar motor) have no motor function. If the last stages in a hypothetical series of functional intermediates are not functional, then it follows that the series as a whole is not. For this and other reasons, co-option does not presently provide either an adequate explanation of the origin of the flagellar motor or a better explanation than Behe’s design hypothesis.
  14. Greenstein himself does not favor the design hypothesis. Instead, he favors the so-called “participatory universe principle” or “PAP.” PAP attributes the apparent design of the fine tuning of the physical constants to the universe’s (alleged) need to be observed in order to exist. As he says, the universe “brought forth life in order to exist [...] that the very Cosmos does not exist unless observed.” See Greenstein 1988: 223.
  15. In arguing that our place in the cosmos is optimized for life and discovery, they introduce a concept from engineering, constrained optimization, offering the example of a notebook computer. Yes, a notebook computer’s screen could be substantially bigger, but that would compromise its effectiveness as a lightweight, portable computer. The best notebook computer is the best compromise among a range of sometimes competing qualities. In the same way, Earth’s situation in the cosmos might be improved in this or that way, but these improvements would involve tradeoffs. For instance, if we were near the center of our galaxy, we might be able to learn more about the black hole posited to rest there, but the bright galactic core would greatly compromise our ability to observe distant galaxies. Our actual viewing position, while perhaps not ideal in any one respect, possesses the same quality of constrained optimization that a well-designed notebook computer possesses.

References

  • Alston, W. P. (1971): The place of the explanation of particular facts in science, in: Philosophy of science 38, 13-34.
  • Axe, D. (2004): Estimating the prevalence of protein sequences adopting functional enzyme folds, in: Journal of Molecular Biology, 341, 1295-1315.
  • Behe, M. (2004): Irreducible complexity: Obstacle to Darwinian evolution, in: W. A. Dembski/M. Ruse (eds.), Debating design: from Darwin to DNA, Cambridge, 352-370.
  • (2006a): From muttering to mayhem: How Phillip Johnson got me moving, in: W. A. Dembski (ed.), Darwin’s nemesis: Phillip Johnson and the intelligent design movement, Downers Grove, IL, 37-47.
  • – (2006b): Darwin’s black box: The biochemical challenge to evolution. Afterword, New York, 255-272.
  • Berlinski, D. (1996): The deniable Darwin, in: Commentary 101.6, 19-29.
  • Bowler, P. J. (1986): Theories of human evolution: A century of debate, 1844-1944, Baltimore, 44-50.
  • Boyle, R. (1979): Selected philosophical papers of Robert Boyle, edited by M. A. Stewart, Manchester, 172.
  • Bradley, W. (2004): Information, entropy and the origin of life, in: W. A. Dembski / M. Ruse (eds.), Debating design: from Darwin to DNA, Cambridge, 331-351.
  • Bridgman, P. W. (1955): Reflections of a physicist, 2nd edition, New York, 535.
  • Capretti, G. (1983): Peirce, Holmes, Popper, in: U. Eco and T. Sebeok (eds.), The sign of three, Bloom
  • ington, IN, 135-153.
  • Chamberlain, T. C. (1965): The method of multiple working hypotheses, in: Science 148, 754-59.
  • Cicero (1933): De natura deorum, translated by Harris Rackham, Cambridge, MA, 217.
  • Crick, F. (1958): On Protein Synthesis, in: Symposium for the Society of Experimental Biology, 12,138– 63, esp. 138-63.
  • Darwin, C. (1896): Life and letters of Charles Darwin, 2 volumes, edited by Francis Darwin, London, vol. 1, 437.
    • – (1964): On the origin of species, Cambridge, MA, 481-82.
  • Dawkins, R. (1986): The blind watchmaker, London, 1.
    • – (1995): River out of Eden, New York, 11.
  • Davies, P. (1988): The cosmic blueprint, New York, 203.
  • Dembski, W. A. (1996): Demise of British natural theology. Unpublished paper presented to Philosophy of Religion seminar, University of Notre Dame, fall.
    • – (1998): The design inference: Eliminating chance through small probabilities. Cambridge.
    • – (2002): No free lunch: why specified complexity cannot be purchased without intelligence. Lanham, Maryland.
    • – (2004): The logical underpinnings of intelligent design, in: W. A. Dembski / M. Ruse (eds.), Debating design: from Darwin to DNA, Cambridge, 311-440.
  • Denton, M. (1985): Evolution: a theory in crisis, London.
    • – (1986): Nature’s destiny, New York.
  • Dretske, F. (1981): Knowledge and the flow of information, Cambridge, MA, 6-10.
  • Eden, M. (1967): Inadequacies of neo-Darwinian evolution as a scientific theory, in: P. S. Moorhead / M.M. Kaplan (eds.), Mathematical challenges to the neo-Darwinian interpretation of evolution, Philadelphia, 109-111.
  • Eldredge, N. (1982): An ode to adaptive transformation, in: Nature 296, 508-9.
  • Futuyama, D. (1985): Evolution as fact and theory, in: Bios 56, 3-13.
  • Gallie, W. B. (1959): Explanations in history and the genetic sciences, in: P. Gardiner (ed.), Theories of history: Readings from classical and contemporary sources, Glencoe, IL, 386-402.
  • Gates, B. (1995): The road ahead, New York, 188.
  • Giberson, K. (1997): The anthropic principle, in: Journal of interdisciplinary studies 9, 63-90.
  • Gillespie, N. (1979): Charles Darwin and the problem of creation, Chicago, 41-66, 82-108.
    • – (1987): Natural history, natural theology, and social order: John Ray and the “Newtonian Ideology”, in: Journal of the History of Biology 20, 1-49.
  • Gonzalez, G. and Richards, J. W. (2004): The privileged planet: How our place in the cosmos was designed for discovery. Washington, D.C.
  • Gould, S. J. (1986): Evolution and the triumph of homology: Or, why history matters, in: American scientist 74, 61.
    • – (2003): Is a new and general theory of evolution emerging? In: Paleobiology 119, 119-20.
  • Greenstein, G. (1988): The symbiotic universe: Life and mind in the cosmos, New York, 26-27; 223.
  • Hick, J. (1970): Arguments for the existence of God, London, 1.
  • Hoyle, F. (1954): On nuclear reactions occurring in very hot stars. I. The synthesis of elements from carbon to nickel, in: Astrophysical journal supplement 1, 121-146.
    • – (1982): The universe: Past and present reflections, in: Annual Review of Astronomy and Astrophysics 20, 16.
    • – (1983): The intelligent universe, New York, 189.
  • Hughes, D. (2005): The observatory, 125.1185, 113.
  • Judson, H. (1979): Eighth day of creation, New York.
  • Johnson, P. E. (1991): Darwin on trial, Washington, D.C., 8.
  • Kamminga, H. (1986): Protoplasm and the Gene, in: A. G. Cairns-Smith / H. Hartman (eds.), Clay Minerals and the Origin of Life, Cambridge, 1-10.
  • Kant, I. (1963): Critique of pure reason, translated by Norman Kemp Smith, London, 523.
  • Kenyon, D. (1984): Foreword to The mystery of life’s origin, New York, v-viii.
  • Kenyon, D. / Gordon, M. (1996): The RNA world: A critique, in: Origins & Design 17 (1), 9-16.
  • Kepler, J. (1981): Mysterium cosmographicum [The secret of the universe], translated by A. M. Duncan, New York, 93-103.
  • Kepler, J. (1995): Harmonies of the world, translated by Charles Glen Wallis, Amherst, NY, 170, 240.
  • Kline, M. (1980): Mathematics: The loss of certainty, New York, 39.
  • Klinghoffer, D. (2005): The Branding of a Heretic, in: The Wall Street Journal, 28 January, W11.
  • Küppers, B.-O. (1987): On the Prior Probability of the Existence of Life, in: L. Krüger et al. (eds.), The Probabilistic revolution, Cambridge, MA, 355–69.
    • (1990): Information and the origin of life, Cambridge, MA, 170-172.
  • Laudan, L. (2000a): The demise of the demarcation problem, in: M. Ruse (ed.), But is it science?, Amherst, NY, 337-350.
    • (2000b): Science at the bar – causes for concern, in: M. Ruse (ed.), But is it science?, Amherst, NY, 351-355.
  • Lönnig, W.-E. (2001): Natural selection, in: W. E. Craighead / C. B. Nemeroff (eds.), The Corsini encyclopedia of psychology and behavioral sciences, 3rd edition, New York, vol. 3, 1008-1016.
  • Lönnig, W.-E. / Saedler, H. (2002): Chromosome rearrangements and transposable elements, in: Annual review of genetics 36, 389-410.
  • Mayr, E. (1982): Foreword to Darwinism defended, by Michael Ruse, Reading, MA, xi-xii.
  • Meyer, S. C. (1998): DNA by design: An inference to the best explanation for the origin of biological information, in: Journal of rhetoric and public affairs 4.1, 519-556.
    • – (1998b): The Explanatory power of design: DNA and the origin of information, in: W. A. Dembski (ed.), Mere creation: science, faith and intelligent design, Downers Grove, IL, 114-147.
    • – (2000a): DNA & other designs, in: First things 102 (April 2000), 30-38.
    • – (2000b): The scientific status of intelligent design: The methodological equivalence of naturalistic and non-naturalistic origins theories, in: M. J. Behe / W. A. Dembski / S. C. Meyer (eds.), Science and evidence for design in the universe, San Francisco, 151-211.
    • – (2000c): The demarcation of science and religion, in: G. B. Ferngren et al. (eds.), The history of science and religion in the western tradition, New York, 12-23.
    • – (2003a): DNA and the origin of life: information, specification and explanation, in: J. A. Campbell / S. C. Meyer (eds.), Darwinism, design and public education, Lansing, MI, 223-285.
    • – (2004): The Cambrian information explosion: evidence for intelligent design, in: W. A. Dembski / M. Ruse (eds.), Debating design, Cambridge, 371-391.
    • – (2004): The origin of biological information and the higher taxonomic categories, in: Proceedings of the Biological Society of Washington 117, 213-239.
  • Meyer, S. C. / Ross, M. / Nelson, P. / Chien, P. (2003): The Cambrian explosion: Biology’s big bang, in: J. A. Campbell / S. C. Meyer (eds.), Darwinism, design and public education, Lansing, MI, 323-402.
  • Miller, K. (2004): The bacterial flagellum unspun, in: W. A. Dembski / M. Ruse (eds.), Debating design: from Darwin to DNA, Cambridge, 81-97.
  • Minnich, S. A. / Meyer, S. C. (2004): Genetic analysis of coordinate flagellar and type III regulatory circuits in pathogenic bacteria, in: M. W. Collins / C. A. Brebbia (eds.), Design and nature II: Comparing design in nature with science and engineering, Southampton, 295-304.
  • Moorhead, P. S. / Kaplan, M. M. (eds.) (1967): Mathematical challenges to the neo-Darwinian interpretation of evolution, Philadelphia.
  • Morris, S. C. (1998): The crucible of creation: The Burgess Shale and the rise of animals, Oxford, 63115.
    • (2000): Evolution: bringing molecules into the fold, in: Cell 100, 1-11.
    • – (2003a): The Cambrian “explosion” of metazoans, in: Origination of organismal form, 13-32.
    • (2003b): Cambrian “explosions” of metazoans and molecular biology: would Darwin be satisfied?, in: International journal of developmental biology 47 (7-8), 505-515.
  • Müller, G. B. / Newman, S. A. (2003): Origination of organismal form: The forgotten cause in evolutionary theory, in: G. B. Müller / S. A. Newman (eds.), Origination of organismal form: Beyond the gene in developmental and evolutionary biology, Cambridge, MA, 3-12.
  • Nelson, P. / Wells, J. (2003): Homology in biology: problem for naturalistic science and prospect for intelligent design, in: J. A. Campbell / S. C. Meyer (eds.), Darwinism, design and public education, Lansing, MI, 303-322.
  • Newton, I. (1934): Newton’s Principia: Motte’s translation revised (1686), translated by A. Motte, revised by F. Cajori, Berkeley, 543-44.
    • (1952): Opticks, New York, 369-70.
  • Paine, T. (1925): The life and works of Thomas Paine, vol. 8: The age of reason, New Rochelle, NY, 6.
  • Paley, W. (1852): Natural theology, Boston, 8-9.
  • Peirce, C. S. (1932): Collected papers, Vols. 1-6, edited by C. Hartshorne and P. Weiss, Cambridge, MA, vol. 2, 375.
  • Plantinga, A. (1986a): Methodological naturalism?, in: Origins and design 18.1, 18-26.
    • – (1986b): Methodological naturalism?, in: Origins and design 18.2, 22-34.
  • Plato (1960): The laws, translated by A. E. Taylor, London, 279.
  • Polanyi, M. (1967): Life transcending physics and chemistry, in: Chemical and engineering news 45(35), 21.
    • – (1968): Life’s irreducible structure, in: Science 160, 1308-12.
  • Ray, J. (1701): The wisdom of God manifested in the works of the creation, 3rd edition, London.
  • Quastler, H. (1964): The emergence of biological organization, 16. New Haven, Connecticut.
  • Reid, T. (1981): Lectures on natural theology (1780), edited by E. Duncan and W. R. Eakin, Washington, D.C., 59.
  • Ruse, M. (1988): McLean v. Arkansas: Witness testimony sheet, in: M. Ruse (ed.), But is it science?, Amherst, NY, 103.
  • Saier, M. H. (2004): Evolution of bacterial type III protein secretion systems, in: Trends in microbiology 12, 113-115.
  • Shannon, C. E. (1948): A Mathematical theory of communication, in: Bell System Technical Journal, 27, 379–423; 623–56.
  • Shannon, C. E. / Weaver, W. (1949): The Mathematical theory of communication. Urbana, IL.
  • Schiller, F. C. S. (1903): Darwinism and design argument, in: Humanism: Philosophical essays, New York, 141.
  • Schneider, T. D. (1997): Information content of individual genetic sequences, in: Journal of Theoretical Biology, 189, 427–41.
  • Schützenberger, M. (1967): Algorithms and neo-Darwinian theory, in: P. S. Moorhead / M. M. Kaplan (eds.), Mathematical challenges to the neo-Darwinian interpretation of evolution, Philadelphia, 73-5.
  • Scriven, M. (1959): Explanation and prediction in evolutionary theory, in: Science 130, 477-82.
    • (1966): Causes, connections and conditions in history, in: W. H. Dray (ed.), Philosophical analysis and history, New York, 238-64.
  • Simpson, G. G. (1978): The meaning of evolution, Cambridge, MA, 45.
  • Smith, J. M. (1975): The theory of evolution, 3rd edition, London, 30.
  • Sober, E. (1988): Reconstructing the past: parsimony, evolution, and inference, Cambridge, MA, 1-5.
  • Taylor, G. R. (1983): The great evolution mystery, New York, 4.
  • Thaxton, C. / Bradley, W. / Olsen, R. L. (1984): The mystery of life’s origin, New York.
  • Wallace, A. R. (1991): Sir Charles Lyell on geological climates the origin of species, in: C. H. Smith (ed.), An anthology of his shorter writings, Oxford, 33-34.
  • Whewell, W. (1840): The philosophy of the inductive sciences, 2 vols., London, vol. 2, 121-22; 101-03.
    • – (1857): History of the inductive sciences, 3 vols., London, vol. 3, 397.
  • Witham, L. (2003): By design, San Francisco, chapter 2.
  • Woodward, T. (2003): Doubts about Darwin: A history of intelligent design, Grand Rapids, Michigan, 69.
  • Yates, S. (1997): Postmodern creation myth? A response, in: Journal of interdisciplinary studies 9, 91104.
  • Yockey, H. P. (1992): Information theory and molecular biology, Cambridge.

On August 4th, 2004 an extensive review essay by Dr. Stephen C. Meyer, Director of Discovery Institute's Center for Science & Culture appeared in the Proceedings of the Biological Society of Washington (volume 117, no. 2, pp. 213-239). The Proceedings is a peer-reviewed biology journal published at the National Museum of Natural History at the Smithsonian Institution in Washington D.C.

In the article, entitled "The Origin of Biological Information and the Higher Taxonomic Categories", Dr. Meyer argues that no current materialistic theory of evolution can account for the origin of the information necessary to build novel animal forms. He proposes intelligent design as an alternative explanation for the origin of biological information and the higher taxa.

Due to an unusual number of inquiries about the article, Dr. Meyer, the copyright holder, has decided to make the article available now in HTML format on this website. (Off prints are also available from Discovery Institute by writing to Rob Crowther at: cscinfo@discovery.org. Please provide your mailing address and we will dispatch a copy).

Introduction

In a recent volume of the Vienna Series in a Theoretical Biology (2003), Gerd B. Muller and Stuart Newman argue that what they call the "origination of organismal form" remains an unsolved problem. In making this claim, Muller and Newman (2003:3-10) distinguish two distinct issues, namely, (1) the causes of form generation in the individual organism during embryological development and (2) the causes responsible for the production of novel organismal forms in the first place during the history of life. To distinguish the latter case (phylogeny) from the former (ontogeny), Muller and Newman use the term "origination" to designate the causal processes by which biological form first arose during the evolution of life. They insist that "the molecular mechanisms that bring about biological form in modern day embryos should not be confused" with the causes responsible for the origin (or "origination") of novel biological forms during the history of life (p.3). They further argue that we know more about the causes of ontogenesis, due to advances in molecular biology, molecular genetics and developmental biology, than we do about the causes of phylogenesis--the ultimate origination of new biological forms during the remote past.

In making this claim, Muller and Newman are careful to affirm that evolutionary biology has succeeded in explaining how preexisting forms diversify under the twin influences of natural selection and variation of genetic traits. Sophisticated mathematically-based models of population genetics have proven adequate for mapping and understanding quantitative variability and populational changes in organisms. Yet Muller and Newman insist that population genetics, and thus evolutionary biology, has not identified a specifically causal explanation for the origin of true morphological novelty during the history of life. Central to their concern is what they see as the inadequacy of the variation of genetic traits as a source of new form and structure. They note, following Darwin himself, that the sources of new form and structure must precede the action of natural selection (2003:3)--that selection must act on what already exists. Yet, in their view, the "genocentricity" and "incrementalism" of the neo-Darwinian mechanism has meant that an adequate source of new form and structure has yet to be identified by theoretical biologists. Instead, Muller and Newman see the need to identify epigenetic sources of morphological innovation during the evolution of life. In the meantime, however, they insist neo-Darwinism lacks any "theory of the generative" (p. 7).

As it happens, Muller and Newman are not alone in this judgment. In the last decade or so a host of scientific essays and books have questioned the efficacy of selection and mutation as a mechanism for generating morphological novelty, as even a brief literature survey will establish. Thomson (1992:107) expressed doubt that large-scale morphological changes could accumulate via minor phenotypic changes at the population genetic level. Miklos (1993:29) argued that neo-Darwinism fails to provide a mechanism that can produce large-scale innovations in form and complexity. Gilbert et al. (1996) attempted to develop a new theory of evolutionary mechanisms to supplement classical neo-Darwinism, which, they argued, could not adequately explain macroevolution. As they put it in a memorable summary of the situation: "starting in the 1970s, many biologists began questioning its (neo-Darwinism's) adequacy in explaining evolution. Genetics might be adequate for explaining microevolution, but microevolutionary changes in gene frequency were not seen as able to turn a reptile into a mammal or to convert a fish into an amphibian. Microevolution looks at adaptations that concern the survival of the fittest, not the arrival of the fittest. As Goodwin (1995) points out, 'the origin of species--Darwin's problem--remains unsolved'" (p. 361). Though Gilbert et al. (1996) attempted to solve the problem of the origin of form by proposing a greater role for developmental genetics within an otherwise neo-Darwinian framework,1 numerous recent authors have continued to raise questions about the adequacy of that framework itself or about the problem of the origination of form generally (Webster & Goodwin 1996; Shubin & Marshall 2000; Erwin 2000; Conway Morris 2000, 2003b; Carroll 2000; Wagner 2001; Becker & Lonnig 2001; Stadler et al. 2001; Lonnig & Saedler 2002; Wagner & Stadler 2003; Valentine 2004:189-194).

What lies behind this skepticism? Is it warranted? Is a new and specifically causal theory needed to explain the origination of biological form?

This review will address these questions. It will do so by analyzing the problem of the origination of organismal form (and the corresponding emergence of higher taxa) from a particular theoretical standpoint. Specifically, it will treat the problem of the origination of the higher taxonomic groups as a manifestation of a deeper problem, namely, the problem of the origin of the information (whether genetic or epigenetic) that, as it will be argued, is necessary to generate morphological novelty.

In order to perform this analysis, and to make it relevant and tractable to systematists and paleontologists, this paper will examine a paradigmatic example of the origin of biological form and information during the history of life: the Cambrian explosion. During the Cambrian, many novel animal forms and body plans (representing new phyla, subphyla and classes) arose in a geologically brief period of time. The following information-based analysis of the Cambrian explosion will support the claim of recent authors such as Muller and Newman that the mechanism of selection and genetic mutation does not constitute an adequate causal explanation of the origination of biological form in the higher taxonomic groups. It will also suggest the need to explore other possible causal factors for the origin of form and information during the evolution of life and will examine some other possibilities that have been proposed.

The Cambrian Explosion

The "Cambrian explosion" refers to the geologically sudden appearance of many new animal body plans about 530 million years ago. At this time, at least nineteen, and perhaps as many as thirty-five phyla of forty total (Meyer et al. 2003), made their first appearance on earth within a narrow five- to ten-million-year window of geologic time (Bowring et al. 1993, 1998a:1, 1998b:40; Kerr 1993; Monastersky 1993; Aris-Brosou & Yang 2003). Many new subphyla, between 32 and 48 of 56 total (Meyer et al. 2003), and classes of animals also arose at this time with representatives of these new higher taxa manifesting significant morphological innovations. The Cambrian explosion thus marked a major episode of morphogenesis in which many new and disparate organismal forms arose in a geologically brief period of time.

To say that the fauna of the Cambrian period appeared in a geologically sudden manner also implies the absence of clear transitional intermediate forms connecting Cambrian animals with simpler pre-Cambrian forms. And, indeed, in almost all cases, the Cambrian animals have no clear morphological antecedents in earlier Vendian or Precambrian fauna (Miklos 1993, Erwin et al. 1997:132, Steiner & Reitner 2001, Conway Morris 2003b:510, Valentine et al. 2003:519-520). Further, several recent discoveries and analyses suggest that these morphological gaps may not be merely an artifact of incomplete sampling of the fossil record (Foote 1997, Foote et al. 1999, Benton & Ayala 2003, Meyer et al. 2003), suggesting that the fossil record is at least approximately reliable (Conway Morris 2003b:505).

As a result, debate now exists about the extent to which this pattern of evidence comports with a strictly monophyletic view of evolution (Conway Morris 1998a, 2003a, 2003b:510; Willmer 1990, 2003). Further, among those who accept a monophyletic view of the history of life, debate exists about whether to privilege fossil or molecular data and analyses. Those who think the fossil data provide a more reliable picture of the origin of the Metazoan tend to think these animals arose relatively quickly--that the Cambrian explosion had a "short fuse." (Conway Morris 2003b:505-506, Valentine & Jablonski 2003). Some (Wray et al. 1996), but not all (Ayala et al. 1998), who think that molecular phylogenies establish reliable divergence times from pre-Cambrian ancestors think that the Cambrian animals evolved over a very long period of time--that the Cambrian explosion had a "long fuse." This review will not address these questions of historical pattern. Instead, it will analyze whether the neo-Darwinian process of mutation and selection, or other processes of evolutionary change, can generate the form and information necessary to produce the animals that arise in the Cambrian. This analysis will, for the most part, 2 therefore, not depend upon assumptions of either a long or short fuse for the Cambrian explosion, or upon a monophyletic or polyphyletic view of the early history of life.

Defining Biological Form and Information

Form, like life itself, is easy to recognize but often hard to define precisely. Yet, a reasonable working definition of form will suffice for our present purposes. Form can be defined as the four-dimensional topological relations of anatomical parts. This means that one can understand form as a unified arrangement of body parts or material components in a distinct shape or pattern (topology)--one that exists in three spatial dimensions and which arises in time during ontogeny.

Insofar as any particular biological form constitutes something like a distinct arrangement of constituent body parts, form can be seen as arising from constraints that limit the possible arrangements of matter. Specifically, organismal form arises (both in phylogeny and ontogeny) as possible arrangements of material parts are constrained to establish a specific or particular arrangement with an identifiable three dimensional topography--one that we would recognize as a particular protein, cell type, organ, body plan or organism. A particular "form," therefore, represents a highly specific and constrained arrangement of material components (among a much larger set of possible arrangements).

Understanding form in this way suggests a connection to the notion of information in its most theoretically general sense. When Shannon (1948) first developed a mathematical theory of information he equated the amount of information transmitted with the amount of uncertainty reduced or eliminated in a series of symbols or characters. Information, in Shannon's theory, is thus imparted as some options are excluded and others are actualized. The greater the number of options excluded, the greater the amount of information conveyed. Further, constraining a set of possible material arrangements by whatever process or means involves excluding some options and actualizing others. Thus, to constrain a set of possible material states is to generate information in Shannon's sense. It follows that the constraints that produce biological form also imparted information. Or conversely, one might say that producing organismal form by definition requires the generation of information.

In classical Shannon information theory, the amount of information in a system is also inversely related to the probability of the arrangement of constituents in a system or the characters along a communication channel (Shannon 1948). The more improbable (or complex) the arrangement, the more Shannon information, or information-carrying capacity, a string or system possesses.

Since the 1960s, mathematical biologists have realized that Shannon's theory could be applied to the analysis of DNA and proteins to measure the information-carrying capacity of these macromolecules. Since DNA contains the assembly instructions for building proteins, the information-processing system in the cell represents a kind of communication channel (Yockey 1992:110). Further, DNA conveys information via specifically arranged sequences of nucleotide bases. Since each of the four bases has a roughly equal chance of occurring at each site along the spine of the DNA molecule, biologists can calculate the probability, and thus the information-carrying capacity, of any particular sequence n bases long.

The ease with which information theory applies to molecular biology has created confusion about the type of information that DNA and proteins possess. Sequences of nucleotide bases in DNA, or amino acids in a protein, are highly improbable and thus have large information-carrying capacities. But, like meaningful sentences or lines of computer code, genes and proteins are also specified with respect to function. Just as the meaning of a sentence depends upon the specific arrangement of the letters in a sentence, so too does the function of a gene sequence depend upon the specific arrangement of the nucleotide bases in a gene. Thus, molecular biologists beginning with Crick equated information not only with complexity but also with "specificity," where "specificity" or "specified" has meant "necessary to function" (Crick 1958:144, 153; Sarkar, 1996:191).3 Molecular biologists such as Monod and Crick understood biological information--the information stored in DNA and proteins--as something more than mere complexity (or improbability). Their notion of information associated both biochemical contingency and combinatorial complexity with DNA sequences (allowing DNA's carrying capacity to be calculated), but it also affirmed that sequences of nucleotides and amino acids in functioning macromolecules possessed a high degree of specificity relative to the maintenance of cellular function.

The ease with which information theory applies to molecular biology has also created confusion about the location of information in organisms. Perhaps because the information carrying capacity of the gene could be so easily measured, it has been easy to treat DNA, RNA and proteins as the sole repositories of biological information. Neo-Darwinists in particular have assumed that the origination of biological form could be explained by recourse to processes of genetic variation and mutation alone (Levinton 1988:485). Yet if one understands organismal form as resulting from constraints on the possible arrangements of matter at many levels in the biological hierarchy--from genes and proteins to cell types and tissues to organs and body plans--then clearly biological organisms exhibit many levels of information-rich structure.

Thus, we can pose a question, not only about the origin of genetic information, but also about the origin of the information necessary to generate form and structure at levels higher than that present in individual proteins. We must also ask about the origin of the "specified complexity," as opposed to mere complexity, that characterizes the new genes, proteins, cell types and body plans that arose in the Cambrian explosion. Dembski (2002) has used the term "complex specified information" (CSI) as a synonym for "specified complexity" to help distinguish functional biological information from mere Shannon information--that is, specified complexity from mere complexity. This review will use this term as well.

The Cambrian Information Explosion

The Cambrian explosion represents a remarkable jump in the specified complexity or "complex specified information" (CSI) of the biological world. For over three billions years, the biological realm included little more than bacteria and algae (Brocks et al. 1999). Then, beginning about 570-565 million years ago (mya), the first complex multicellular organisms appeared in the rock strata, including sponges, cnidarians, and the peculiar Ediacaran biota (Grotzinger et al. 1995). Forty million years later, the Cambrian explosion occurred (Bowring et al. 1993). The emergence of the Ediacaran biota (570 mya), and then to a much greater extent the Cambrian explosion (530 mya), represented steep climbs up the biological complexity gradient.

One way to estimate the amount of new CSI that appeared with the Cambrian animals is to count the number of new cell types that emerged with them (Valentine 1995:91-93). Studies of modern animals suggest that the sponges that appeared in the late Precambrian, for example, would have required five cell types, whereas the more complex animals that appeared in the Cambrian (e.g., arthropods) would have required fifty or more cell types. Functionally more complex animals require more cell types to perform their more diverse functions. New cell types require many new and specialized proteins. New proteins, in turn, require new genetic information. Thus an increase in the number of cell types implies (at a minimum) a considerable increase in the amount of specified genetic information. Molecular biologists have recently estimated that a minimally complex single-celled organism would require between 318 and 562 kilobase pairs of DNA to produce the proteins necessary to maintain life (Koonin 2000). More complex single cells might require upward of a million base pairs. Yet to build the proteins necessary to sustain a complex arthropod such as a trilobite would require orders of magnitude more coding instructions. The genome size of a modern arthropod, the fruitfly Drosophila melanogaster, is approximately 180 million base pairs (Gerhart & Kirschner 1997:121, Adams et al. 2000). Transitions from a single cell to colonies of cells to complex animals represent significant (and, in principle, measurable) increases in CSI.

Building a new animal from a single-celled organism requires a vast amount of new genetic information. It also requires a way of arranging gene products--proteins--into higher levels of organization. New proteins are required to service new cell types. But new proteins must be organized into new systems within the cell; new cell types must be organized into new tissues, organs, and body parts. These, in turn, must be organized to form body plans. New animals, therefore, embody hierarchically organized systems of lower-level parts within a functional whole. Such hierarchical organization itself represents a type of information, since body plans comprise both highly improbable and functionally specified arrangements of lower-level parts. The specified complexity of new body plans requires explanation in any account of the Cambrian explosion.

Can neo-Darwinism explain the discontinuous increase in CSI that appears in the Cambrian explosion--either in the form of new genetic information or in the form of hierarchically organized systems of parts? We will now examine the two parts of this question.

Novel Genes and Proteins

Many scientists and mathematicians have questioned the ability of mutation and selection to generate information in the form of novel genes and proteins. Such skepticism often derives from consideration of the extreme improbability (and specificity) of functional genes and proteins.

A typical gene contains over one thousand precisely arranged bases. For any specific arrangement of four nucleotide bases of length n, there is a corresponding number of possible arrangements of bases, 4n. For any protein, there are 20n possible arrangements of protein-forming amino acids. A gene 999 bases in length represents one of 4999 possible nucleotide sequences; a protein of 333 amino acids is one of 20333 possibilities.

Since the 1960s, some biologists have thought functional proteins to be rare among the set of possible amino acid sequences. Some have used an analogy with human language to illustrate why this should be the case. Denton (1986, 309-311), for example, has shown that meaningful words and sentences are extremely rare among the set of possible combinations of English letters, especially as sequence length grows. (The ratio of meaningful 12-letter words to 12-letter sequences is 1/1014, the ratio of 100-letter sentences to possible 100-letter strings is 1/10100.) Further, Denton shows that most meaningful sentences are highly isolated from one another in the space of possible combinations, so that random substitutions of letters will, after a very few changes, inevitably degrade meaning. Apart from a few closely clustered sentences accessible by random substitution, the overwhelming majority of meaningful sentences lie, probabilistically speaking, beyond the reach of random search.

Denton (1986:301-324) and others have argued that similar constraints apply to genes and proteins. They have questioned whether an undirected search via mutation and selection would have a reasonable chance of locating new islands of function--representing fundamentally new genes or proteins--within the time available (Eden 1967, Shutzenberger 1967, Lovtrup 1979). Some have also argued that alterations in sequencing would likely result in loss of protein function before fundamentally new function could arise (Eden 1967, Denton 1986). Nevertheless, neither the extent to which genes and proteins are sensitive to functional loss as a result of sequence change, nor the extent to which functional proteins are isolated within sequence space, has been fully known.

Recently, experiments in molecular biology have shed light on these questions. A variety of mutagenesis techniques have shown that proteins (and thus the genes that produce them) are indeed highly specified relative to biological function (Bowie & Sauer 1989, Reidhaar-Olson & Sauer 1990, Taylor et al. 2001). Mutagenesis research tests the sensitivity of proteins (and, by implication, DNA) to functional loss as a result of alterations in sequencing. Studies of proteins have long shown that amino acid residues at many active positions cannot vary without functional loss (Perutz & Lehmann 1968). More recent protein studies (often using mutagenesis experiments) have shown that functional requirements place significant constraints on sequencing even at non-active site positions (Bowie & Sauer 1989, Reidhaar-Olson & Sauer 1990, Chothia et al. 1998, Axe 2000, Taylor et al. 2001). In particular, Axe (2000) has shown that multiple as opposed to single position amino acid substitutions inevitably result in loss of protein function, even when these changes occur at sites that allow variation when altered in isolation. Cumulatively, these constraints imply that proteins are highly sensitive to functional loss as a result of alterations in sequencing, and that functional proteins represent highly isolated and improbable arrangements of amino acids -arrangements that are far more improbable, in fact, than would be likely to arise by chance alone in the time available (Reidhaar-Olson & Sauer 1990; Behe 1992; Kauffman 1995:44; Dembski 1998:175-223; Axe 2000, 2004). (See below the discussion of the neutral theory of evolution for a precise quantitative assessment.)

Of course, neo-Darwinists do not envision a completely random search through the set of all possible nucleotide sequences--so-called "sequence space." They envision natural selection acting to preserve small advantageous variations in genetic sequences and their corresponding protein products. Dawkins (1996), for example, likens an organism to a high mountain peak. He compares climbing the sheer precipice up the front side of the mountain to building a new organism by chance. He acknowledges that his approach up "Mount Improbable" will not succeed. Nevertheless, he suggests that there is a gradual slope up the backside of the mountain that could be climbed in small incremental steps. In his analogy, the backside climb up "Mount Improbable" corresponds to the process of natural selection acting on random changes in the genetic text. What chance alone cannot accomplish blindly or in one leap, selection (acting on mutations) can accomplish through the cumulative effect of many slight successive steps.

Yet the extreme specificity and complexity of proteins presents a difficulty, not only for the chance origin of specified biological information (i.e., for random mutations acting alone), but also for selection and mutation acting in concert. Indeed, mutagenesis experiments cast doubt on each of the two scenarios by which neo-Darwinists envisioned new information arising from the mutation/selection mechanism (for review, see Lonnig 2001). For neo-Darwinism, new functional genes either arise from non-coding sections in the genome or from preexisting genes. Both scenarios are problematic.

In the first scenario, neo-Darwinists envision new genetic information arising from those sections of the genetic text that can presumably vary freely without consequence to the organism. According to this scenario, non-coding sections of the genome, or duplicated sections of coding regions, can experience a protracted period of "neutral evolution" (Kimura 1983) during which alterations in nucleotide sequences have no discernible effect on the function of the organism. Eventually, however, a new gene sequence will arise that can code for a novel protein. At that point, natural selection can favor the new gene and its functional protein product, thus securing the preservation and heritability of both.

This scenario has the advantage of allowing the genome to vary through many generations, as mutations "search" the space of possible base sequences. The scenario has an overriding problem, however: the size of the combinatorial space (i.e., the number of possible amino acid sequences) and the extreme rarity and isolation of the functional sequences within that space of possibilities. Since natural selection can do nothing to help generate new functional sequences, but rather can only preserve such sequences once they have arisen, chance alone--random variation--must do the work of information generation--that is, of finding the exceedingly rare functional sequences within the set of combinatorial possibilities. Yet the probability of randomly assembling (or "finding," in the previous sense) a functional sequence is extremely small.

Cassette mutagenesis experiments performed during the early 1990s suggest that the probability of attaining (at random) the correct sequencing for a short protein 100 amino acids long is about 1 in 1065 (Reidhaar-Olson & Sauer 1990, Behe 1992:65-69). This result agreed closely with earlier calculations that Yockey (1978) had performed based upon the known sequence variability of cytochrome c in different species and other theoretical considerations. More recent mutagenesis research has provided additional support for the conclusion that functional proteins are exceedingly rare among possible amino acid sequences (Axe 2000, 2004). Axe (2004) has performed site directed mutagenesis experiments on a 150-residue protein-folding domain within a B-lactamase enzyme. His experimental method improves upon earlier mutagenesis techniques and corrects for several sources of possible estimation error inherent in them. On the basis of these experiments, Axe has estimated the ratio of (a) proteins of typical size (150 residues) that perform a specified function via any folded structure to (b) the whole set of possible amino acids sequences of that size. Based on his experiments, Axe has estimated his ratio to be 1 to 1077. Thus, the probability of finding a functional protein among the possible amino acid sequences corresponding to a 150-residue protein is similarly 1 in 1077.

Other considerations imply additional improbabilities. First, new Cambrian animals would require proteins much longer than 100 residues to perform many necessary specialized functions. Ohno (1996) has noted that Cambrian animals would have required complex proteins such as lysyl oxidase in order to support their stout body structures. Lysyl oxidase molecules in extant organisms comprise over 400 amino acids. These molecules are both highly complex (non-repetitive) and functionally specified. Reasonable extrapolation from mutagenesis experiments done on shorter protein molecules suggests that the probability of producing functionally sequenced proteins of this length at random is so small as to make appeals to chance absurd, even granting the duration of the entire universe. (See Dembski 1998:175-223 for a rigorous calculation of this "Universal Probability Bound"; See also Axe 2004.) Yet, second, fossil data (Bowring et al. 1993, 1998a:1, 1998b:40; Kerr 1993; Monatersky 1993), and even molecular analyses supporting deep divergence (Wray et al. 1996), suggest that the duration of the Cambrian explosion (between 5-10 x 106 and, at most, 7 x 107 years) is far smaller than that of the entire universe (1.3-2 x 1010 years). Third, DNA mutation rates are far too low to generate the novel genes and proteins necessary to building the Cambrian animals, given the most probable duration of the explosion as determined by fossil studies (Conway Morris 1998b). As Ohno (1996:8475) notes, even a mutation rate of 10-9 per base pair per year results in only a 1% change in the sequence of a given section of DNA in 10 million years. Thus, he argues that mutational divergence of preexisting genes cannot explain the origin of the Cambrian forms in that time.4

The selection/mutation mechanism faces another probabilistic obstacle. The animals that arise in the Cambrian exhibit structures that would have required many new types of cells, each of which would have required many novel proteins to perform their specialized functions. Further, new cell types require Asystems of proteins that must, as a condition of functioning, act in close coordination with one another. The unit of selection in such systems ascends to the system as a whole. Natural selection selects for functional advantage. But new cell types require whole systems of proteins to perform their distinctive functions. In such cases, natural selection cannot contribute to the process of information generation until after the information necessary to build the requisite system of proteins has arisen. Thus random variations must, again, do the work of information generation--and now not simply for one protein, but for many proteins arising at nearly the same time. Yet the odds of this occurring by chance alone are, of course, far smaller than the odds of the chance origin of a single gene or protein--so small in fact as to render the chance origin of the genetic information necessary to build a new cell type (a necessary but not sufficient condition of building a new body plan) problematic given even the most optimistic estimates for the duration of the Cambrian explosion.

Dawkins (1986:139) has noted that scientific theories can rely on only so much "luck" before they cease to be credible. The neutral theory of evolution, which, by its own logic, prevents natural selection from playing a role in generating genetic information until after the fact, relies on entirely too much luck. The sensitivity of proteins to functional loss, the need for long proteins to build new cell types and animals, the need for whole new systems of proteins to service new cell types, the probable brevity of the Cambrian explosion relative to mutation rates--all suggest the immense improbability (and implausibility) of any scenario for the origination of Cambrian genetic information that relies upon random variation alone unassisted by natural selection.

Yet the neutral theory requires novel genes and proteins to arise--essentially--by random mutation alone. Adaptive advantage accrues after the generation of new functional genes and proteins. Thus, natural selection cannot play a role until new information-bearing molecules have independently arisen. Thus neutral theorists envisioned the need to scale the steep face of a Dawkins-style precipice of which there is no gradually sloping backside--a situation that, by Dawkins' own logic, is probabilistically untenable.

In the second scenario, neo-Darwinists envisioned novel genes and proteins arising by numerous successive mutations in the preexisting genetic text that codes for proteins. To adapt Dawkins's metaphor, this scenario envisions gradually climbing down one functional peak and then ascending another. Yet mutagenesis experiments again suggest a difficulty. Recent experiments show that, even when exploring a region of sequence space populated by proteins of a single fold and function, most multiple-position changes quickly lead to loss of function (Axe 2000). Yet to turn one protein into another with a completely novel structure and function requires specified changes at many sites. Indeed, the number of changes necessary to produce a new protein greatly exceeds the number of changes that will typically produce functional losses. Given this, the probability of escaping total functional loss during a random search for the changes needed to produce a new function is extremely small--and this probability diminishes exponentially with each additional requisite change (Axe 2000). Thus, Axe's results imply that, in all probability, random searches for novel proteins (through sequence space) will result in functional loss long before any novel functional protein will emerge.

Blanco et al. have come to a similar conclusion. Using directed mutagenesis, they have determined that residues in both the hydrophobic core and on the surface of the protein play essential roles in determining protein structure. By sampling intermediate sequences between two naturally occurring sequences that adopt different folds, they found that the intermediate sequences "lack a well defined three-dimensional structure." Thus, they conclude that it is unlikely that a new protein fold via a series of folded intermediates sequences (Blanco et al. 1999:741).

Thus, although this second neo-Darwinian scenario has the advantage of starting with functional genes and proteins, it also has a lethal disadvantage: any process of random mutation or rearrangement in the genome would in all probability generate nonfunctional intermediate sequences before fundamentally new functional genes or proteins would arise. Clearly, nonfunctional intermediate sequences confer no survival advantage on their host organisms. Natural selection favors only functional advantage. It cannot select or favor nucleotide sequences or polypeptide chains that do not yet perform biological functions, and still less will it favor sequences that efface or destroy preexisting function.

Evolving genes and proteins will range through a series of nonfunctional intermediate sequences that natural selection will not favor or preserve but will, in all probability, eliminate (Blanco et al. 1999, Axe 2000). When this happens, selection-driven evolution will cease. At this point, neutral evolution of the genome (unhinged from selective pressure) may ensue, but, as we have seen, such a process must overcome immense probabilistic hurdles, even granting cosmic time.

Thus, whether one envisions the evolutionary process beginning with a noncoding region of the genome or a preexisting functional gene, the functional specificity and complexity of proteins impose very stringent limitations on the efficacy of mutation and selection. In the first case, function must arise first, before natural selection can act to favor a novel variation. In the second case, function must be continuously maintained in order to prevent deleterious (or lethal) consequences to the organism and to allow further evolution. Yet the complexity and functional specificity of proteins implies that both these conditions will be extremely difficult to meet. Therefore, the neo-Darwinian mechanism appears to be inadequate to generate the new information present in the novel genes and proteins that arise with the Cambrian animals.

Novel Body Plans

The problems with the neo-Darwinian mechanism run deeper still. In order to explain the origin of the Cambrian animals, one must account not only for new proteins and cell types, but also for the origin of new body plans. Within the past decade, developmental biology has dramatically advanced our understanding of how body plans are built during ontogeny. In the process, it has also uncovered a profound difficulty for neo-Darwinism.

Significant morphological change in organisms requires attention to timing. Mutations in genes that are expressed late in the development of an organism will not affect the body plan. Mutations expressed early in development, however, could conceivably produce significant morphological change (Arthur 1997:21). Thus, events expressed early in the development of organisms have the only realistic chance of producing large-scale macroevolutionary change (Thomson 1992). As John and Miklos (1988:309) explain, macroevolutionary change requires alterations in the very early stages of ontogenesis.

Yet recent studies in developmental biology make clear that mutations expressed early in development typically have deleterious effects (Arthur 1997:21). For example, when early-acting body plan molecules, or morphogens such as bicoid (which helps to set up the anterior-posterior head-to-tail axis in Drosophila), are perturbed, development shuts down (Nusslein-Volhard & Wieschaus 1980, Lawrence & Struhl 1996, Muller & Newman 2003).5 The resulting embryos die. Moreover, there is a good reason for this. If an engineer modifies the length of the piston rods in an internal combustion engine without modifying the crankshaft accordingly, the engine won't start. Similarly, processes of development are tightly integrated spatially and temporally such that changes early in development will require a host of other coordinated changes in separate but functionally interrelated developmental processes downstream. For this reason, mutations will be much more likely to be deadly if they disrupt a functionally deeply-embedded structure such as a spinal column than if they affect more isolated anatomical features such as fingers (Kauffman 1995:200).

This problem has led to what McDonald (1983) has called "a great Darwinian paradox" (p. 93). McDonald notes that genes that are observed to vary within natural populations do not lead to major adaptive changes, while genes that could cause major changes--the very stuff of macroevolution--apparently do not vary. In other words, mutations of the kind that macroevolution doesn't need (namely, viable genetic mutations in DNA expressed late in development) do occur, but those that it does need (namely, beneficial body plan mutations expressed early in development) apparently don't occur.6 According to Darwin (1859:108) natural selection cannot act until favorable variations arise in a population. Yet there is no evidence from developmental genetics that the kind of variations required by neo-Darwinism--namely, favorable body plan mutations--ever occur.

Developmental biology has raised another formidable problem for the mutation/selection mechanism. Embryological evidence has long shown that DNA does not wholly determine morphological form (Goodwin 1985, Nijhout 1990, Sapp 1987, Muller & Newman 2003), suggesting that mutations in DNA alone cannot account for the morphological changes required to build a new body plan.

DNA helps direct protein synthesis.7 It also helps to regulate the timing and expression of the synthesis of various proteins within cells. Yet, DNA alone does not determine how individual proteins assemble themselves into larger systems of proteins; still less does it solely determine how cell types, tissue types, and organs arrange themselves into body plans (Harold 1995:2774, Moss 2004). Instead, other factors--such as the three-dimensional structure and organization of the cell membrane and cytoskeleton and the spatial architecture of the fertilized egg--play important roles in determining body plan formation during embryogenesis.

For example, the structure and location of the cytoskeleton influence the patterning of embryos. Arrays of microtubules help to distribute the essential proteins used during development to their correct locations in the cell. Of course, microtubules themselves are made of many protein subunits. Nevertheless, like bricks that can be used to assemble many different structures, the tubulin subunits in the cell's microtubules are identical to one another. Thus, neither the tubulin subunits nor the genes that produce them account for the different shape of microtubule arrays that distinguish different kinds of embryos and developmental pathways. Instead, the structure of the microtubule array itself is determined by the location and arrangement of its subunits, not the properties of the subunits themselves. For this reason, it is not possible to predict the structure of the cytoskeleton of the cell from the characteristics of the protein constituents that form that structure (Harold 2001:125).

Two analogies may help further clarify the point. At a building site, builders will make use of many materials: lumber, wires, nails, drywall, piping, and windows. Yet building materials do not determine the floor plan of the house, or the arrangement of houses in a neighborhood. Similarly, electronic circuits are composed of many components, such as resistors, capacitors, and transistors. But such lower-level components do not determine their own arrangement in an integrated circuit. Biological symptoms also depend on hierarchical arrangements of parts. Genes and proteins are made from simple building blocks--nucleotide bases and amino acids--arranged in specific ways. Cell types are made of, among other things, systems of specialized proteins. Organs are made of specialized arrangements of cell types and tissues. And body plans comprise specific arrangements of specialized organs. Yet, clearly, the properties of individual proteins (or, indeed, the lower-level parts in the hierarchy generally) do not fully determine the organization of the higher-level structures and organizational patterns (Harold 2001:125). It follows that the genetic information that codes for proteins does not determine these higher-level structures either.

These considerations pose another challenge to the sufficiency of the neo-Darwinian mechanism. Neo-Darwinism seeks to explain the origin of new information, form, and structure as a result of selection acting on randomly arising variation at a very low level within the biological hierarchy, namely, within the genetic text. Yet major morphological innovations depend on a specificity of arrangement at a much higher level of the organizational hierarchy, a level that DNA alone does not determine. Yet if DNA is not wholly responsible for body plan morphogenesis, then DNA sequences can mutate indefinitely, without regard to realistic probabilistic limits, and still not produce a new body plan. Thus, the mechanism of natural selection acting on random mutations in DNA cannot in principle generate novel body plans, including those that first arose in the Cambrian explosion.

Of course, it could be argued that, while many single proteins do not by themselves determine cellular structures and/or body plans, proteins acting in concert with other proteins or suites of proteins could determine such higher-level form. For example, it might be pointed out that the tubulin subunits (cited above) are assembled by other helper proteins--gene products--called Microtubule Associated Proteins (MAPS). This might seem to suggest that genes and gene products alone do suffice to determine the development of the three-dimensional structure of the cytoskeleton.

Yet MAPS, and indeed many other necessary proteins, are only part of the story. The location of specified target sites on the interior of the cell membrane also helps to determine the shape of the cytoskeleton. Similarly, so does the position and structure of the centrosome which nucleates the microtubules that form the cytoskeleton. While both the membrane targets and the centrosomes are made of proteins, the location and form of these structures is not wholly determined by the proteins that form them. Indeed, centrosome structure and membrane patterns as a whole convey three-dimensional structural information that helps determine the structure of the cytoskeleton and the location of its subunits (McNiven & Porter 1992:313-329). Moreover, the centrioles that compose the centrosomes replicate independently of DNA replication (Lange et al. 2000:235-249, Marshall & Rosenbaum 2000:187-205). The daughter centriole receives its form from the overall structure of the mother centriole, not from the individual gene products that constitute it (Lange et al. 2000). In ciliates, microsurgery on cell membranes can produce heritable changes in membrane patterns, even though the DNA of the ciliates has not been altered (Sonneborn 1970:1-13, Frankel 1980:607-623; Nanney 1983:163-170). This suggests that membrane patterns (as opposed to membrane constituents) are impressed directly on daughter cells. In both cases, form is transmitted from parent three-dimensional structures to daughter three-dimensional structures directly and is not wholly contained in constituent proteins or genetic information (Moss 2004).

Thus, in each new generation, the form and structure of the cell arises as the result of both gene products and preexisting three-dimensional structure and organization. Cellular structures are built from proteins, but proteins find their way to correct locations in part because of preexisting three-dimensional patterns and organization inherent in cellular structures. Preexisting three-dimensional form present in the preceding generation (whether inherent in the cell membrane, the centrosomes, the cytoskeleton or other features of the fertilized egg) contributes to the production of form in the next generation. Neither structural proteins alone, nor the genes that code for them, are sufficient to determine the three-dimensional shape and structure of the entities they form. Gene products provide necessary, but not sufficient conditions, for the development of three-dimensional structure within cells, organs and body plans (Harold 1995:2767). But if this is so, then natural selection acting on genetic variation alone cannot produce the new forms that arise in history of life.

Self-Organizational Models

Of course, neo-Darwinism is not the only evolutionary theory for explaining the origin of novel biological form. Kauffman (1995) doubts the efficacy of the mutation/selection mechanism. Nevertheless, he has advanced a self-organizational theory to account for the emergence of new form, and presumably the information necessary to generate it. Whereas neo-Darwinism attempts to explain new form as the consequence of selection acting on random mutation, Kauffman suggests that selection acts, not mainly on random variations, but on emergent patterns of order that self-organize via the laws of nature.

Kauffman (1995:47-92) illustrates how this might work with various model systems in a computer environment. In one, he conceives a system of buttons connected by strings. Buttons represent novel genes or gene products; strings represent the law-like forces of interaction that obtain between gene products-i.e., proteins. Kauffman suggests that when the complexity of the system (as represented by the number of buttons and strings) reaches a critical threshold, new modes of organization can arise in the system "for free"--that is, naturally and spontaneously--after the manner of a phase transition in chemistry.

Another model that Kauffman develops is a system of interconnected lights. Each light can flash in a variety of states--on, off, twinkling, etc. Since there is more than one possible state for each light, and many lights, there are a vast number of possible states that the system can adopt. Further, in his system, rules determine how past states will influence future states. Kauffman asserts that, as a result of these rules, the system will, if properly tuned, eventually produce a kind of order in which a few basic patterns of light activity recur with greater-than-random frequency. Since these actual patterns of light activity represent a small portion of the total number of possible states in which the system can reside, Kauffman seems to imply that self-organizational laws might similarly result in highly improbable biological outcomes--perhaps even sequences (of bases or amino acids) within a much larger sequence space of possibilities.

Do these simulations of self-organizational processes accurately model the origin of novel genetic information? It is hard to think so.

First, in both examples, Kauffman presupposes but does not explain significant sources of preexisting information. In his buttons-and-strings system, the buttons represent proteins, themselves packets of CSI, and the result of preexisting genetic information. Where does this information come from? Kauffman (1995) doesn't say, but the origin of such information is an essential part of what needs to be explained in the history of life. Similarly, in his light system, the order that allegedly arises for "for free" actually arises only if the programmer of the model system "tunes" it in such a way as to keep it from either (a) generating an excessively rigid order or (b) developing into chaos (pp. 86-88). Yet this necessary tuning involves an intelligent programmer selecting certain parameters and excluding others--that is, inputting information.

Second, Kauffman's model systems are not constrained by functional considerations and thus are not analogous to biological systems. A system of interconnected lights governed by pre-programmed rules may well settle into a small number of patterns within a much larger space of possibilities. But because these patterns have no function, and need not meet any functional requirements, they have no specificity analogous to that present in actual organisms. Instead, examination of Kauffman's (1995) model systems shows that they do not produce sequences or systems characterized by specified complexity, but instead by large amounts of symmetrical order or internal redundancy interspersed with aperiodicity or (mere) complexity (pp. 53, 89, 102). Getting a law-governed system to generate repetitive patterns of flashing lights, even with a certain amount of variation, is clearly interesting, but not biologically relevant. On the other hand, a system of lights flashing the title of a Broadway play would model a biologically relevant self-organizational process, at least if such a meaningful or functionally specified sequence arose without intelligent agents previously programming the system with equivalent amounts of CSI. In any case, Kauffman's systems do not produce specified complexity, and thus do not offer promising models for explaining the new genes and proteins that arose in the Cambrian.

Even so, Kauffman suggests that his self-organizational models can specifically elucidate aspects of the Cambrian explosion. According to Kauffman (1995:199-201), new Cambrian animals emerged as the result of "long jump" mutations that established new body plans in a discrete rather than gradual fashion. He also recognizes that mutations affecting early development are almost inevitably harmful. Thus, he concludes that body plans, once established, will not change, and that any subsequent evolution must occur within an established body plan (Kauffman 1995:201). And indeed, the fossil record does show a curious (from a neo-Darwinian point of view) top-down pattern of appearance, in which higher taxa (and the body plans they represent) appear first, only later to be followed by the multiplication of lower taxa representing variations within those original body designs (Erwin et al. 1987, Lewin 1988, Valentine & Jablonski 2003:518). Further, as Kauffman expects, body plans appear suddenly and persist without significant modification over time.

But here, again, Kauffman begs the most important question, which is: what produces the new Cambrian body plans in the first place? Granted, he invokes "long jump mutations" to explain this, but he identifies no specific self-organizational process that can produce such mutations. Moreover, he concedes a principle that undermines the plausibility of his own proposal. Kauffman acknowledges that mutations that occur early in development are almost inevitably deleterious. Yet developmental biologists know that these are the only kind of mutations that have a realistic chance of producing large-scale evolutionary change--i.e., the big jumps that Kauffman invokes. Though Kauffman repudiates the neo-Darwinian reliance upon random mutations in favor of self-organizing order, in the end, he must invoke the most implausible kind of random mutation in order to provide a self-organizational account of the new Cambrian body plans. Clearly, his model is not sufficient.

Punctuated Equilibrium

Of course, still other causal explanations have been proposed. During the 1970s, the paleontologists Eldredge and Gould (1972) proposed the theory of evolution by punctuated equilibrium in order to account for a pervasive pattern of "sudden appearance" and "stasis" in the fossil record. Though advocates of punctuated equilibrium were mainly seeking to describe the fossil record more accurately than earlier gradualist neo-Darwinian models had done, they did also propose a mechanism--known as species selection--by which the large morphological jumps evident in fossil record might have been produced. According to punctuationalists, natural selection functions more as a mechanism for selecting the fittest species rather than the most-fit individual among a species. Accordingly, on this model, morphological change should occur in larger, more discrete intervals than it would given a traditional neo-Darwinian understanding.

Despite its virtues as a descriptive model of the history of life, punctuated equilibrium has been widely criticized for failing to provide a mechanism sufficient to produce the novel form characteristic of higher taxonomic groups. For one thing, critics have noted that the proposed mechanism of punctuated evolutionary change simply lacked the raw material upon which to work. As Valentine and Erwin (1987) note, the fossil record fails to document a large pool of species prior to the Cambrian. Yet the proposed mechanism of species selection requires just such a pool of species upon which to act. Thus, they conclude that the mechanism of species selection probably does not resolve the problem of the origin of the higher taxonomic groups (p. 96).8 Further, punctuated equilibrium has not addressed the more specific and fundamental problem of explaining the origin of the new biological information (whether genetic or epigenetic) necessary to produce novel biological form. Advocates of punctuated equilibrium might assume that the new species (upon which natural selection acts) arise by known microevolutionary processes of speciation (such as founder effect, genetic drift or bottleneck effect) that do not necessarily depend upon mutations to produce adaptive changes. But, in that case, the theory lacks an account of how the specifically higher taxa arise. Species selection will only produce more fit species. On the other hand, if punctuationalists assume that processes of genetic mutation can produce more fundamental morphological changes and variations, then their model becomes subject to the same problems as neo-Darwinism (see above). This dilemma is evident in Gould (2002:710) insofar as his attempts to explain adaptive complexity inevitably employ classical neo-Darwinian modes of explanation.9

Structuralism

Another attempt to explain the origin of form has been proposed by the structuralists such as Gerry Webster and Brian Goodwin (1984, 1996). These biologists, drawing on the earlier work of D'Arcy Thompson (1942), view biological form as the result of structural constraints imposed upon matter by morphogenetic rules or laws. For reasons similar to those discussed above, the structuralists have insisted that these generative or morphogenetic rules do not reside in the lower level building materials of organisms, whether in genes or proteins. Webster and Goodwin (1984:510-511) further envisioned morphogenetic rules or laws operating ahistorically, similar to the way in which gravitational or electromagnetic laws operate. For this reason, structuralists see phylogeny as of secondary importance in understanding the origin of the higher taxa, though they think that transformations of form can occur. For structuralists, constraints on the arrangement of matter arise not mainly as the result of historical contingencies--such as environmental changes or genetic mutations--but instead because of the continuous ahistorical operation of fundamental laws of form--laws that organize or inform matter.

While this approach avoids many of the difficulties currently afflicting neo-Darwinism (in particular those associated with its "genocentricity"), critics (such as Maynard Smith 1986) of structuralism have argued that the structuralist explanation of form lacks specificity. They note that structuralists have been unable to say just where laws of form reside--whether in the universe, or in every possible world, or in organisms as a whole, or in just some part of organisms. Further, according to structuralists, morphogenetic laws are mathematical in character. Yet, structuralists have yet to specify the mathematical formulae that determine biological forms.

Others (Yockey 1992; Polanyi 1967, 1968; Meyer 2003) have questioned whether physical laws could in principle generate the kind of complexity that characterizes biological systems. Structuralists envision the existence of biological laws that produce form in much the same way that physical laws produce form. Yet the forms that physicists regard as manifestations of underlying laws are characterized by large amounts of symmetric or redundant order, by relatively simple patterns such as vortices or gravitational fields or magnetic lines of force. Indeed, physical laws are typically expressed as differential equations (or algorithms) that almost by definition describe recurring phenomena--patterns of compressible "order" not "complexity" as defined by algorithmic information theory (Yockey 1992:77-83). Biological forms, by contrast, manifest greater complexity and derive in ontogeny from highly complex initial conditions--i.e., non-redundant sequences of nucleotide bases in the genome and other forms of information expressed in the complex and irregular three-dimensional topography of the organism or the fertilized egg. Thus, the kind of form that physical laws produce is not analogous to biological form--at least not when compared from the standpoint of (algorithmic) complexity. Further, physical laws lack the information content to specify biology systems. As Polyanyi (1967, 1968) and Yockey (1992:290) have shown, the laws of physics and chemistry allow, but do not determine, distinctively biological modes of organization. In other words, living systems are consistent with, but not deducible, from physical-chemical laws (1992:290).

Of course, biological systems do manifest some reoccurring patterns, processes and behaviors. The same type of organism develops repeatedly from similar ontogenetic processes in the same species. Similar processes of cell division reoccur in many organisms. Thus, one might describe certain biological processes as law-governed. Even so, the existence of such biological regularities does not solve the problem of the origin of form and information, since the recurring processes described by such biological laws (if there be such laws) only occur as the result of preexisting stores of (genetic and/or epigenetic) information and these information-rich initial conditions impose the constraints that produce the recurring behavior in biological systems. (For example, processes of cell division recur with great frequency in organisms, but depend upon information-rich DNA and proteins molecules.) In other words, distinctively biological regularities depend upon preexisting biological information. Thus, appeals to higher-level biological laws presuppose, but do not explain, the origination of the information necessary to morphogenesis.

Thus, structuralism faces a difficult in principle dilemma. On the one hand, physical laws produce very simple redundant patterns that lack the complexity characteristic of biological systems. On the other hand, distinctively biological laws--if there are such laws--depend upon preexisting information-rich structures. In either case, laws are not good candidates for explaining the origination of biological form or the information necessary to produce it.

Cladism: An Artifact of Classification?

Some cladists have advanced another approach to the problem of the origin of form, specifically as it arises in the Cambrian. They have argued that the problem of the origin of the phyla is an artifact of the classification system, and therefore, does not require explanation. Budd and Jensen (2000), for example, argue that the problem of the Cambrian explosion resolves itself if one keeps in mind the cladistic distinction between "stem" and "crown" groups. Since crown groups arise whenever new characters are added to simpler more ancestral stem groups during the evolutionary process, new phyla will inevitably arise once a new stem group has arisen. Thus, for Budd and Jensen what requires explanation is not the crown groups corresponding to the new Cambrian phyla, but the earlier more primitive stem groups that presumably arose deep in the Proterozoic. Yet since these earlier stem groups are by definition less derived, explaining them will be considerably easier than explaining the origin of the Cambrian animals de novo. In any case, for Budd and Jensen the explosion of new phyla in the Cambrian does not require explanation. As they put it, "given that the early branching points of major clades is an inevitable result of clade diversification, the alleged phenomenon of the phyla appearing early and remaining morphologically static is not seen to require particular explanation" (Budd & Jensen 2000:253).

While superficially plausible, perhaps, Budd and Jensen's attempt to explain away the Cambrian explosion begs crucial questions. Granted, as new characters are added to existing forms, novels morphology and greater morphological disparity will likely result. But what causes new characters to arise? And how does the information necessary to produce new characters originate? Budd and Jensen do not specify. Nor can they say how derived the ancestral forms are likely to have been, and what processes, might have been sufficient to produce them. Instead, they simply assume the sufficiency of known neo-Darwinian mechanisms (Budd & Jensen 2000:288). Yet, as shown above, this assumption is now problematic. In any case, Budd and Jensen do not explain what causes the origination of biological form and information.

Convergence and Teleological Evolution

More recently, Conway Morris (2000, 2003c) has suggested another possible explanation based on the tendency for evolution to converge on the same structural forms during the history of life. Conway Morris cites numerous examples of organisms that possess very similar forms and structures, even though such structures are often built from different material substrates and arise (in ontogeny) by the expression of very different genes. Given the extreme improbability of the same structures arising by random mutation and selection in disparate phylogenies, Conway Morris argues that the pervasiveness of convergent structures suggests that evolution may be in some way "channeled" toward similar functional and/or structural endpoints. Such an end-directed understanding of evolution, he admits, raises the controversial prospect of a teleological or purposive element in the history of life. For this reason, he argues that the phenomenon of convergence has received less attention than it might have otherwise. Nevertheless, he argues that just as physicists have reopened the question of design in their discussions of anthropic fine-tuning, the ubiquity of convergent structures in the history of life has led some biologists (Denton 1998) to consider extending teleological thinking to biology. And, indeed, Conway Morris himself intimates that the evolutionary process might be "underpinned by a purpose" (2000:8, 2003b:511).

Conway Morris, of course, considers this possibility in relation to a very specific aspect of the problem of organismal form, namely, the problem of explaining why the same forms arise repeatedly in so many disparate lines of decent. But this raises a question. Could a similar approach shed explanatory light on the more general causal question that has been addressed in this review? Could the notion of purposive design help provide a more adequate explanation for the origin of organismal form generally? Are there reasons to consider design as an explanation for the origin of the biological information necessary to produce the higher taxa and their corresponding morphological novelty?

The remainder of this review will suggest that there are such reasons. In so doing, it may also help explain why the issue of teleology or design has reemerged within the scientific discussion of biological origins (Denton 1986, 1998; Thaxton et al. 1992; Kenyon & Mills 1996: Behe 1996, 2004; Dembski 1998, 2002, 2004; Conway Morris 2000, 2003a, 2003b, Lonnig 2001; Lonnig & Saedler 2002; Nelson & Wells 2003; Meyer 2003, 2004; Bradley 2004) and why some scientists and philosophers of science have considered teleological explanations for the origin of form and information despite strong methodological prohibitions against design as a scientific hypothesis (Gillespie 1979, Lenior 1982:4).

First, the possibility of design as an explanation follows logically from a consideration of the deficiencies of neo-Darwinism and other current theories as explanations for some of the more striking "appearances of design" in biological systems. Neo-Darwinists such as Ayala (1994:5), Dawkins (1986:1), Mayr (1982:xi-xii) and Lewontin (1978) have long acknowledged that organisms appear to have been designed. Of course, neo-Darwinists assert that what Ayala (1994:5) calls the "obvious design" of living things is only apparent since the selection/mutation mechanism can explain the origin of complex form and organization in living systems without an appeal to a designing agent. Indeed, neo-Darwinists affirm that mutation and selection--and perhaps other similarly undirected mechanisms--are fully sufficient to explain the appearance of design in biology. Self-organizational theorists and punctuationalists modify this claim, but affirm its essential tenet. Self-organization theorists argue that natural selection acting on self organizing order can explain the complexity of living things--again, without any appeal to design. Punctuationalists similarly envision natural selection acting on newly arising species with no actual design involved.

And clearly, the neo-Darwinian mechanism does explain many appearances of design, such as the adaptation of organisms to specialized environments that attracted the interest of 19th century biologists. More specifically, known microevolutionary processes appear quite sufficient to account for changes in the size of Galapagos finch beaks that have occurred in response to variations in annual rainfall and available food supplies (Weiner 1994, Grant 1999).

But does neo-Darwinism, or any other fully materialistic model, explain all appearances of design in biology, including the body plans and information that characterize living systems? Arguably, biological forms--such as the structure of a chambered nautilus, the organization of a trilobite, the functional integration of parts in an eye or molecular machine--attract our attention in part because the organized complexity of such systems seems reminiscent of our own designs. Yet, this review has argued that neo-Darwinism does not adequately account for the origin of all appearances of design, especially if one considers animal body plans, and the information necessary to construct them, as especially striking examples of the appearance of design in living systems. Indeed, Dawkins (1995:11) and Gates (1996:228) have noted that genetic information bears an uncanny resemblance to computer software or machine code. For this reason, the presence of CSI in living organisms, and the discontinuous increases of CSI that occurred during events such as the Cambrian explosion, appears at least suggestive of design.

Does neo-Darwinism or any other purely materialistic model of morphogenesis account for the origin of the genetic and other forms of CSI necessary to produce novel organismal form? If not, as this review has argued, could the emergence of novel information-rich genes, proteins, cell types and body plans have resulted from actual design, rather than a purposeless process that merely mimics the powers of a designing intelligence? The logic of neo-Darwinism, with its specific claim to have accounted for the appearance of design, would itself seem to open the door to this possibility. Indeed, the historical formulation of Darwinism in dialectical opposition to the design hypothesis (Gillespie 1979), coupled with the neo-Darwinism's inability to account for many salient appearances of design including the emergence of form and information, would seem logically to reopen the possibility of actual (as opposed to apparent) design in the history of life.

A second reason for considering design as an explanation for these phenomena follows from the importance of explanatory power to scientific theory evaluation and from a consideration of the potential explanatory power of the design hypothesis. Studies in the methodology and philosophy of science have shown that many scientific theories, particularly in the historical sciences, are formulated and justified as inferences to the best explanation (Lipton 1991:32-88, Brush 1989:1124-1129, Sober 2000:44). Historical scientists, in particular, assess or test competing hypotheses by evaluating which hypothesis would, if true, provide the best explanation for some set of relevant data (Meyer 1991, 2002; Cleland 2001:987-989, 2002:474-496).10 Those with greater explanatory power are typically judged to be better, more probably true, theories. Darwin (1896:437) used this method of reasoning in defending his theory of universal common descent. Moreover, contemporary studies on the method of "inference to the best explanation" have shown that determining which among a set of competing possible explanations constitutes the best depends upon judgments about the causal adequacy, or "causal powers," of competing explanatory entities (Lipton 1991:32-88). In the historical sciences, uniformitarian and/or actualistic (Gould 1965, Simpson 1970, Rutten 1971, Hooykaas 1975) canons of method suggest that judgments about causal adequacy should derive from our present knowledge of cause and effect relationships. For historical scientists, "the present is the key to the past" means that present experience-based knowledge of cause and effect relationships typically guides the assessment of the plausibility of proposed causes of past events.

Yet it is precisely for this reason that current advocates of the design hypothesis want to reconsider design as an explanation for the origin of biological form and information. This review, and much of the literature it has surveyed, suggests that four of the most prominent models for explaining the origin of biological form fail to provide adequate causal explanations for the discontinuous increases of CSI that are required to produce novel morphologies. Yet, we have repeated experience of rational and conscious agents--in particular ourselves--generating or causing increases in complex specified information, both in the form of sequence-specific lines of code and in the form of hierarchically arranged systems of parts.

In the first place, intelligent human agents--in virtue of their rationality and consciousness--have demonstrated the power to produce information in the form of linear sequence-specific arrangements of characters. Indeed, experience affirms that information of this type routinely arises from the activity of intelligent agents. A computer user who traces the information on a screen back to its source invariably comes to a mind--that of a software engineer or programmer. The information in a book or inscriptions ultimately derives from a writer or scribe--from a mental, rather than a strictly material, cause. Our experience-based knowledge of information-flow confirms that systems with large amounts of specified complexity (especially codes and languages) invariably originate from an intelligent source from a mind or personal agent. As Quastler (1964) put it, the "creation of new information is habitually associated with conscious activity" (p. 16). Experience teaches this obvious truth.

Further, the highly specified hierarchical arrangements of parts in animal body plans also suggest design, again because of our experience of the kinds of features and systems that designers can and do produce. At every level of the biological hierarchy, organisms require specified and highly improbable arrangements of lower-level constituents in order to maintain their form and function. Genes require specified arrangements of nucleotide bases; proteins require specified arrangements of amino acids; new cell types require specified arrangements of systems of proteins; body plans require specialized arrangements of cell types and organs. Organisms not only contain information-rich components (such as proteins and genes), but they comprise information-rich arrangements of those components and the systems that comprise them. Yet we know, based on our present experience of cause and effect relationships, that design engineers--possessing purposive intelligence and rationality--have the ability to produce information-rich hierarchies in which both individual modules and the arrangements of those modules exhibit complexity and specificity--information so defined. Individual transistors, resistors, and capacitors exhibit considerable complexity and specificity of design; at a higher level of organization, their specific arrangement within an integrated circuit represents additional information and reflects further design. Conscious and rational agents have, as part of their powers of purposive intelligence, the capacity to design information-rich parts and to organize those parts into functional information-rich systems and hierarchies. Further, we know of no other causal entity or process that has this capacity. Clearly, we have good reason to doubt that mutation and selection, self-organizational processes or laws of nature, can produce the information-rich components, systems, and body plans necessary to explain the origination of morphological novelty such as that which arises in the Cambrian period.

There is a third reason to consider purpose or design as an explanation for the origin of biological form and information: purposive agents have just those necessary powers that natural selection lacks as a condition of its causal adequacy. At several points in the previous analysis, we saw that natural selection lacked the ability to generate novel information precisely because it can only act after new functional CSI has arisen. Natural selection can favor new proteins, and genes, but only after they perform some function. The job of generating new functional genes, proteins and systems of proteins therefore falls entirely to random mutations. Yet without functional criteria to guide a search through the space of possible sequences, random variation is probabilistically doomed. What is needed is not just a source of variation (i.e., the freedom to search a space of possibilities) or a mode of selection that can operate after the fact of a successful search, but instead a means of selection that (a) operates during a search--before success--and that (b) is guided by information about, or knowledge of, a functional target.

Demonstration of this requirement has come from an unlikely quarter: genetic algorithms. Genetic algorithms are programs that allegedly simulate the creative power of mutation and selection. Dawkins and Kuppers, for example, have developed computer programs that putatively simulate the production of genetic information by mutation and natural selection (Dawkins 1986:47-49, Kuppers 1987:355-369). Nevertheless, as shown elsewhere (Meyer 1998:127-128, 2003:247-248), these programs only succeed by the illicit expedient of providing the computer with a "target sequence" and then treating relatively greater proximity to future function (i.e., the target sequence), not actual present function, as a selection criterion. As Berlinski (2000) has argued, genetic algorithms need something akin to a "forward looking memory" in order to succeed. Yet such foresighted selection has no analogue in nature. In biology, where differential survival depends upon maintaining function, selection cannot occur before new functional sequences arise. Natural selection lacks foresight.

What natural selection lacks, intelligent selection--purposive or goal-directed design--provides. Rational agents can arrange both matter and symbols with distant goals in mind. In using language, the human mind routinely "finds" or generates highly improbable linguistic sequences to convey an intended or preconceived idea. In the process of thought, functional objectives precede and constrain the selection of words, sounds and symbols to generate functional (and indeed meaningful) sequences from among a vast ensemble of meaningless alternative combinations of sound or symbol (Denton 1986:309-311). Similarly, the construction of complex technological objects and products, such as bridges, circuit boards, engines and software, result from the application of goal-directed constraints (Polanyi 1967, 1968). Indeed, in all functionally integrated complex systems where the cause is known by experience or observation, design engineers or other intelligent agents applied boundary constraints to limit possibilities in order to produce improbable forms, sequences or structures. Rational agents have repeatedly demonstrated the capacity to constrain the possible to actualize improbable but initially unrealized future functions. Repeated experience affirms that intelligent agents (minds) uniquely possess such causal powers.

Analysis of the problem of the origin of biological information, therefore, exposes a deficiency in the causal powers of natural selection that corresponds precisely to powers that agents are uniquely known to possess. Intelligent agents have foresight. Such agents can select functional goals before they exist. They can devise or select material means to accomplish those ends from among an array of possibilities and then actualize those goals in accord with a preconceived design plan or set of functional requirements. Rational agents can constrain combinatorial space with distant outcomes in mind. The causal powers that natural selection lacks--almost by definition--are associated with the attributes of consciousness and rationality--with purposive intelligence. Thus, by invoking design to explain the origin of new biological information, contemporary design theorists are not positing an arbitrary explanatory element unmotivated by a consideration of the evidence. Instead, they are positing an entity possessing precisely the attributes and causal powers that the phenomenon in question requires as a condition of its production and explanation.

Conclusion

An experience-based analysis of the causal powers of various explanatory hypotheses suggests purposive or intelligent design as a causally adequate--and perhaps the most causally adequate--explanation for the origin of the complex specified information required to build the Cambrian animals and the novel forms they represent. For this reason, recent scientific interest in the design hypothesis is unlikely to abate as biologists continue to wrestle with the problem of the origination of biological form and the higher taxa.


Literature Cited

Adams, M. D. Et alia. 2000. The genome sequence of Drosophila melanogaster.--Science 287:2185-2195.

Aris-Brosou, S., & Z. Yang. 2003. Bayesian models of episodic evolution support a late Precambrian explosive diversification of the Metazoa.--Molecular Biology and Evolution 20:1947-1954.

Arthur, W. 1997. The origin of animal body plans. Cambridge University Press, Cambridge, United Kingdom.

Axe, D. D. 2000. Extreme functional sensitivity to conservative amino acid changes on enzyme exteriors.--Journal of Molecular Biology 301(3):585-596.

______. 2004. Estimating the prevalence of protein sequences adopting functional enzyme folds.--Journal of Molecular Biology (in press).

Ayala, F. 1994. Darwin's revolution. Pp. 1-17 in J. Campbell and J. Schopf, eds., Creative evolution?! Jones and Bartlett Publishers, Boston, Massachusetts.

______. A. Rzhetsky, & F. J. Ayala. 1998. Origin of the metazoan phyla: molecular clocks confirm paleontological estimates--Proceedings of the National Academy of Sciences USA. 95:606-611.

Becker, H., & W. Lonnig, 2001. Transposons: eukaryotic. Pp. 529-539 in Nature encyclopedia of life sciences, vol. 18. Nature Publishing Group, London, United Kingdom.

Behe, M. 1992. Experimental support for regarding functional classes of proteins to be highly isolated from each other. Pp. 60-71 in J. Buell and V. Hearn, eds., Darwinism: science or philosophy? Foundation for Thought and Ethics, Richardson, Texas.

______. 1996. Darwin's black box. The Free Press, New York.

______. 2004. Irreducible complexity: obstacle to Darwinian evolution. Pp. 352-370 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Benton, M., & F. J. Ayala. 2003. Dating the tree of life--Science 300:1698-1700.

Berlinski, D. 2000. "On assessing genetic algorithms." Public lecture. Conference: Science and evidence of design in the universe. Yale University, November 4, 2000.

Blanco, F., I. Angrand, & L. Serrano. 1999. Exploring the confirmational properties of the sequence space between two proteins with different folds: an experimental study.--Journal of Molecular Biology 285:741-753.

Bowie, J., & R. Sauer. 1989. Identifying determinants of folding and activity for a protein of unknown sequences: tolerance to amino acid substitution.--Proceedings of the National Academy of Sciences, U.S.A. 86:2152-2156.

Bowring, S. A., J. P. Grotzinger, C. E. Isachsen, A. H. Knoll, S. M. Pelechaty, & P. Kolosov. 1993. Calibrating rates of early Cambrian evolution.--Science 261:1293-1298.

______. 1998a. A new look at evolutionary rates in deep time: Uniting paleontology and high-precision geochronology.--GSA Today 8:1-8.

______. 1998b. Geochronology comes of age.--Geotimes 43:36-40.

Bradley, W. 2004. Information, entropy and the origin of life. Pp. 331-351 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Brocks, J. J., G. A. Logan, R. Buick, & R. E. Summons. 1999. Archean molecular fossils and the early rise of eukaryotes.--Science 285:1033-1036.

Brush, S. G. 1989. Prediction and theory evaluation: the case of light bending.--Science 246:1124-1129.

Budd, G. E. & S. E. Jensen. 2000. A critical reappraisal of the fossil record of the bilaterial phyla.--Biological Reviews of the Cambridge Philosophical Society 75:253-295.

Carroll, R. L. 2000. Towards a new evolutionary synthesis.--Trends in Ecology and Evolution 15:27-32.

Cleland, C. 2001. Historical science, experimental science, and the scientific method.--Geology 29:987-990.

______. 2002. Methodological and epistemic differences between historical science and experimental science.--Philosophy of Science 69:474-496.

Chothia, C., I. Gelfland, & A. Kister. 1998. Structural determinants in the sequences of immunoglobulin variable domain.--Journal of Molecular Biology 278:457-479.

Conway Morris, S. 1998a. The question of metazoan monophyly and the fossil record.--Progress in Molecular and Subcellular Biology 21:1-9.

______. 1998b. Early Metazoan evolution: Reconciling paleontology and molecular biology.--American Zoologist 38 (1998):867-877.

______. 2000. Evolution: bringing molecules into the fold.--Cell 100:1-11.

______. 2003a. The Cambrian "explosion" of metazoans. Pp. 13-32 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

______. 2003b. Cambrian "explosion" of metazoans and molecular biology: would Darwin be satisfied?--International Journal of Developmental Biology 47(7-8):505-515.

______. 2003c. Life's solution: inevitable humans in a lonely universe. Cambridge University Press, Cambridge, United Kingdom.

Crick, F. 1958. On protein synthesis.--Symposium for the Society of Experimental Biology. 12(1958):138-163.

Darwin, C. 1859. On the origin of species. John Murray, London, United Kingdom.

______. 1896. Letter to Asa Gray. P. 437 in F. Darwin, ed., Life and letters of Charles Darwin, vol. 1., D. Appleton, London, United Kingdom.

Davidson, E. 2001. Genomic regulatory systems: development and evolution. Academic Press, New York, New York.

Dawkins, R. 1986. The blind watchmaker. Penguin Books, London, United Kingdom.

______. 1995. River out of Eden. Basic Books, New York.

______. 1996. Climbing Mount Improbable. W. W. Norton & Company, New York.

Dembski, W. A. 1998. The design inference. Cambridge University Press, Cambridge, United Kingdom.

______. 2002. No free lunch: why specified complexity cannot be purchased without intelligence. Rowman & Littlefield, Lanham, Maryland.

______. 2004. The logical underpinnings of intelligent design. Pp. 311-330 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

Denton, M. 1986. Evolution: a theory in crisis. Adler & Adler, London, United Kingdom.

______. 1998. Nature's density. The Free Press, New York.

Eden, M. 1967. The inadequacies of neo-Darwinian evolution as a scientific theory. Pp. 5-12 in P. S. Morehead and M. M. Kaplan, eds., Mathematical challenges to the Darwinian interpretation of evolution. Wistar Institute Symposium Monograph, Allen R. Liss, New York.

Eldredge, N., & S. J. Gould. 1972. Punctuated equilibria: an alternative to phyletic gradualism. Pp. 82-115 in T. Schopf, ed., Models in paleobiology. W. H. Freeman, San Francisco.

Erwin, D. H. 1994. Early introduction of major morphological innovations.--Acta Palaeontologica Polonica 38:281-294.

______. 2000. Macroevolution is more than repeated rounds of microevolution.--Evolution & Development 2:78-84.

______. 2004. One very long argument.--Biology and Philosophy 19:17-28.

______, J. Valentine, & D. Jablonski. 1997. The origin of animal body plans.--American Scientist 85:126-137.

______, ______, & J. J. Sepkoski. 1987. A comparative study of diversification events: the early Paleozoic versus the Mesozoic.--Evolution 41:1177-1186.

Foote, M. 1997. Sampling, taxonomic description, and our evolving knowledge of morphological diversity.--Paleobiology 23:181-206.

______, J. P. Hunter, C. M. Janis, & J. J. Sepkoski. 1999. Evolutionary and preservational constraints on origins of biologic groups: Divergence times of eutherian mammals.--Science 283:1310-1314.

Frankel, J. 1980. Propagation of cortical differences in tetrahymena.--Genetics 94:607-623.

Gates, B. 1996. The road ahead. Blue Penguin, Boulder, Colorado.
Gerhart, J., & M. Kirschner. 1997. Cells, embryos, and evolution. Blackwell Science, London, United Kingdom.

Gibbs, W. W. 2003. The unseen genome: gems among the junk.--Scientific American. 289:46-53.

Gilbert, S. F., J. M. Opitz, & R. A. Raff. 1996. Resynthesizing evolutionary and developmental biology.--Developmental Biology 173:357-372.

Gillespie, N. C. 1979. Charles Darwin and the problem of creation. University of Chicago Press, Chicago.

Goodwin, B. C. 1985. What are the causes of morphogenesis?--BioEssays 3:32-36.

______. 1995. How the leopard changed its spots: the evolution of complexity. Scribner's, New York, New York.

Gould, S. J. 1965. Is uniformitarianism necessary?--American Journal of Science 263:223-228.

Gould, S. J. 2002. The structure of evolutionary theory. Harvard University Press, Cambridge, Massachusetts.

Grant, P. R. 1999. Ecology and evolution of Darwin's finches. Princeton University Press, Princeton, New Jersey.

Grimes, G. W., & K. J. Aufderheide. 1991. Cellular aspects of pattern formation: the problem of assembly. Monographs in Developmental Biology, vol. 22. Karger, Baseline, Switzerland.

Grotzinger, J. P., S. A. Bowring, B. Z. Saylor, & A. J. Kaufman. 1995. Biostratigraphic and geochronologic constraints on early animal evolution.--Science 270:598-604.

Harold, F. M. 1995. From morphogenes to morphogenesis.--Microbiology 141:2765-2778.

______. 2001. The way of the cell: molecules, organisms, and the order of life. Oxford University Press, New York.

Hodge, M. J. S. 1977. The structure and strategy of Darwin's long argument.--British Journal for the History of Science 10:237-245.

Hooykaas, R. 1975. Catastrophism in geology, its scientific character in relation to actualism and uniformitarianism. Pp. 270-316 in C. Albritton, ed., Philosophy of geohistory (1785-1970). Dowden, Hutchinson & Ross, Stroudsburg, Pennsylvania.

John, B., & G. Miklos. 1988. The eukaryote genome in development and evolution. Allen & Unwinding, London, United Kingdom.

Kauffman, S. 1995. At home in the universe. Oxford University Press, Oxford, United Kingdom.

Kenyon, D., & G. Mills. 1996. The RNA world: a critique.--Origins & Design 17(1):9-16.

Kerr, R. A. 1993. Evolution's Big Bang gets even more explosive.-- Science 261:1274-1275.

Kimura, M. 1983. The neutral theory of molecular evolution. Cambridge University Press, Cambridge, United Kingdom.

Koonin, E. 2000. How many genes can make a cell?: the minimal genome concept.--Annual Review of Genomics and Human Genetics 1:99-116.

Kuppers, B. O. 1987. On the prior probability of the existence of life. Pp. 355-369 in L. Kruger et al., eds., The probabilistic revolution. M.I.T. Press, Cambridge, Massachusetts.

Lange, B. M. H., A. J. Faragher, P. March, & K. Gull. 2000. Centriole duplication and maturation in animal cells. Pp. 235-249 in R. E. Palazzo and G. P. Schatten, eds., The centrosome in cell replication and early development. Current Topics in Developmental Biology, vol. 49. Academic Press, San Diego.

Lawrence, P. A., & G. Struhl. 1996. Morphogens, compartments and pattern: lessons from Drosophila?--Cell 85:951-961.

Lenior, T. 1982. The strategy of life. University of Chicago Press, Chicago.

Levinton, J. 1988. Genetics, paleontology, and macroevolution. Cambridge University Press, Cambridge, United Kingdom.

______. 1992. The big bang of animal evolution.--Scientific American 267:84-91.

Lewin, R. 1988. A lopsided look at evolution.--Science 241:292.

Lewontin, R. 1978. Adaptation. Pp. 113-125 in Evolution: a Scientific American book. W. H. Freeman & Company, San Francisco.

Lipton, P. 1991. Inference to the best explanation. Routledge, New York.

Lonnig, W. E. 2001. Natural selection. Pp. 1008-1016 in W. E. Craighead and C. B. Nemeroff, eds., The Corsini encyclopedia of psychology and behavioral sciences, 3rd edition, vol. 3. John Wiley & Sons, New York.

______, & H. Saedler. 2002. Chromosome rearrangements and transposable elements.--Annual Review of Genetics 36:389-410.

Lovtrup, S. 1979. Semantics, logic and vulgate neo-darwinism.--Evolutionary Theory 4:157-172.

Marshall, W. F. & J. L. Rosenbaum. 2000. Are there nucleic acids in the centrosome? Pp. 187-205 in R. E. Palazzo and G. P. Schatten, eds., The centrosome in cell replication and early development. Current Topics in Developmental Biology, vol. 49. San Diego, Academic Press.

Maynard Smith, J. 1986. Structuralism versus selection--is Darwinism enough? Pp. 39-46 in S. Rose and L. Appignanesi, eds., Science and Beyond. Basil Blackwell, London, United Kingdom.

Mayr, E. 1982. Foreword. Pp. xi-xii in M. Ruse, Darwinism defended. Pearson Addison Wesley, Boston, Massachusetts.

McDonald, J. F. 1983. The molecular basis of adaptation: a critical review of relevant ideas and observations.--Annual Review of Ecology and Systematics 14:77-102.

McNiven, M. A. & K. R. Porter. 1992. The centrosome: contributions to cell form. Pp. 313-329 in V. I. Kalnins, ed., The centrosome. Academic Press, San Diego.

Meyer, S. C. 1991. Of clues and causes: a methodological interpretation of origin of life studies. Unpublished doctoral dissertation, University of Cambridge, Cambridge, United Kingdom.

______. 1998. DNA by design: an inference to the best explanation for the origin of biological information.--Rhetoric & Public Affairs, 1(4):519-555.

______. The scientific status of intelligent design: The methodological equivalence of naturalistic and non-naturalistic origins theories. Pp. 151-211 in Science and evidence for design in the universe. Proceedings of the Wethersfield Institute. Ignatius Press, San Francisco.

______. 2003. DNA and the origin of life: information, specification and explanation. Pp. 223-285 in J. A. Campbell and S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing, Michigan.

______. 2004. The Cambrian information explosion: evidence for intelligent design. Pp. 371-391 in W. A. Dembski and M. Ruse, eds., Debating design: from Darwin to DNA. Cambridge University Press, Cambridge, United Kingdom.

______, M. Ross, P. Nelson, & P. Chien. 2003. The Cambrian explosion: biology's big bang. Pp. 323-402 in J. A. Campbell & S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing. See also Appendix C: Stratigraphic first appearance of phyla body plans, pp. 593-598.

Miklos, G. L. G. 1993. Emergence of organizational complexities during metazoan evolution: perspectives from molecular biology, palaeontology and neo-Darwinism.--Mem. Ass. Australas. Palaeontols, 15:7-41.

Monastersky, R. 1993. Siberian rocks clock biological big bang.--Science News 144:148.

Moss, L. 2004. What genes can't do. The M.I.T. Press, Cambridge, Massachusetts.

Muller, G. B. & S. A. Newman. 2003. Origination of organismal form: the forgotten cause in evolutionary theory. Pp. 3-12 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

Nanney, D. L. 1983. The ciliates and the cytoplasm.--Journal of Heredity, 74:163-170.

Nelson, P., & J. Wells. 2003. Homology in biology: problem for naturalistic science and prospect for intelligent design. Pp. 303-322 in J. A. Campbell and S. C. Meyer, eds., Darwinism, design and public education. Michigan State University Press, Lansing.

Nijhout, H. F. 1990. Metaphors and the role of genes in development.--BioEssays 12:441-446.

Nusslein-Volhard, C., & E. Wieschaus. 1980. Mutations affecting segment number and polarity in Drosophila.--Nature 287:795-801.

Ohno, S. 1996. The notion of the Cambrian pananimalia genome.--Proceedings of the National Academy of Sciences, U.S.A. 93:8475-8478.

Orgel, L. E., & F. H. Crick. 1980. Selfish DNA: the ultimate parasite.--Nature 284:604-607.

Perutz, M. F., & H. Lehmann. 1968. Molecular pathology of human hemoglobin.--Nature 219:902-909.

Polanyi, M. 1967. Life transcending physics and chemistry.--Chemical and Engineering News, 45(35):54-66.

______. 1968. Life's irreducible structure.--Science 160:1308-1312, especially p. 1309.

Pourquie, O. 2003. Vertebrate somitogenesis: a novel paradigm for animal segmentation?--International Journal of Developmental Biology 47(7-8):597-603.

Quastler, H. 1964. The emergence of biological organization. Yale University Press, New Haven, Connecticut.

Raff, R. 1999. Larval homologies and radical evolutionary changes in early development, Pp. 110-121 in Homology. Novartis Symposium, vol. 222. John Wiley & Sons, Chichester, United Kingdom.

Reidhaar-Olson, J., & R. Sauer. 1990. Functionally acceptable solutions in two alpha-helical regions of lambda repressor.--Proteins, Structure, Function, and Genetics, 7:306-316.

Rutten, M. G. 1971. The origin of life by natural causes. Elsevier, Amsterdam.

Sapp, J. 1987. Beyond the gene. Oxford University Press, New York.

Sarkar, S. 1996. Biological information: a skeptical look at some central dogmas of molecular biology. Pp. 187-233 in S. Sarkar, ed., The philosophy and history of molecular biology: new perspectives. Kluwer Academic Publishers, Dordrecht.

Schutzenberger, M. 1967. Algorithms and the neo-Darwinian theory of evolution. Pp. 73-75 in P. S. Morehead and M. M. Kaplan, eds., Mathematical challenges to the Darwinian interpretation of evolution. Wistar Institute Symposium Monograph. Allen R. Liss, New York.

Shannon, C. 1948. A mathematical theory of communication.--Bell System Technical Journal 27:379-423, 623-656.

Shu, D. G., H. L. Loud, S. Conway Morris, X. L. Zhang, S. X. Hu, L. Chen, J. Han, M. Zhu, Y. Li, & L. Z. Chen. 1999. Lower Cambrian vertebrates from south China.--Nature 402:42-46.

Shubin, N. H., & C. R. Marshall. 2000. Fossils, genes, and the origin of novelty. Pp. 324-340 in Deep time. The Paleontological Society.

Simpson, G. 1970. Uniformitarianism: an inquiry into principle, theory, and method in geohistory and biohistory. Pp. 43-96 in M. K. Hecht and W. C. Steered, eds., Essays in evolution and genetics in honor of Theodosius Dobzhansky. Appleton-Century-Crofts, New York.

Sober, E. 2000. The philosophy of biology, 2nd edition. Westview Press, San Francisco.

Sonneborn, T. M. 1970. Determination, development, and inheritance of the structure of the cell cortex. In Symposia of the International Society for Cell Biology 9:1-13.

Sole, R. V., P. Fernandez, & S. A. Kauffman. 2003. Adaptive walks in a gene network model of morphogenesis: insight into the Cambrian explosion.--International Journal of Developmental Biology 47(7-8):685-693.

Stadler, B. M. R., P. F. Stadler, G. P. Wagner, & W. Fontana. 2001. The topology of the possible: formal spaces underlying patterns of evolutionary change.--Journal of Theoretical Biology 213:241-274.

Steiner, M., & R. Reitner. 2001. Evidence of organic structures in Ediacara-type fossils and associated microbial mats.--Geology 29(12):1119-1122.

Taylor, S. V., K. U. Walter, P. Kast, & D. Hilvert. 2001. Searching sequence space for protein catalysts.--Proceedings of the National Academy of Sciences, U.S.A. 98:10596-10601.

Thaxton, C. B., W. L. Bradley, & R. L. Olsen. 1992. The mystery of life's origin: reassessing current theories. Lewis and Stanley, Dallas, Texas.

Thompson, D. W. 1942. On growth and form, 2nd edition. Cambridge University Press, Cambridge, United Kingdom.

Thomson, K. S. 1992. Macroevolution: The morphological problem.--American Zoologist 32:106-112.

Valentine, J. W. 1995. Late Precambrian bilaterians: grades and clades. Pp. 87-107 in W. M. Fitch and F. J. Ayala, eds., Temporal and mode in evolution: genetics and paleontology 50 years after Simpson. National Academy Press, Washington, D.C.

______. 2004. On the origin of phyla. University of Chicago Press, Chicago, Illinois.

______, & D. H. Erwin, 1987. Interpreting great developmental experiments: the fossil record. Pp. 71-107 in R. A. Raff and E. C. Raff, eds., Development as an evolutionary process. Alan R. Liss, New York.

______, & D. Jablonski. 2003. Morphological and developmental macroevolution: a paleontological perspective.--International Journal of Developmental Biology 47:517-522.

Wagner, G. P. 2001. What is the promise of developmental evolution? Part II: A causal explanation of evolutionary innovations may be impossible.--Journal of Experimental Zoology (Mol. Dev. Evol.) 291:305-309.

______, & P. F. Stadler. 2003. Quasi-independence, homology and the Unity-C of type: a topological theory of characters.--Journal of Theoretical Biology 220:505-527.

Webster, G., & B. Goodwin. 1984. A structuralist approach to morphology.--Rivista di Biologia 77:503-10.

______, & ______. 1996. Form and transformation: generative and relational principles in biology. Cambridge University Press, Cambridge, United Kingdom.

Weiner, J. 1994. The beak of the finch. Vintage Books, New York.

Willmer, P. 1990. Invertebrate relationships: patterns in animal evolution. Cambridge University Press, Cambridge, United Kingdom.

______. 2003. Convergence and homoplasy in the evolution of organismal form. Pp. 33-50 in G. B. Muller and S. A. Newman, eds., Origination of organismal form: beyond the gene in developmental and evolutionary biology. The M.I.T. Press, Cambridge, Massachusetts.

Woese, C. 1998. The universal ancestor.--Proceedings of the National Academy of Sciences, U.S.A. 95:6854-6859.

Wray, G. A., J. S. Levinton, & L. H. Shapiro. 1996. Molecular evidence for deep Precambrian divergences among metazoan phyla.--Science 274:568-573.

Yockey, H. P. 1978. A calculation of the probability of spontaneous biogenesis by information theory.--Journal of Theoretical Biology 67:377-398.

______, 1992. Information theory and molecular biology, Cambridge University Press, Cambridge, United Kingdom.

End Notes


En Español (PDF)

PROCEEDINGS OF THE BIOLOGICAL SOCIETY OF WASHINGTON
117(2):213-239. 2004

The origin of biological information and the higher taxonomic categories
Stephen C. Meyer

(more…)

Abstract: For the scientific community intelligent design represents creationism's latest grasp at scientific legitimacy. Accordingly, intelligent design is viewed as yet another ill-conceived attempt by creationists to straightjacket science within a religious ideology. But in fact intelligent design can be formulated as a scientific theory having empirical consequences and devoid of religious commitments. Intelligent design can be unpacked as a theory of information. Within such a theory, information becomes a reliable indicator of design as well as a proper object for scientific investigation. In my paper I shall (1) show how information can be reliably detected and measured, and (2) formulate a conservation law that governs the origin and flow of information. My broad conclusion is that information is not reducible to natural causes, and that the origin of information is best sought in intelligent causes. Intelligent design thereby becomes a theory for detecting and measuring information, explaining its origin, and tracing its flow.


1. Information

In Steps Towards Life Manfred Eigen (1992, p. 12) identifies what he regards as the central problem facing origins-of-life research: "Our task is to find an algorithm, a natural law that leads to the origin of information." Eigen is only half right. To determine how life began, it is indeed necessary to understand the origin of information. Even so, neither algorithms nor natural laws are capable of producing information. The great myth of modern evolutionary biology is that information can be gotten on the cheap without recourse to intelligence. It is this myth I seek to dispel, but to do so I shall need to give an account of information. No one disputes that there is such a thing as information. As Keith Devlin (1991, p. 1) remarks, "Our very lives depend upon it, upon its gathering, storage, manipulation, transmission, security, and so on. Huge amounts of money change hands in exchange for information. People talk about it all the time. Lives are lost in its pursuit. Vast commercial empires are created in order to manufacture equipment to handle it." But what exactly is information? The burden of this paper is to answer this question, presenting an account of information that is relevant to biology.

What then is information? The fundamental intuition underlying information is not, as is sometimes thought, the transmission of signals across a communication channel, but rather, the actualization of one possibility to the exclusion of others. As Fred Dretske (1981, p. 4) puts it, "Information theory identifies the amount of information associated with, or generated by, the occurrence of an event (or the realization of a state of affairs) with the reduction in uncertainty, the elimination of possibilities, represented by that event or state of affairs." To be sure, whenever signals are transmitted across a communication channel, one possibility is actualized to the exclusion of others, namely, the signal that was transmitted to the exclusion of those that weren't. But this is only a special case. Information in the first instance presupposes not some medium of communication, but contingency. Robert Stalnaker (1984, p. 85) makes this point clearly: "Content requires contingency. To learn something, to acquire information, is to rule out possibilities. To understand the information conveyed in a communication is to know what possibilities would be excluded by its truth." For there to be information, there must be a multiplicity of distinct possibilities any one of which might happen. When one of these possibilities does happen and the others are ruled out, information becomes actualized. Indeed, information in its most general sense can be defined as the actualization of one possibility to the exclusion of others (observe that this definition encompasses both syntactic and semantic information).

This way of defining information may seem counterintuitive since we often speak of the information inherent in possibilities that are never actualized. Thus we may speak of the information inherent in flipping one-hundred heads in a row with a fair coin even if this event never happens. There is no difficulty here. In counterfactual situations the definition of information needs to be applied counterfactually. Thus to consider the information inherent in flipping one-hundred heads in a row with a fair coin, we treat this event/possibility as though it were actualized. Information needs to referenced not just to the actual world, but also cross-referenced with all possible worlds.

2. Complex Information

How does our definition of information apply to biology, and to science more generally? To render information a useful concept for science we need to do two things: first, show how to measure information; second, introduce a crucial distinction--the distinction between specified and unspecified information. First, let us show how to measure information. In measuring information it is not enough to count the number of possibilities that were excluded, and offer this number as the relevant measure of information. The problem is that a simple enumeration of excluded possibilities tells us nothing about how those possibilities were individuated in the first place. Consider, for instance, the following individuation of poker hands:

  • (i) A royal flush.
  • (ii) Everything else.

To learn that something other than a royal flush was dealt (i.e., possibility (ii)) is clearly to acquire less information than to learn that a royal flush was dealt (i.e., possibility (i)). Yet if our measure of information is simply an enumeration of excluded possibilities, the same numerical value must be assigned in both instances since in both instances a single possibility is excluded.

It follows, therefore, that how we measure information needs to be independent of whatever procedure we use to individuate the possibilities under consideration. And the way to do this is not simply to count possibilities, but to assign probabilities to these possibilities. For a thoroughly shuffled deck of cards, the probability of being dealt a royal flush (i.e., possibility (i)) is approximately .000002 whereas the probability of being dealt anything other than a royal flush (i.e., possibility (ii)) is approximately .999998. Probabilities by themselves, however, are not information measures. Although probabilities properly distinguish possibilities according to the information they contain, nonetheless probabilities remain an inconvenient way of measuring information. There are two reasons for this. First, the scaling and directionality of the numbers assigned by probabilities needs to be recalibrated. We are clearly acquiring more information when we learn someone was dealt a royal flush than when we learn someone wasn't dealt a royal flush. And yet the probability of being dealt a royal flush (i.e., .000002) is minuscule compared to the probability of being dealt something other than a royal flush (i.e., .999998). Smaller probabilities signify more information, not less.

The second reason probabilities are inconvenient for measuring information is that they are multiplicative rather than additive. If I learn that Alice was dealt a royal flush playing poker at Caesar's Palace and that Bob was dealt a royal flush playing poker at the Mirage, the probability that both Alice and Bob were dealt royal flushes is the product of the individual probabilities. Nonetheless, it is convenient for information to be measured additively so that the measure of information assigned to Alice and Bob jointly being dealt royal flushes equals the measure of information assigned to Alice being dealt a royal flush plus the measure of information assigned to Bob being dealt a royal flush.

Now there is an obvious way to transform probabilities which circumvents both these difficulties, and that is to apply a negative logarithm to the probabilities. Applying a negative logarithm assigns the more information to the less probability and, because the logarithm of a product is the sum of the logarithms, transforms multiplicative probability measures into additive information measures. What's more, in deference to communication theorists, it is customary to use the logarithm to the base 2. The rationale for this choice of logarithmic base is as follows. The most convenient way for communication theorists to measure information is in bits. Any message sent across a communication channel can be viewed as a string of 0's and 1's. For instance, the ASCII code uses strings of eight 0's and 1's to represent the characters on a typewriter, with whole words and sentences in turn represented as strings of such character strings. In like manner all communication may be reduced to the transmission of sequences of 0's and 1's. Given this reduction, the obvious way for communication theorists to measure information is in number of bits transmitted across a communication channel. And since the negative logarithm to the base 2 of a probability corresponds to the average number of bits needed to identify an event of that probability, the logarithm to the base 2 is the canonical logarithm for communication theorists. Thus we define the measure of information in an event of probability p as -log2p (see Shannon and Weaver, 1949, p. 32; Hamming, 1986; or indeed any mathematical introduction to information theory).

What about the additivity of this information measure? Recall the example of Alice being dealt a royal flush playing poker at Caesar's Palace and that Bob being dealt a royal flush playing poker at the Mirage. Let's call the first event A and the second B. Since randomly dealt poker hands are probabilistically independent, the probability of A and B taken jointly equals the product of the probabilities of A and B taken individually. Symbolically, P(A&B) = P(A)xP(B). Given our logarithmic definition of information we therefore define the amount of information in an event E as I(E) =def -log2P(E). It then follows that P(A&B) = P(A)xP(B) if and only if I(A&B) = I(A)+I(B). Since in the example of Alice and Bob P(A) = P(B) = .000002, I(A) = I(B) = 19, and I(A&B) = I(A)+I(B) = 19 + 19 = 38. Thus the amount of information inherent in Alice and Bob jointly obtaining royal flushes is 38 bits.

Since lots of events are probabilistically independent, information measures exhibit lots of additivity. But since lots of events are also correlated, information measures exhibit lots of non-additivity as well. In the case of Alice and Bob, Alice being dealt a royal flush is probabilistically independent of Bob being dealt a royal flush, and so the amount of information in Alice and Bob both being dealt royal flushes equals the sum of the individual amounts of information. But consider now a different example. Alice and Bob together toss a coin five times. Alice observes the first four tosses but is distracted, and so misses the fifth toss. On the other hand, Bob misses the first toss, but observes the last four tosses. Let's say the actual sequence of tosses is 11001 (1 = heads, 0 = tails). Thus Alice observes 1100* and Bob observes *1001. Let A denote the first observation, B the second. It follows that the amount of information in A&B is the amount of information in the completed sequence 11001, namely, 5 bits. On the other hand, the amount of information in A alone is the amount of information in the incomplete sequence 1100*, namely 4 bits. Similarly, the amount of information in B alone is the amount of information in the incomplete sequence *1001, also 4 bits. This time information doesn't add up: 5 = I(A&B) _ I(A)+I(B) = 4+4 = 8.

Here A and B are correlated. Alice knows all but the last bit of information in the completed sequence 11001. Thus when Bob gives her the incomplete sequence *1001, all Alice really learns is the last bit in this sequence. Similarly, Bob knows all but the first bit of information in the completed sequence 11001. Thus when Alice gives him the incomplete sequence 1100*, all Bob really learns is the first bit in this sequence. What appears to be four bits of information actually ends up being only one bit of information once Alice and Bob factor in the prior information they possess about the completed sequence 11001. If we introduce the idea of conditional information, this is just to say that 5 = I(A&B) = I(A)+I(B|A) = 4+1. I(B|A), the conditional information of B given A, is the amount of information in Bob's observation once Alice's observation is taken into account. And this, as we just saw, is 1 bit.

I(B|A), like I(A&B), I(A), and I(B), can be represented as the negative logarithm to the base two of a probability, only this time the probability under the logarithm is a conditional as opposed to an unconditional probability. By definition I(B|A) =def -log2P(B|A), where P(B|A) is the conditional probability of B given A. But since P(B|A) =def P(A&B)/P(A), and since the logarithm of a quotient is the difference of the logarithms, log2P(B|A) = log2P(A&B) - log2P(A), and so -log2P(B|A) = -log2P(A&B) + log2P(A), which is just I(B|A) = I(A&B) - I(A). This last equation is equivalent to

(*) I(A&B) = I(A)+I(B|A)

Formula (*) holds with full generality, reducing to I(A&B) = I(A)+I(B) when A and B are probabilistically independent (in which case P(B|A) = P(B) and thus I(B|A) = I(B)).

Formula (*) asserts that the information in both A and B jointly is the information in A plus the information in B that is not in A. Its point, therefore, is to spell out how much additional information B contributes to A. As such, this formula places tight constraints on the generation of new information. Does, for instance, a computer program, call it A, by outputting some data, call the data B, generate new information? Computer programs are fully deterministic, and so B is fully determined by A. It follows that P(B|A) = 1, and thus I(B|A) = 0 (the logarithm of 1 is always 0). From Formula (*) it therefore follows that I(A&B) = I(A), and therefore that the amount of information in A and B jointly is no more than the amount of information in A by itself.

For an example in the same spirit consider that there is no more information in two copies of Shakespeare's Hamlet than in a single copy. This is of course patently obvious, and any formal account of information had better agree. To see that our formal account does indeed agree, let A denote the printing of the first copy of Hamlet, and B the printing of the second copy. Once A is given, B is entirely determined. Indeed, the correlation between A and B is perfect. Probabilistically this is expressed by saying the conditional probability of B given A is 1, namely, P(B|A) = 1. In information-theoretic terms this is to say that I(B|A) = 0. As a result I(B|A) drops out of Formula (*), and so I(A&B) = I(A). Our information-theoretic formalism therefore agrees with our intuition that two copies of Hamlet contain no more information than a single copy.

Information is a complexity-theoretic notion. Indeed, as a purely formal object, the information measure described here is a complexity measure (cf. Dembski, 1998, ch. 4). Complexity measures arise whenever we assign numbers to degrees of complication. A set of possibilities will often admit varying degrees of complication, ranging from extremely simple to extremely complicated. Complexity measures assign non-negative numbers to these possibilities so that 0 corresponds to the most simple and _ to the most complicated. For instance, computational complexity is always measured in terms of either time (i.e., number of computational steps) or space (i.e., size of memory, usually measured in bits or bytes) or some combination of the two. The more difficult a computational problem, the more time and space are required to run the algorithm that solves the problem. For information measures, degree of complication is measured in bits. Given an event A of probability P(A), I(A) = -log2P(A) measures the number of bits associated with the probability P(A). We therefore speak of the "complexity of information" and say that the complexity of information increases as I(A) increases (or, correspondingly, as P(A) decreases). We also speak of "simple" and "complex" information according to whether I(A) signifies few or many bits of information. This notion of complexity is important to biology since not just the origin of information stands in question, but the origin of complex information.

3. Complex Specified Information

Given a means of measuring information and determining its complexity, we turn now to the distinction between specified and unspecified information. This is a vast topic whose full elucidation is beyond the scope of this paper (the details can be found in my monograph The Design Inference). Nonetheless, in what follows I shall try to make this distinction intelligible, and offer some hints on how to make it rigorous. For an intuitive grasp of the difference between specified and unspecified information, consider the following example. Suppose an archer stands 50 meters from a large blank wall with bow and arrow in hand. The wall, let us say, is sufficiently large that the archer cannot help but hit it. Consider now two alternative scenarios. In the first scenario the archer simply shoots at the wall. In the second scenario the archer first paints a target on the wall, and then shoots at the wall, squarely hitting the target's bull's-eye. Let us suppose that in both scenarios where the arrow lands is identical. In both scenarios the arrow might have landed anywhere on the wall. What's more, any place where it might land is highly improbable. It follows that in both scenarios highly complex information is actualized. Yet the conclusions we draw from these scenarios are very different. In the first scenario we can conclude absolutely nothing about the archer's ability as an archer, whereas in the second scenario we have evidence of the archer's skill.

The obvious difference between the two scenarios is of course that in the first the information follows no pattern whereas in the second it does. Now the information that tends to interest us as rational inquirers generally, and scientists in particular, is not the actualization of arbitrary possibilities which correspond to no patterns, but rather the actualization of circumscribed possibilities which do correspond to patterns. There's more. Patterned information, though a step in the right direction, still doesn't quite get us specified information. The problem is that patterns can be concocted after the fact so that instead of helping elucidate information, the patterns are merely read off already actualized information.

To see this, consider a third scenario in which an archer shoots at a wall. As before, we suppose the archer stands 50 meters from a large blank wall with bow and arrow in hand, the wall being so large that the archer cannot help but hit it. And as in the first scenario, the archer shoots at the wall while it is still blank. But this time suppose that after having shot the arrow, and finding the arrow stuck in the wall, the archer paints a target around the arrow so that the arrow sticks squarely in the bull's-eye. Let us suppose further that the precise place where the arrow lands in this scenario is identical with where it landed in the first two scenarios. Since any place where the arrow might land is highly improbable, in this as in the other scenarios highly complex information has been actualized. What's more, since the information corresponds to a pattern, we can even say that in this third scenario highly complex patterned information has been actualized. Nevertheless, it would be wrong to say that highly complex specified information has been actualized. Of the three scenarios, only the information in the second scenario is specified. In that scenario, by first painting the target and then shooting the arrow, the pattern is given independently of the information. On the other hand, in this, the third scenario, by first shooting the arrow and then painting the target around it, the pattern is merely read off the information.

Specified information is always patterned information, but patterned information is not always specified information. For specified information not just any pattern will do. We therefore distinguish between the "good" patterns and the "bad" patterns. The "good" patterns will henceforth be called specifications. Specifications are the independently given patterns that are not simply read off information. By contrast, the "bad" patterns will be called fabrications. Fabrications are the post hoc patterns that are simply read off already existing information.

Unlike specifications, fabrications are wholly unenlightening. We are no better off with a fabrication than without one. This is clear from comparing the first and third scenarios. Whether an arrow lands on a blank wall and the wall stays blank (as in the first scenario), or an arrow lands on a blank wall and a target is then painted around the arrow (as in the third scenario), any conclusions we draw about the arrow's flight remain the same. In either case chance is as good an explanation as any for the arrow's flight. The fact that the target in the third scenario constitutes a pattern makes no difference since the pattern is constructed entirely in response to where the arrow lands. Only when the pattern is given independently of the arrow's flight does a hypothesis other than chance come into play. Thus only in the second scenario does it make sense to ask whether we are dealing with a skilled archer. Only in the second scenario does the pattern constitute a specification. In the third scenario the pattern constitutes a mere fabrication.

The distinction between specified and unspecified information may now be defined as follows: the actualization of a possibility (i.e., information) is specified if independently of the possibility's actualization, the possibility is identifiable by means of a pattern. If not, then the information is unspecified. Note that this definition implies an asymmetry between specified and unspecified information: specified information cannot become unspecified information, though unspecified information may become specified information. Unspecified information need not remain unspecified, but can become specified as our background knowledge increases. For instance, a cryptographic transmission whose cryptosystem we have yet to break will constitute unspecified information. Yet as soon as we break the cryptosystem, the cryptographic transmission becomes specified information.

What is it for a possibility to be identifiable by means of an independently given pattern? A full exposition of specification requires a detailed answer to this question. Unfortunately, such an exposition is beyond the scope of this paper. The key conceptual difficulty here is to characterize the independence condition between patterns and information. This independence condition breaks into two subsidiary conditions: (1) a condition to stochastic conditional independence between the information in question and certain relevant background knowledge; and (2) a tractability condition whereby the pattern in question can be constructed from the aforementioned background knowledge. Although these conditions make good intuitive sense, they are not easily formalized. For the details refer to my monograph The Design Inference.

If formalizing what it means for a pattern to be given independently of a possibility is difficult, determining in practice whether a pattern is given independently of a possibility is much easier. If the pattern is given prior to the possibility being actualized--as in the second scenario above where the target was painted before the arrow was shot--then the pattern is automatically independent of the possibility, and we are dealing with specified information. Patterns given prior to the actualization of a possibility are just the rejection regions of statistics. There is a well-established statistical theory that describes such patterns and their use in probabilistic reasoning. These are clearly specifications since having been given prior to the actualization of some possibility, they have already been identified, and thus are identifiable independently of the possibility being actualized (cf. Hacking, 1965).

Many of the interesting cases of specified information, however, are those in which the pattern is given after a possibility has been actualized. This is certainly the case with the origin of life: life originates first and only afterwards do pattern-forming rational agents (like ourselves) enter the scene. It remains the case, however, that a pattern corresponding to a possibility, though formulated after the possibility has been actualized, can constitute a specification. Certainly this was not the case in the third scenario above where the target was painted around the arrow only after it hit the wall. But consider the following example. Alice and Bob are celebrating their fiftieth wedding anniversary. Their six children all show up bearing gifts. Each gift is part of a matching set of china. There is no duplication of gifts, and together the gifts constitute a complete set of china. Suppose Alice and Bob were satisfied with their old set of china, and had no inkling prior to opening their gifts that they might expect a new set of china. Alice and Bob are therefore without a relevant pattern whither to refer their gifts prior to actually receiving the gifts from their children. Nevertheless, the pattern they explicitly formulate only after receiving the gifts could be formed independently of receiving the gifts--indeed, we all know about matching sets of china and how to distinguish them from unmatched sets. This pattern therefore constitutes a specification. What's more, there is an obvious inference connected with this specification: Alice and Bob's children were in collusion, and did not present their gifts as random acts of kindness.

But what about the origin of life? Is life specified? If so, to what patterns does life correspond, and how are these patterns given independently of life's origin? Obviously, pattern-forming rational agents like ourselves don't enter the scene till after life originates. Nonetheless, there are functional patterns to which life corresponds, and which are given independently of the actual living systems. An organism is a functional system comprising many functional subsystems. The functionality of organisms can be cashed out in any number of ways. Arno Wouters (1995) cashes it out globally in terms of viability of whole organisms. Michael Behe (1996) cashes it out in terms of the irreducible complexity and minimal function of biochemical systems. Even the staunch Darwinist Richard Dawkins will admit that life is specified functionally, cashing out the functionality of organisms in terms of reproduction of genes. Thus Dawkins (1987, p. 9) will write: "Complicated things have some quality, specifiable in advance, that is highly unlikely to have been acquired by random chance alone. In the case of living things, the quality that is specified in advance is . . . the ability to propagate genes in reproduction."

Information can be specified. Information can be complex. Information can be both complex and specified. Information that is both complex and specified I call "complex specified information," or CSI for short. CSI is what all the fuss over information has been about in recent years, not just in biology, but in science generally. It is CSI that for Manfred Eigen constitutes the great mystery of biology, and one he hopes eventually to unravel in terms of algorithms and natural laws. It is CSI that for cosmologists underlies the fine-tuning of the universe, and which the various anthropic principles attempt to understand (cf. Barrow and Tipler, 1986). It is CSI that David Bohm's quantum potentials are extracting when they scour the microworld for what Bohm calls "active information" (cf. Bohm, 1993, pp. 35-38). It is CSI that enables Maxwell's demon to outsmart a thermodynamic system tending towards thermal equilibrium (cf. Landauer, 1991, p. 26). It is CSI on which David Chalmers hopes to base a comprehensive theory of human consciousness (cf. Chalmers, 1996, ch. 8). It is CSI that within the Kolmogorov-Chaitin theory of algorithmic information takes the form of highly compressible, non-random strings of digits (cf. Kolmogorov, 1965; Chaitin, 1966).

Nor is CSI confined to science. CSI is indispensable in our everyday lives. The 16-digit number on your VISA card is an example of CSI. The complexity of this number ensures that a would-be thief cannot randomly pick a number and have it turn out to be a valid VISA card number. What's more, the specification of this number ensures that it is your number, and not anyone else's. Even your phone number constitutes CSI. As with the VISA card number, the complexity ensures that this number won't be dialed randomly (at least not too often), and the specification ensures that this number is yours and yours only. All the numbers on our bills, credit slips, and purchase orders represent CSI. CSI makes the world go round. It follows that CSI is a rife field for criminality. CSI is what motivated the greedy Michael Douglas character in the movie Wall Street to lie, cheat, and steal. CSI's total and absolute control was the objective of the monomaniacal Ben Kingsley character in the movie Sneakers. CSI is the artifact of interest in most techno-thrillers. Ours is an information age, and the information that captivates us is CSI.

4. Intelligent Design

Whence the origin of complex specified information? In this section I shall argue that intelligent causation, or equivalently design, accounts for the origin of complex specified information. My argument focuses on the nature of intelligent causation, and specifically, on what it is about intelligent causes that makes them detectable. To see why CSI is a reliable indicator of design, we need to examine the nature of intelligent causation. The principal characteristic of intelligent causation is directed contingency, or what we call choice. Whenever an intelligent cause acts, it chooses from a range of competing possibilities. This is true not just of humans, but of animals as well as extra-terrestrial intelligences. A rat navigating a maze must choose whether to go right or left at various points in the maze. When SETI (Search for Extra-Terrestrial Intelligence) researchers attempt to discover intelligence in the extra-terrestrial radio transmissions they are monitoring, they assume an extra-terrestrial intelligence could have chosen any number of possible radio transmissions, and then attempt to match the transmissions they observe with certain patterns as opposed to others (patterns that presumably are markers of intelligence). Whenever a human being utters meaningful speech, a choice is made from a range of possible sound-combinations that might have been uttered. Intelligent causation always entails discrimination, choosing certain things, ruling out others.

Given this characterization of intelligent causes, the crucial question is how to recognize their operation. Intelligent causes act by making a choice. How then do we recognize that an intelligent cause has made a choice? A bottle of ink spills accidentally onto a sheet of paper; someone takes a fountain pen and writes a message on a sheet of paper. In both instances ink is applied to paper. In both instances one among an almost infinite set of possibilities is realized. In both instances a contingency is actualized and others are ruled out. Yet in one instance we infer design, in the other chance. What is the relevant difference? Not only do we need to observe that a contingency was actualized, but we ourselves need also to be able to specify that contingency. The contingency must conform to an independently given pattern, and we must be able independently to formulate that pattern. A random ink blot is unspecifiable; a message written with ink on paper is specifiable. Wittgenstein (1980, p. 1e) made the same point as follows: "We tend to take the speech of a Chinese for inarticulate gurgling. Someone who understands Chinese will recognize language in what he hears. Similarly I often cannot discern the humanity in man."

In hearing a Chinese utterance, someone who understands Chinese not only recognizes that one from a range of all possible utterances was actualized, but is also able to specify the utterance as coherent Chinese speech. Contrast this with someone who does not understand Chinese. In hearing a Chinese utterance, someone who does not understand Chinese also recognizes that one from a range of possible utterances was actualized, but this time, because lacking the ability to understand Chinese, is unable to specify the utterance as coherent speech. To someone who does not understand Chinese, the utterance will appear gibberish. Gibberish--the utterance of nonsense syllables uninterpretable within any natural language--always actualizes one utterance from the range of possible utterances. Nevertheless, gibberish, by corresponding to nothing we can understand in any language, also cannot be specified. As a result, gibberish is never taken for intelligent communication, but always for what Wittgenstein calls "inarticulate gurgling."

The actualization of one among several competing possibilities, the exclusion of the rest, and the specification of the possibility that was actualized encapsulates how we recognize intelligent causes, or equivalently, how we detect design. Actualization-Exclusion-Specification, this triad constitutes a general criterion for detecting intelligence, be it animal, human, or extra-terrestrial. Actualization establishes that the possibility in question is the one that actually occurred. Exclusion establishes that there was genuine contingency (i.e., that there were other live possibilities, and that these were ruled out). Specification establishes that the actualized possibility conforms to a pattern given independently of its actualization.

Now where does choice, which we've cited as the principal characteristic of intelligent causation, figure into this criterion? The problem is that we never witness choice directly. Instead, we witness actualizations of contingency which might be the result of choice (i.e., directed contingency), but which also might be the result of chance (i.e., blind contingency). Now there is only one way to tell the difference--specification. Specification is the only means available to us for distinguishing choice from chance, directed contingency from blind contingency. Actualization and exclusion together guarantee we are dealing with contingency. Specification guarantees we are dealing with a directed contingency. The Actualization-Exclusion-Specification triad is therefore precisely what we need to identify choice and therewith intelligent causation.

Psychologists who study animal learning and behavior have known of the Actualization-Exclusion-Specification triad all along, albeit implicitly. For these psychologists--known as learning theorists--learning is discrimination (cf. Mazur, 1990; Schwartz, 1984). To learn a task an animal must acquire the ability to actualize behaviors suitable for the task as well as the ability to exclude behaviors unsuitable for the task. Moreover, for a psychologist to recognize that an animal has learned a task, it is necessary not only to observe the animal making the appropriate behavior, but also to specify this behavior. Thus to recognize whether a rat has successfully learned how to traverse a maze, a psychologist must first specify the sequence of right and left turns that conducts the rat out of the maze. No doubt, a rat randomly wandering a maze also discriminates a sequence of right and left turns. But by randomly wandering the maze, the rat gives no indication that it can discriminate the appropriate sequence of right and left turns for exiting the maze. Consequently, the psychologist studying the rat will have no reason to think the rat has learned how to traverse the maze. Only if the rat executes the sequence of right and left turns specified by the psychologist will the psychologist recognize that the rat has learned how to traverse the maze. Now it is precisely the learned behaviors we regard as intelligent in animals. Hence it is no surprise that the same scheme for recognizing animal learning recurs for recognizing intelligent causes generally, to wit, actualization, exclusion, and specification.

Now this general scheme for recognizing intelligent causes coincides precisely with how we recognize complex specified information: First, the basic precondition for information to exist must hold, namely, contingency. Thus one must establish that any one of a multiplicity of distinct possibilities might obtain. Next, one must establish that the possibility which was actualized after the others were excluded was also specified. So far the match between this general scheme for recognizing intelligent causation and how we recognize complex specified information is exact. Only one loose end remains--complexity. Although complexity is essential to CSI (corresponding to the first letter of the acronym), its role in this general scheme for recognizing intelligent causation is not immediately evident. In this scheme one among several competing possibilities is actualized, the rest are excluded, and the possibility which was actualized is specified. Where in this scheme does complexity figure in?

The answer is that it is there implicitly. To see this, consider again a rat traversing a maze, but now take a very simple maze in which two right turns conduct the rat out of the maze. How will a psychologist studying the rat determine whether it has learned to exit the maze. Just putting the rat in the maze will not be enough. Because the maze is so simple, the rat could by chance just happen to take two right turns, and thereby exit the maze. The psychologist will therefore be uncertain whether the rat actually learned to exit this maze, or whether the rat just got lucky. But contrast this now with a complicated maze in which a rat must take just the right sequence of left and right turns to exit the maze. Suppose the rat must take one hundred appropriate right and left turns, and that any mistake will prevent the rat from exiting the maze. A psychologist who sees the rat take no erroneous turns and in short order exit the maze will be convinced that the rat has indeed learned how to exit the maze, and that this was not dumb luck. With the simple maze there is a substantial probability that the rat will exit the maze by chance; with the complicated maze this is exceedingly improbable. The role of complexity in detecting design is now clear since improbability is precisely what we mean by complexity (cf. section 2).

This argument for showing that CSI is a reliable indicator of design may now be summarized as follows: CSI is a reliable indicator of design because its recognition coincides with how we recognize intelligent causation generally. In general, to recognize intelligent causation we must establish that one from a range of competing possibilities was actualized, determine which possibilities were excluded, and then specify the possibility that was actualized. What's more, the competing possibilities that were excluded must be live possibilities, sufficiently numerous so that specifying the possibility that was actualized cannot be attributed to chance. In terms of probability, this means that the possibility that was specified is highly improbable. In terms of complexity, this means that the possibility that was specified is highly complex. All the elements in the general scheme for recognizing intelligent causation (i.e., Actualization-Exclusion-Specification) find their counterpart in complex specified information--CSI. CSI pinpoints what we need to be looking for when we detect design.

As a postscript, I call the reader's attention to the etymology of the word "intelligent." The word "intelligent" derives from two Latin words, the preposition inter, meaning between, and the verb lego, meaning to choose or select. Thus according to its etymology, intelligence consists in choosing between. It follows that the etymology of the word "intelligent" parallels the formal analysis of intelligent causation just given. "Intelligent design" is therefore a thoroughly apt phrase, signifying that design is inferred precisely because an intelligent cause has done what only an intelligent cause can do--make a choice.

5. The Law of the Conversation of Information

Evolutionary biology has steadfastly resisted attributing CSI to intelligent causation. Although Manfred Eigen recognizes that the central problem of evolutionary biology is the origin of CSI, he has no thought of attributing CSI to intelligent causation. According to Eigen natural causes are adequate to explain the origin of CSI. The only question for Eigen is which natural causes explain the origin of CSI. The logically prior question of whether natural causes are even in-principle capable of explaining the origin of CSI he ignores. And yet it is a question that undermines Eigen's entire project. Natural causes are in-principle incapable of explaining the origin of CSI. To be sure, natural causes can explain the flow of CSI, being ideally suited for transmitting already existing CSI. What natural causes cannot do, however, is originate CSI. This strong proscriptive claim, that natural causes can only transmit CSI but never originate it, I call the Law of Conservation of Information. It is this law that gives definite scientific content to the claim that CSI is intelligently caused. The aim of this last section is briefly to sketch the Law of Conservation of Information (a full treatment will be given in Uncommon Descent, a book I am jointly authoring with Stephen Meyer and Paul Nelson).

To see that natural causes cannot account for CSI is straightforward. Natural causes comprise chance and necessity (cf. Jacques Monod's book by that title). Because information presupposes contingency, necessity is by definition incapable of producing information, much less complex specified information. For there to be information there must be a multiplicity of live possibilities, one of which is actualized, and the rest of which are excluded. This is contingency. But if some outcome B is necessary given antecedent conditions A, then the probability of B given A is one, and the information in B given A is zero. If B is necessary given A, Formula (*) reduces to I(A&B) = I(A), which is to say that B contributes no new information to A. It follows that necessity is incapable of generating new information. Observe that what Eigen calls "algorithms" and "natural laws" fall under necessity.

Since information presupposes contingency, let us take a closer look at contingency. Contingency can assume only one of two forms. Either the contingency is a blind, purposeless contingency--which is chance; or it is a guided, purposeful contingency--which is intelligent causation. Since we already know that intelligent causation is capable of generating CSI (cf. section 4), let us next consider whether chance might also be capable of generating CSI. First notice that pure chance, entirely unsupplemented and left to its own devices, is incapable of generating CSI. Chance can generate complex unspecified information, and chance can generate non-complex specified information. What chance cannot generate is information that is jointly complex and specified.

Biologists by and large do not dispute this claim. Most agree that pure chance--what Hume called the Epicurean hypothesis--does not adequately explain CSI. Jacques Monod (1972) is one of the few exceptions, arguing that the origin of life, though vastly improbable, can nonetheless be attributed to chance because of a selection effect. Just as the winner of a lottery is shocked at winning, so we are shocked to have evolved. But the lottery was bound to have a winner, and so too something was bound to have evolved. Something vastly improbable was bound to happen, and so, the fact that it happened to us (i.e., that we were selected--hence the name selection effect) does not preclude chance. This is Monod's argument and it is fallacious. It fails utterly to come to grips with specification. Moreover, it confuses a necessary condition for life's existence with its explanation. Monod's argument has been refuted by the philosophers John Leslie (1989), John Earman (1987), and Richard Swinburne (1979). It has also been refuted by the biologists Francis Crick (1981, ch. 7), Bernd-Olaf Küppers (1990, ch. 6), and Hubert Yockey (1992, ch. 9). Selection effects do nothing to render chance an adequate explanation of CSI.

Most biologists therefore reject pure chance as an adequate explanation of CSI. The problem here is not simply one of faulty statistical reasoning. Pure chance is also scientifically unsatisfying as an explanation of CSI. To explain CSI in terms of pure chance is no more instructive than pleading ignorance or proclaiming CSI a mystery. It is one thing to explain the occurrence of heads on a single coin toss by appealing to chance. It is quite another, as Küppers (1990, p. 59) points out, to follow Monod and take the view that "the specific sequence of the nucleotides in the DNA molecule of the first organism came about by a purely random process in the early history of the earth." CSI cries out for explanation, and pure chance won't do. As Richard Dawkins (1987, p. 139) correctly notes, "We can accept a certain amount of luck in our [scientific] explanations, but not too much."

If chance and necessity left to themselves cannot generate CSI, is it possible that chance and necessity working together might generate CSI? The answer is No. Whenever chance and necessity work together, the respective contributions of chance and necessity can be arranged sequentially. But by arranging the respective contributions of chance and necessity sequentially, it becomes clear that at no point in the sequence is CSI generated. Consider the case of trial-and-error (trial corresponds to necessity and error to chance). Once considered a crude method of problem solving, trial-and-error has so risen in the estimation of scientists that it is now regarded as the ultimate source of wisdom and creativity in nature. The probabilistic algorithms of computer science (e.g., genetic algorithms--see Forrest, 1993) all depend on trial-and-error. So too, the Darwinian mechanism of mutation and natural selection is a trial-and-error combination in which mutation supplies the error and selection the trial. An error is committed after which a trial is made. But at no point is CSI generated.

Natural causes are therefore incapable of generating CSI. This broad conclusion I call the Law of Conservation of Information, or LCI for short. LCI has profound implications for science. Among its corollaries are the following: (1) The CSI in a closed system of natural causes remains constant or decreases. (2) CSI cannot be generated spontaneously, originate endogenously, or organize itself (as these terms are used in origins-of-life research). (3) The CSI in a closed system of natural causes either has been in the system eternally or was at some point added exogenously (implying that the system though now closed was not always closed). (4) In particular, any closed system of natural causes that is also of finite duration received whatever CSI it contains before it became a closed system.

This last corollary is especially pertinent to the nature of science for it shows that scientific explanation is not coextensive with reductive explanation. Richard Dawkins, Daniel Dennett, and many scientists are convinced that proper scientific explanations must be reductive, moving from the complex to the simple. Thus Dawkins (1987, p. 316) will write, "The one thing that makes evolution such a neat theory is that it explains how organized complexity can arise out of primeval simplicity." Thus Dennett (1995, p. 153) will view any scientific explanation that moves from simple to complex as "question-begging." Thus Dawkins (1987, p. 13) will explicitly equate proper scientific explanation with what he calls "hierarchical reductionism," according to which "a complex entity at any particular level in the hierarchy of organization" must properly be explained "in terms of entities only one level down the hierarchy." While no one will deny that reductive explanation is extremely effective within science, it is hardly the only type of explanation available to science. The divide-and-conquer mode of analysis behind reductive explanation has strictly limited applicability within science. In particular, this mode of analysis is utterly incapable of making headway with CSI. CSI demands an intelligent cause. Natural causes will not do.


William A. Dembski, presented at Naturalism, Theism and the Scientific Enterprise: An Interdisciplinary Conference at the University of Texas, Feb. 20-23, 1997.


References

Barrow, John D. and Frank J. Tipler. 1986. The Anthropic Cosmological Principle. Oxford: Oxford University Press.
Behe, Michael. 1996. Darwin's Black Box: The Biochemical Challenge to Evolution. New York: The Free Press.
Bohm, David. 1993. The Undivided Universe: An Ontological Interpretation of Quantum Theory. London: Routledge.
Chaitin, Gregory J. 1966. On the Length of Programs for Computing Finite Binary Sequences. Journal of the ACM, 13:547-569.
Chalmers, David J. 1996. The Conscious Mind: In Search of a Fundamental Theory. New York : Oxford University Press.
Crick, Francis. 1981. Life Itself: Its Origin and Nature. New York: Simon and Schuster.
Dawkins, Richard. 1987. The Blind Watchmaker. New York: Norton.
Dembski, William A. 1998. The Design Inference: Eliminating Chance through Small Probabilities. Forthcoming, Cambridge University Press.
Dennett, Daniel C. 1995. Darwin's Dangerous Idea: Evolution and the Meanings of Life. New York: Simon & Schuster.
Devlin, Keith J. 1991. Logic and Information. New York: Cambridge University Press.
Dretske, Fred I. 1981. Knowledge and the Flow of Information. Cambridge, Mass.: MIT Press.
Earman, John. 1987. The Sap Also Rises: A Critical Examination of the Anthropic Principle. American Philosophical Quarterly, 24(4): 307­317.
Eigen, Manfred. 1992. Steps Towards Life: A Perspective on Evolution, translated by Paul Woolley. Oxford: Oxford University Press.
Forrest, Stephanie. 1993. Genetic Algorithms: Principles of Natural Selection Applied to Computation. Science, 261:872-878.
Hacking, Ian. 1965. Logic of Statistical Inference. Cambridge: Cambridge University Press.
Hamming, R. W. 1986. Coding and Information Theory, 2nd edition. Englewood Cliffs, N. J.: Prentice-Hall.
Kolmogorov, Andrei N. 1965. Three Approaches to the Quantitative Definition of Information. Problemy Peredachi Informatsii (in translation), 1(1): 3-11.
Küppers, Bernd-Olaf. 1990. Information and the Origin of Life. Cambridge, Mass.: MIT Press.
Landauer, Rolf. 1991. Information is Physical. Physics Today, May: 23­29.
Leslie, John. 1989. Universes. London: Routledge.
Mazur, James. E. 1990. Learning and Behavior, 2nd edition. Englewood Cliffs, N.J.: Prentice Hall.
Monod, Jacques. 1972. Chance and Necessity. New York: Vintage.
Schwartz, Barry. 1984. Psychology of Learning and Behavior, 2nd edition. New York: Norton.
Shannon, Claude E. and W. Weaver. 1949. The Mathematical Theory of Communication. Urbana, Ill.: University of Illinois Press.
Stalnaker, Robert. 1984. Inquiry. Cambridge, Mass.: MIT Press.
Swinburne, Richard. 1979. The Existence of God. Oxford: Oxford University Press.
Wittgenstein, Ludwig. 1980. Culture and Value, edited by G. H. von Wright, translated by P. Winch. Chicago: University of Chicago Press.
Wouters, Arno. 1995. Viability Explanation. Biology and Philosophy, 10:435-457.
Yockey, Hubert P. 1992. Information Theory and Molecular Biology. Cambridge: Cambridge University Press.

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