I am reviewing Jason Rosenhouse’s new book, The Failures of Mathematical Anti-Evolutionism (Cambridge University Press), serially. For the full series so far, go here.
The publication of Rosenhouse’s book comes at an opportune time for me. That is because I’m currently working on a second edition of The Design Inference (co-authored with my colleague Winston Ewert, and due out for a 25th anniversary edition in 2023 — the first edition appeared in 1998). Like Rosenhouse’s book, The Design Inference was published by Cambridge University Press. Unlike it, The Design Inference appeared in a statistical monograph series (Cambridge Studies in Probability, Induction, and Decision Theory), and thus constituted a full-scale technical treatise rather than a popular exposition, as with Rosenhouse’s book. At the risk of immodesty, I’ll venture that without The Design Inference, Rosenhouse’s book would never have been written.
So, what is the upshot of The Design Inference, and why is Rosenhouse so dead set against not only it but also the subsequent mathematical ideas and research that it inspired? The Design Inference purports to provide a reliable statistical method for uncovering the effects of intelligent causes and teasing them apart from unintelligent causes (i.e., chance, necessity, and their combination, as exemplified in the Darwinian mechanism of natural selection acting on random variations). If this method could legitimately be applied to biological systems, it might, potentially, undercut the credibility of Darwinian processes to produce biological innovation. The mere possibility that The Design Inference could pose a threat to Darwinism is, however, too much for Rosenhouse and fellow Darwinists.
What Is That Method?
Briefly, the design inference (the method rather than the book) identifies two features as essential for eliminating chance: improbability and specification. If something is not improbable, then it could readily happen by chance (think of tossing three heads in a row, which has probability of ⅛, and is thus quite likely — no one would think this result beyond the reach of chance). Even so, highly improbable things happen. In fact, just about anything that happens is highly improbable. Toss a coin a thousand times, and you’ll witness an event of probability less than 1 in 10^300. Ordinarily, you’ll attribute it to chance.
But you won’t attribute that observed sequence to chance if it exhibits a salient pattern. It might be all heads, or it might correspond to the expansion of π, or it might spell out in Unicode (treating tails as 0 and heads as 1) the first lines of Shakespeare’s Hamlet. Such salient patterns are called specifications. Their defining characteristic is that they have short descriptions (more on this later). Specifications together with improbability eliminate chance, and in some cases actually sweep the field clear of chance hypotheses, in which case they warrant a design inference. That’s the design inferential method in a nutshell. I’ll expand on it later in this series in the discussion of specified complexity.
The Darwinist Logic
In any case, the Darwinist logic for dismissing The Design Inference and its design inferential method is instructive. Methods are what they are. Methods don’t care where they’re applied. If something is a bona-fide method, it does not bias or prejudge the outcome of applying the method. When The Design Inference was first published, it received enthusiastic endorsements from a wide cross-section of scientists and scholars. That initial enthusiasm, however, abated among evolutionary naturalists once my views on intelligent design became clear. Take, as an example of early enthusiasm, the following endorsement of The Design Inference by Bill Wimsatt, an evolutionary naturalist and philosopher of biology at the University of Chicago (an endorsement removed by the publisher, for whatever reason, from the back cover of the paperback edition):
Dembski has written a sparklingly original book. Not since David Hume’s Dialogues Concerning Natural Religion has someone taken such a close look at the design argument, but it is done now in a much broader post-Darwinian context. Now we proceed with modern characterizations of probability and complexity, and the results bear fundamentally on notions of randomness and on strategies for dealing with the explanation of radically improbable events. We almost forget that design arguments are implicit in criminal arguments “beyond a reasonable doubt,” plagiarism, phylogenetic inference, cryptography, and a host of other modern contexts. Dembski’s analysis of randomness is the most sophisticated to be found in the literature, and his discussions are an important contribution to the theory of explanation, and a timely discussion of a neglected and unanticipatedly important topic.
Wimsatt admits that the method is widely applicable. In consequence, if this method could legitimately be applied to Darwinian evolutionary processes, that could pose a threat to Darwinism itself. Maybe the method would give results acceptable to Darwinists: “We’ve applied the design inferential method to Darwinian evolutionary processes, and invariably found that design could not be convincingly inferred.” Fair enough, that could be one outcome. But what if the method instead yielded: “We’ve applied the design inferential method to Darwinian evolutionary processes, and found that at least in some instances design could be convincingly inferred.” Even that possibility was a bridge too far for Darwinists. Note that to show design in biology, it’s not necessary to show that every aspect of biological systems is designed. Even one unequivocal case of design in biology would be enough. Darwinists maintain that all biological systems give no evidence of design. To refute this claim, logic only requires showing that some biological system gives evidence of design.
The bottom line is that the very method developed in The Design Inference needed to be invalidated. Critics got busy on that work of invalidation shortly after The Design Inference was published (e.g., Elliott Sober). And a new generation of critics continues these efforts to this day (e.g., Joshua Swamidass). I’ve even seen a book review of The Design Inference, written well over a decade after its publication, disparaging it (see James Bradley’s 2010 review for BioLogos). If the very method described in the book is misconceived, then there’s no need to worry about its application to biology, or to anything else for that matter. Rosenhouse’s aspiration for The Failures of Mathematical Anti-Evolutionism is that it will definitively invalidate The Design Inference and subsequent work inspired by it.
Next, “The Challenge from Jason Rosenhouse.”
Editor’s note: This review is cross-posted with permission of the author from BillDembski.com.