Critics of intelligent design often mistakenly charge the design inference with being an argument from ignorance (i.e., arguing for design based on our ignorance of chance alternatives). In fact, a design inference, by ruling out relevant chance hypotheses, engages in an eliminative induction, whose logic is sound and differs from an argument from ignorance. Eliminative induction is a method of reasoning used in science and philosophy to support a hypothesis by systematically eliminating competing hypotheses. The principle underlying eliminative induction is that if all alternative hypotheses can be falsified or shown to be less likely, then the remaining hypothesis (a design hypothesis in this case) gains credibility and support. Thus, as alternative hypotheses are tested and rejected, the likelihood of the remaining hypothesis being correct increases. This approach is especially useful in situations where direct evidence for a hypothesis is unavailable (as with a design hypothesis in a typical design inference). While this method of reasoning is able to strengthen support for the remaining hypothesis, it cannot guarantee its truth, as there could always be other hypotheses or explanations that have not yet been considered or discovered. Leaving aside strict deductive reasoning, fallibility of this sort is a feature of all human reasoning. Eliminative inductions rely on the successful falsification of competing hypotheses. Their strength therefore increases to the degree that they effectively eliminate such competition. The problem with eliminative inductions in practice, however, is that we don’t have a neat way of organizing competitors so that they can be eliminated with a few manageable blows. As philosopher of science John Earman puts it, The eliminative inductivist [seems to be] in a position analogous to that of Zeno’s archer whose arrow can never reach the target, for faced with an infinite number of hypotheses, he can eliminate one, then two, then three, etc., but no matter how long he labors, he will never get down to just one. Indeed, it is as if the arrow never gets half way, or a quarter way, etc. to the target, since however long the eliminativist labors, he will always be faced with an infinite list [of remaining hypotheses to eliminate].24 Earman then immediately answers this objection: My response on behalf of the eliminativist has two parts. (1) Elimination need not proceed in such a plodding fashion, for the alternatives may be so ordered that an infinite number can be eliminated in one blow. (2) Even if we never get down to a single hypothesis, progress occurs if we succeed in eliminating finite or infinite chunks of the possibility space.25 The key word in Earman’s remarks here about eliminative induction is progress. Design inferences, as eliminative inductions, make progress in advancing our understanding of biological origins. To deny design inferences is to stymie progress, leaving biology in a holding pattern that reflexively invokes non-design naturalistic explanations even though these explanations come up short time after time. Much more can be said in defense against the argument-from-ignorance objection, but we will not belabor the issue here. Suffice it to say that design theorists have responded cogently and at length to this objection.26 There is, however, an argument from ignorance in play in the Darwinism/design controversy. It lies not with design theorists, but with Darwinian evolutionists who struggle in vain to explain how natural selection could have produced the biological systems that lead design theorists to infer design. For example, cell biologist Franklin Harold, who is not a supporter of Michael Behe, states, “We should reject, as a matter of principle, the substitution of intelligent design for the dialogue of chance and necessity.” However, the basis of this principle and why it should be followed he leaves unanswered. Indeed, the principle becomes implausible given what Harold says next: “But we must concede that there are presently no detailed Darwinian accounts of the evolution of any biochemical or cellular system, only a variety of wishful speculations.”27 Harold made this remark back in 2001 and in doing so explicitly cited Behe. As we will discuss later in this chapter, such wishful speculations by Darwinists persist, and the design-related challenges facing biology continue to grow. In inflating natural selection’s prior probability and in dismissing design inferences as arguments from ignorance, mainstream evolutionary biologists assume no burden of proof. Instead, whenever a design inference for the emergence of a biological system threatens to be drawn, they invoke unidentified and indeed unidentifiable chance hypotheses that render the probability of its emergence high enough to avoid a design inference. They do this not by actually exhibiting such chance hypotheses (that’s why they are unidentified and even unidentifiable) but by an act of faith in the wonder-working power of natural selection. Lowering the Bayesian prior in natural selection blocks this maneuver and opens the way for design inferences in biology that would otherwise get blocked. Before turning to actual limitations and shortcomings of the Darwinian selection mechanism with the aim of establishing a more sensible Bayesian prior, we need to make a general point about Bayesian probabilities. What is relevant to determining the prior probability of a hypothesis in general, and of natural selection in particular, is the evidence supporting it, which includes everything from its conceptual soundness to its empirical adequacy. But it does not include the mere feeling of confidence in a hypothesis or the intensity of that feeling. Biologist and Nobel laureate Peter Medawar put it best: “I cannot give any scientist of any age better advice than this: the intensity of the conviction that a hypothesis is true has no bearing on whether it is true or not. The importance of the strength of our conviction is only to provide a proportionately strong incentive to find out if the hypothesis will stand up to critical evaluation.”28
Dembski, William A.. The Design Inference: Eliminating Chance through Small Probabilities (pp. 388-392). (Function). Kindle Edition.
