INFERENCE TO THE BEST EXPLANATION

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. One of the central aims of the philosophy of science is to give a principled account of these judgments and inferences connecting evidence to theory. In the deductive case, this project is well-advanced, thanks to a productive stream of research into the structure of deductive argument that stretches back to antiquity. The same cannot be said for inductive inferences. Although some of the central problems were presented incisively by David Hume in the eighteenth century, our current understanding of inductive reasoning remains remarkably poor, in spite of the intense efforts of numerous epistemologists and philosophers of science. The model of inference to the best explanation (IBE) is designed to give a partial account of many inductive inferences, both in science and in ordinary life. One version of the model was developed under the name “abduction” by Charles Sanders Peirce early in the twentieth century, and the model has been considerably developed and discussed over the last four decades (e.g., Harman 1965; Thagard 1978; Day and kincaid 1994; Barnes 1995; Psillos 2002; Lipton 2004). Its governing idea is that explanatory considerations are a guide to inference, that scientists infer from the available evidence to the hypothesis which would, if correct, best explain that evidence. Many inferences

are naturally described in this way. Darwin inferred the hypothesis of natural selection because, although it was not entailed by his biological evidence, natural selection would provide the best explanation of that evidence. When an astronomer infers that a galaxy is receding from the earth with a specified velocity, she does this because the recession would be the best explanation of the observed red-shift of the galaxy’s spectrum. When a detective infers that it was Moriarty who committed the crime, he does so because that hypothesis would best explain the fingerprints, bloodstains, and other forensic evidence. Sherlock Holmes to the contrary, this is not a matter of deduction. The evidence will not entail that Moriarty is to blame, since it always remains possible that someone else was the perpetrator. Nevertheless, Holmes is right to make his inference, since Moriarty’s guilt would provide a better explanation of the evidence than would anyone else’s. IBE can be seen as an extension of the idea of self-evidencing explanations, where the phenomenon that is explained in turn provides an essential part of the reason for believing that the explanation is correct. The galaxy’s speed of recession explains why its spectrum is red-shifted by a specified amount, but the observed red-shift may be an essential part of the reason the astronomer has for believing that the galaxy is receding at that speed. Self-evidencing explanations exhibit a curious circularity, but this circularity is benign. The recession is used to explain the red-shift and the red-shift is used to confirm the recession; this reciprocal relationship may leave the recession hypothesis both explanatory and well-supported. According to IBE, this is a common situation in science: hypotheses are supported by the very observations they are supposed to explain. Moreover, on this model, the observations support the hypothesis precisely because it would explain them. IBE thus partially inverts an otherwise natural view of the relationship between inference and explanation. According to that natural view, inference is prior to explanation. First the scientist must decide which hypotheses to accept; then, when called on to explain some observation, she will draw from her pool of accepted hypotheses. According to IBE, by contrast, it is only by asking how well various hypotheses would explain the available evidence that she can determine which hypotheses merit acceptance. In this sense, IBE has it that explanation is prior to inference. Here it is important to distinguish between actual and potential explanation, where a potential explanation is something that satisfies all the conditions on actual explanation, with the possible exception of truth. Thus all actual explanations are potential explanations, but not conversely. Stories of alien abduction might explain certain observations – to that extent they are potential explanations – but they are not actual explanations because they are not true. According to IBE, we infer that what would best explain our evidence is likely to be true, that is, that the best potential explanation is likely to be an actual explanation. There are two different sorts of problem that an account of inference in science might purport to solve. The problem of description is to give an account of the principles that govern the way scientists weigh evidence and make inferences. The problem of justification is to show that those principles are sound or rational, for example, by showing that they tend to lead scientists to accept hypotheses that are true and to reject those that are false. One popular application of IBE has been the

attempt to mount a philosophical inference to the best explanation to justify scientific realism, arguing that the truth of certain scientific theories, and so the reliability of scientific methods, would be the best explanation of their predictive successes. I return briefly to this justificatory gambit at the end of this essay, but my main focus is on the descriptive problem: not whether letting inferences be governed in part by explanatory considerations would be a good way to think, but whether, for better or worse, scientists do think that way. The difficulties of the descriptive problem are sometimes underrated, because it is supposed that inductive reasoning follows a simple pattern of extrapolation, with more of the same as its fundamental principle. Thus we predict that the sun will rise tomorrow because it has risen every day in the past, or that all ravens are black because all observed ravens are black. This picture of enumerative induction has, however, been shown to be strikingly inadequate as an account of inference in science. On the one hand, a series of formal arguments, most notably the raven paradox and the new riddle of induction, have shown that the enumerative model is wildly permissive, treating virtually any observation as if it were evidence for any hypothesis or prediction (Hempel 1965: Ch. 1; Goodman 1983: Ch. 3). On the other hand, the enumerative model is also much too restrictive to account for most scientific inferences. Scientific hypotheses typically appeal to entities and processes not mentioned in the evidence that supports them and often unobservable and not merely unobserved, so the principle of more of the same does not apply. For example, while the enumerative model might account for the inference that a scientist makes from the observation that the light from one galaxy is red-shifted to the conclusion that the light from another galaxy will be red-shifted as well, it will not account for the inference from observed red-shift to unobserved recession. The best-known attempt to account for these vertical inferences that scientists make from observations to hypotheses about often unobservable entities and processes is the hypothetico-deductive model (Hempel 1966: Chs 2-3). According to this model, scientists deduce predictions from a hypothesis (along with various other auxiliary premises) and then determine whether those predictions are correct. If some of them are not, the hypothesis is disconfirmed; if all of them are correct, the hypothesis is confirmed and may eventually be inferred. Unfortunately, while this model does make room for vertical inferences, it remains (like the enumerative model) far too permissive, counting data as confirming a hypothesis which are in fact totally irrelevant to it. For example, since a hypothesis (H) entails the disjunction of itself and any prediction whatever (H or P), and the truth of the prediction establishes the truth of the disjunction (since P also entails (H or P)), any successful prediction will count as confirming any hypothesis, even if P is the prediction that the sun will rise tomorrow and H the hypothesis that all ravens are black. What is wanted is thus an account that permits vertical inference without permitting absolutely everything, and IBE promises to fill that bill. IBE sanctions vertical inferences, because an explanation of some observed phenomenon may appeal to entities and processes not themselves observed; but it does not sanction just any vertical inference, since a particular scientific hypothesis would not, if true, explain

just any observation. A hypothesis about raven coloration will not, for example, explain why the sun rises tomorrow. Moreover, IBE discriminates between different hypotheses all of which would explain the evidence, since the model sanctions an inference only to the hypothesis which would best explain it.