Online research in philosophy

This paper offers an account of the relationship between inference and explanation in functional morphology which combines Robert Brandon's theory of adaptation explanation with standard accounts of inference to the best explanation. Inferences of function from structure, it is argued, are inferences to the best adaptation explanation. There are, however, three different approaches to the problem of determining which adaptation explanation is the best. The theory of inference to the best adaptation explanation is then applied to a case study from the history of functional morphology: the case of the crested duckbilled dinosaurs.

Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue that, with regard to the evaluation of hypotheses, Bayesian inductive inference is essentially a probabilistic form of induction by elimination. Bayesian induction is an extension of eliminativism to cases where, rather than contradict the evidence, false hypotheses imply that the evidence is very unlikely, much less likely than the evidence would be if some competing hypothesis were true. This is not, I think, how Bayesian induction is usually understood. The recent book by Howson and Urbach, for example, provides an excellent, comprehensive explanation and defense of the Bayesian approach; but this book scarcely remarks on Bayesian induction's eliminative nature. Nevertheless, the very essence of Bayesian induction is the refutation of false competitors of a true hypothesis, or so I will argue.

This paper considers how we decide whether to believe what we are told. Inference to the Best Explanation, a popular general account of non-demonstrative reasoning, is applied to this task. The core idea of this application is that we believe what we are told when the truth of what we are told would figure in the best explanation of the fact that we were told it. We believe the fact uttered when it is part of the best explanation of the fact of utterance. Having provided some articulation of this account of testimonial inference, the paper goes on to consider whether the account is informative and whether it is plausible.

Earlier in this volume, Wesley Salmon has given a characteristically clear and trenchant critique of the account of non-demonstrative reasoning known by the slogan `Inference to the Best Explanation'. As a long-time fan of the idea that explanatory considerations are a guide to inference, I was delighted by the suggestion that Wes and I might work together on a discussion of the issues. In the event, this project has exceeded my high expectations, for in addition to the intellectual gain that comes from the careful study of his essay, I have benefited enormously from the stream of illuminating emails and faxes that Wes has sent me during our collaboration. Doing philosophy together has been an education and a pleasure. Salmon's essay would place Inference to the Best Explanation beyond the pale of acceptable philosophical accounts of inference. According to Salmon, Inference to the Best Explanation has serious internal difficulties and compares very unfavourably with Bayesian approaches to these matters. My aim in the following remarks is irenic. I hope to show that a number of the claimed difficulties either are not really difficulties or are avoidable. In some cases, the avoidance will require a mild reinterpretation of the account that lies behind the slogan `Inference to the Best Explanation'; in others, it will require admitting limits to the scope of the account. For I accept at the outset that Inference to the Best Explanation cannot possibly be the whole story about the assessment of scientific hypotheses. For me, the interesting idea is simply that we sometimes decide how likely a hypothesis is to be correct in part by considering how good an explanation it would provide, if it were correct. This is the idea of explanatory considerations providing a guide to inference, and this is the idea that I will here promote.

The second edition of Peter Lipton’s classic text contains new and important material on the causal model of explanation, the relation of inference to the best explanation to the Bayesian account of scientific reasoning, how exactly explanation guides inference, and why we ought to think that explanatory virtues are truth-tropic. Lipton is a wonderfully clear writer and a thorough and subtle philosopher, and his book is both a student-friendly introduction to the issues addressed, and essential reading for expert epistemologists and philosophers of science. Appeal to the notion of inference to the best explanation is ubiquitous in defences of scientific realism, but also elsewhere in philosophy where the explanatory virtues of theories are often the only purported grounds for accepting or rejecting them. Despite this, most authors are far from explicit about the details of inference to the best explanation, and Lipton’s book is the most sustained investigation of the relationship between explanation and inference currently available. Furthermore, Lipton is exemplary in his engagement with the problems his arguments face, and judiciously modest in his claims, though not so modest as to court triviality. Hence, the book is replete with interesting and careful arguments. Everyone interested in epistemology or philosophy of science ought to read this book. That said, in my discussion below I will concentrate on what I regard as problems with some of Lipton’s arguments. The model of explanation which he develops is contrastive and causal. Lipton is clear that he does not think all explanations are causal, but he does think that many are, especially in science, and.

The first obstacle that confronts the student of induction is that of defining the subject matter. One initial point is to note that much of the relevant subject matter goes under the description ‘the theory of confirmation’. The distinction is primarily that the study of induction concerns inference, i.e. cases where one takes the conclusion to be established by the evidence, whereas confirmation concerns the weight of evidence, which one may take to be something like the credibility of a hypothesis in the light of the evidence. Discussions of confirmation often concern incremental confirmation, i.e. cases where the evidence is taken to increase the credibility of some hypothesis, even if not sufficiently to warrant inferring the truth of that hypothesis. However, some uses of ‘confirmation’ clearly refer to absolute confirmation, cases where the credibility of the hypothesis in the light of the evidence exceeds some (high) threshold. One may ask whether inductive inference corresponds to the case of absolute confirmation for some suitable threshold. I shall discuss inference and confirmation together, though it should be noted that some approaches eschew inference altogether. For example, the Bayesian takes scientific reasoning to be a matter of adjusting credences in propositions in the light of evidence, and says nothing about unqualified belief in a proposition. However, if we are interested in inductive knowledge then we must consider inference, since only then do we have a detached proposition that is the possible content of a mental state of knowing. A more pressing question concerns which inferences (or allegedly confirmatory relations) should be classed as inductive. A natural and straightforward approach is to define induction as encompassing any form of reasoning that extrapolates from one population to another, usually from a sample of a population to the whole population. For example, one might note that all observations of the position of some planet fall on an ellipse that has the Sun at one of its foci; from this one concludes that all the positions that planet takes fall on this ellipse (i.e..

Peter Lipton argues that inference to the best explanation involves the selection of a hypothesis on the basis of its loveliness. I argue that in optimal cases, a form of eliminative induction takes place, which I call ‘Holmesian inference’. I illustrate Holmesian inference by reference to examples from the history of medicine.

Second, there is a form of ampliative inference that has come to be called ‘inference to the best explanation,’ or more briefly ‘explanatory inference.’ Roughly: From the fact that a certain hypothesis would explain the data at hand better than any other available hypothesis, we infer with some degree of confidence that that leading hypothesis is correct. There is no question but that this inference is often performed. Arguably, every human being performs it many times in a day, perhaps without letup.

Peter Lipton has attempted to flesh out a model of Inference to the Best Explanation (IBE) by clarifying explanation in terms of a causal model. But Lipton's account of explanation makes an adequate explanation depend on a principle which is virtually identical to Mill's Method of Difference. This has the result of collapsing IBE on Lipton's account of it into causal inference as conceived by the Causal-Inference model of induction. According to this model, many of our inductions are inferences from effects to their probable causes, and Mill's Methods are canons to guide such inferences. Thus, Lipton's account of IBE fails to represent an advance over the already familiar Causal-Inference Model of induction.

How do we go about weighing evidence, testing hypotheses, and making inferences? The model of "inference to the best explanation" (IBE) -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and features a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science.