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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.

Going back at least to Duhem, there is a tradition of thinking that crucial experiments are impossible in science. I analyse Duhem's arguments and show that they are based on the excessively strong assumption that only deductive reasoning is permissible in experimental science. This opens the possibility that some principle of inductive inference could provide a sufficient reason for preferring one among a group of hypotheses on the basis of an appropriately controlled experiment. To be sure, there are analogues to Duhem's problems that pertain to inductive inference. Using a famous experiment from the history of molecular biology as an example, I show that an experimentalist version of inference to the best explanation (IBE) does a better job in handling these problems than other accounts of scientific inference. Furthermore, I introduce a concept of experimental mechanism and show that it can guide inferences from data within an IBE-based framework for induction. Introduction Duhem on the Logic of Crucial Experiments ‘The Most Beautiful Experiment in Biology’ Why Not Simple Elimination? Severe Testing An Experimentalist Version of IBE 6.1 Physiological and experimental mechanisms 6.2 Explaining the data 6.3 IBE and the problem of untested auxiliaries 6.4 IBE-turtles all the way down Van Fraassen's ‘Bad Lot’ Argument IBE and Bayesianism Conclusions CiteULike Connotea Del.icio.us What's this?

Van Fraassen (1989) argues that Inference to the Best Explanation is incoherent in the sense that adopting it as a rule for belief change will make one susceptible to a dynamic Dutch book. The present paper argues against this. A strategy is described that allows us to infer to the best explanation free of charge.

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.

In a situation in which several explanations compete, is the one that is better qua explanation also the one we should regard as the more likely to be true? Realists usually answer in the affirmative. They then go on to argue that since realism provides the best explanation for the success of science, realism can be inferred to. Nonrealists, on the other hand, answer the above question in the negative, thereby renouncing the inference to realism. In this paper I separate the two issues. In the first section it is argued that a rationale can be provided for the inference to the best explanation; in the second, that this rationale cannot justify an inference to realism. The defence of the inference rests on the claim that our standards of explanatory power are subject to critical examination, which, in turn, should be informed by empirical considerations. By means of a comparison of the realist's explanation for the success of science with that of conventionalism and instrumentalism it is then shown that realism does not offer a superior explanation and should not, therefore, be inferred to.

Inference to the Best Explanation has become the subject of a livelydebate in the philosophy of science. Scientific realists maintain, while scientificantirealists deny, that it is a compelling rule of inference. It seems that anyattempt to settle this debate empirically must beg the question against theantirealist. The present paper argues that this impression is misleading. A methodis described that, by combining Glymour''s theory of bootstrapping and Hacking''sarguments from microscopy, allows us to test IBE without begging any antirealistissues.

I argue against the tendency in the philosophy of science literature to link abduction to the inference to the best explanation (IBE), and in particular, to claim that Peircean abduction is a conceptual predecessor to IBE. This is not to discount either abduction or IBE. Rather the purpose of this paper is to clarify the relation between Peircean abduction and IBE in accounting for ampliative inference in science. This paper aims at a proper classification—not justification—of types of scientific reasoning. In particular, I claim that Peircean abduction is an in-depth account of the process of generating explanatory hypotheses, while IBE, at least in Peter Lipton’s thorough treatment, is a more encompassing account of the processes both of generating and of evaluating scientific hypotheses. There is then a two-fold problem with the claim that abduction is IBE. On the one hand, it conflates abduction and induction, which are two distinct forms of logical inference, with two distinct aims, as shown by Charles S. Peirce; on the other hand it lacks a clear sense of the full scope of IBE as an account of scientific inference.

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.

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.

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.