Online research in philosophy

Debates about explanation in the social sciences often proceed without any clear idea what an 'account' of explanation should do. In this paper I take a stance - what I will call contextualism - that denies there are purely formal and conceptual constraints on explanation and takes standards of explanation to be substantive empirical claims, paradigmatically claims about causation. I then use this standpoint to argue for position on issues in the philosophy of social science concerning reduction, idealized models, social mechanisms, functional explanations, inference to the best explanation and interpretive understanding.

This paper evaluates Kuipers' account of functional explanation in biology in view of an example of such an explanation taken from real biology. The example is the explanation of why electric fishes swim backwards (Lannoo and Lannoo 1993). Kuipers' account depicts the answer to a request for functional explanation as consisting only of statements that articulate a certain kind of consequence. It is argued that such an account fails to do justice to the main insight provided by the example explanation, namely the insight into why backwards swimming is needed by fishes that locate their food by means of an electric radar. The paper sketches an improved account that does justice to this kind of insight. It is argued that this account is consistent with and complementary to Kuipers' insight that function attributions are established by means of a process of hypothetico-deductive reasoning guided by a heuristic principle.

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.

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 and Bayesianism have both been proposed as descriptions of the way that people make inferences. This paper argues that one result from cognitive psychology, the "feminist bank teller" experiment, suggests that people use Inference to the Best Explanation rather than Bayesian techniques.

G. A. Cohen defends and Jon Elster criticizes Marxist use of functional explanation. But Elster's mechanical conception of explanation is, contrary to Elster's claims, a better basis for vindication of functional explanation than Cohen's nomological conception, which cannot provide an adequate account of functional explanation. Elster also objects that functional explanation commits us to metaphysically bizarre collective subjects, but his argument requires an implausible reading of methodological individualism which involves an unattractive eliminativism about social phenomena.

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.

This article deals with a type of functional explanation, viability explanation, that has been overlooked in recent philosophy of science. Viability explanations relate traits of organisms and their environments in terms of what an individual needs to survive and reproduce. I show that viability explanations are neither causal nor historical and that, therefore, they should be accounted for as a distinct type of explanation.