Online research in philosophy

It has been common wisdom for centuries that scientific inference cannot be deductive; if it is inference at all, it must be a distinctive kind of inductive inference. According to demonstrative theories of induction, however, important scientific inferences are not inductive in the sense of requiring ampliative inference rules at all. Rather, they are deductive inferences with sufficiently strong premises. General considerations about inferences suffice to show that there is no difference in justification between an inference construed demonstratively or ampliatively. The inductive risk may be shouldered by premises or rules, but it cannot be shirked. Demonstrative theories of induction might, nevertheless, better describe scientific practice. And there may be good methodological reasons for constructing our inferences one way rather than the other. By exploring the limits of these possible advantages, I argue that scientific inference is neither of essence deductive nor of essence inductive.

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.

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.

Whereas an inference (deductive as well as inductive) is usually viewed as being valid in virtue of its argument form, the present paper argues that scientific reasoning is material inference, i.e., justified in virtue of its content. A material inference is licensed by the empirical content embodied in the concepts contained in the premises and conclusion. Understanding scientific reasoning as material inference has the advantage of combining different aspects of scientific reasoning, such as confirmation, discovery, and explanation. This approach explains why these different aspects (including discovery) can be rational without conforming to formal schemes, and why scientific reasoning is local, i.e., justified only in certain domains and contingent on particular empirical facts. The notion of material inference also fruitfully interacts with accounts of conceptual change and psychological theories of concepts.

Reichenbach held that all scientific inference reduces, via probability calculus, to induction, and he held that induction can be justified. He sees scientific knowledge in a practical context and insists that any rational assessment of actions requires a justification of induction. Gaps remain in his justifying argument; for we can not hope to prove that induction will succeed if success is possible. However, there are good prospects for completing a justification of essentially the kind he sought by showing that while induction may succeed, no alternative is a rational way of trying.Reichenbach's claim that probability calculus, especially via Bayes' Theorem, can help to exhibit the structure of inference to theories is a valuable insight. However, his thesis that the weighting of all hypotheses rests only on frequency data is too restrictive, especially given his scientific realism. Other empirical factors are relevant. Any satisfactory account of scientific inference must be deeply indebted to Reichenbach's foundation work.

Many scientific realists think that the best reasons for scientific theories are abductive, i.e., must appeal to what is also called inference to the best explanation (IBE), while some anti-realists have argued that the use of abduction in defending realism is question-begging, circular, or incoherent. This paper studies the idea that abductive inference can be reformulated by taking its conclusion to concern the truthlikeness of a hypothetical theory on the basis of its success in explanation and prediction. The strength of such arguments is measured by the estimated verisimilitude of its conclusion given the premises. It is argued that this formulation helps to make precise and justifies the "ultimate argument for scientific realism": the empirical success of scientific theories would be a miracle unless they are truthlike.

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.

The underconsideration argument against inference to the best explanation and scientific realism holds that scientists are not warranted in inferring that the best theory is true, because scientists only ever conceive of a small handful of theories at one time, and as a result, they may not have considered a true theory. However, antirealists have not developed a detailed alternative account of why explanatory inference nevertheless appears so central to scientific practice. In this paper, I provide new defences against some recent objections to the underconsideration argument, while also developing an account of explanatory inference that both survives these criticisms and does not entail realism.

Hacking (1983) introduces an attempt to defend scientific realism on the basis of the reality of theoretical entities. This position, which is called entity realism, is based on disconnecting the reality of theoretical entities from the truth and explanatory power of theories that account for them. In this way, two problems can be avoided. First if theories about theoretical entities are rejected, the entities themselves do not have to go with them, and the realist thesis that we can have knowledge of what exists in the world can be sustained. Second, theoretical entities, which will replace theories as the grounds for the realist position, would be protected from attacks on the validity of the inference to the best explanation which underlies classical or "theory" realism. In other words, theoretical entities would be able to survive the collapse of the inference to the best explanation. The subject of this paper is a critique of this line of defending realism.

Nancy Cartwright relies upon an inference pattern known as inference to the best causal explanation (IBCE) to support a limited form of entity realism, according to which we are warranted in believing in entities that purportively cause observable effects. IBCE, as usually understood, is valid, even though all other forms of inference to the best explanation (IBE) are usually understood to be invalid. We argue that IBCE and IBE are in the same boat with respect to their ability to support realist conclusions. Either rule can be interpreted as valid, this is a matter of semantic convention. However, doing so deprives the rule of the empirical content the realist needs, requiring the realist to find independent warrant for a strong (theoretical or causal) premise. We then examine the proposed means of obtaining this warrant, and find them as inadequate in the case of IBCE as they are in the case of IBE.