How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- 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. (shrink)

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

"How do we go about weighing evidence, testing hypotheses and making inferences? According to the model of 'inference to the Best explanation', we work out what to inter from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In inference to the Best Explanation, Peter Lipton gives this important and influential idea the development and assessment it deserves." "The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contractive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of Inference to the Best Explanation has also been updated throughout and incudes a new bibliography."--BOOK JACKET. (shrink)

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. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Most laws are ceteris paribus (cp) laws: they say not that all Fs are G but only that All Fs are G all else being equal. Most philosophical accounts of laws, however, have focused on strict laws. This paper considers how some of the standard philosophical problems about laws change when we switch attention from strict to cp laws and what special problems these laws raise. It is argued that some cp laws do not simply reflect the complexity of the (...) world and the limitations of our minds. Correctly interpreted, they reveal the simplicity that underlies the complexity, a simplicity that it is without our cognitive powers to grasp. (shrink)

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. (shrink)

We are addicted to explanation, constantly asking and answering why-questions. But what does an explanation give us? I will consider some of the possible goods, intrinsic and instrumental, that explanations provide. The name for the intrinsic good of explanation is `understanding', but what is this? In the first part of this paper I will canvass various conceptions of understanding, according to which explanations provide reasons for belief, make familiar, unify, show to be necessary, or give causes. Three general features of (...) explanation will serve as tests of these varied conceptions. These features are: a) the distinction between knowing that a phenomena occurs and understanding why it does; b) the possibility of giving explanations that are not themselves explained; c) the possibility of explaining a phenomenon in cases where the phenomenon itself provides an essential part of the reason for believing that the explanation is correct. There are many other aspects of our explanatory practices that a good account of explanation and understanding should capture, but these simple tests provide surprisingly effective diagnostic tools for the evaluation of broad conceptions of the nature of understanding. It will turn out that the causal conception of understanding does particularly well on the tests, though of course it too faces various difficulties. The balance of this essay focuses on the causal conception. After addressing some of the difficulties it faces, I will ask.. (shrink)

Peter Lipton; VI*—Is the Best Good Enough?, Proceedings of the Aristotelian Society, Volume 93, Issue 1, 1 June 1993, Pages 89–104, https://doi.org/10.1093/aris.

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. (shrink)

[Peter Lipton] From a reliabilist point of view, our inferential practices make us into instruments for determining the truth value of hypotheses where, like all instruments, reliability is a central virtue. I apply this perspective to second-order inductions, the inductive assessments of inductive practices. Such assessments are extremely common, for example whenever we test the reliability of our instruments or our informants. Nevertheless, the inductive assessment of induction has had a bad name ever since David Hume maintained that any attempt (...) to justify induction by means of an inductive argument must beg the question. I will consider how the inductive justification of induction fares from the reliabilist point of view. I will also consider two other well-known arguments that can be construed as inductive assessments of induction. One is the miracle argument, according to which the truth of scientific theories should be inferred as the best explanation of their predictive success; the other is the disaster argument, according to which we should infer that all present and future theories are false on the grounds that all past theories have been found to be false. \\\ [John Worrall] Science seems in some ways to have been remarkably successful. What does this success tell us about the epistemological status of current scientific claims? Peter Lipton considers various meta-inductive arguments each of which start from premises about science's 'track record'. I show that his endorsements of the 'strongest' of these are, on analysis, remarkably weak. I argue that this is a reflection of difficulties within the general epistemological framework that he adopts-that of reliabilism. Finally, I briefly outline the quite different approach that I take to this issue, in the process responding to Lipton's criticisms of the 'pessimistic meta-induction'. (shrink)

There is a natural objection to the epistemic coherence of Bas van Fraassen’s use of a distinction between the observable and unobservable in his constructive empiricism, an objection that has been raised with particular clarity by Alan Musgrave. We outline Musgrave’s objection, and then consider how one might interpret and evaluate van Fraassen’s response. According to the constructive empiricist, observability for us is measured with respect to the epistemic limits of human beings qua measuring devices, limitations ‘which will be described (...) in detail in the final physics and biology’ (van Fraassen 1980: 17). In order for the constructive empiricist to determine what counts as observable, he will have to appeal to our best scientific theories of light, human physiology, and so forth. To put the same point in a slightly more abstract way, in order to draw a distinction between observable and unobservable entities, the constructive empiricist needs to use his best scientific theory of observability – call it T* – to tell him the identity of the observable entities. This raises an interesting difficulty. Constructive empiricism is the view that ‘science aims to give us theories that are empirically adequate; and acceptance of a theory involves as belief only that it is empirically adequate’ (van Fraassen 1980:12). When he accepts a theory, the constructive empiricist only believes the statements of his scientific theories that are about observable entities. Thus, in order to know which statements of a scientific theory to believe, the constructive empiricist needs to know which statements of that theory are about observable entities. In particular then, the constructive empiricist only believes the statements of his theory of observability T* that are about observable entities. Therefore, in order to know which statements of T* he can believe, the constructive empiricist needs to know which statements of T* are about observable entities. However, it is T* that tells the constructive empiricist what counts as an observable entity: the constructive empiricist therefore needs to use T* to tell him which statements of T* he can believe. The fact that the distinction drawn by T* must also apply to itself is not an immediate cause for alarm.. (shrink)

Alan Garfinkel (1981) and Bas van Fraassen (1980), among others, have proposed a contrastive theory of explanation, according to which the proper form of an explanatory why-question is not simply "Why P?" but "Why P rather than Q?". Dennis Temple (1988) has argued in this journal that the contrastive explanandum "P rather than Q" is equivalent to the conjunction, "P and not-Q". I show that the contrast is not equivalent to the conjunction, nor to other plausible noncontrastive candidates. I then (...) consider David Lewis's (1986) proposal for the way contrasts determine an explanatory cause, which does not require recasting the contrastive explanandum. Lewis's proposal is found to be unacceptable, but it suggests an improvement that shows contrastive explanations to employ a mechanism of "causal triangulation", similar to Mill's method of difference. (shrink)

An effect is typically explained by citing a cause, but not any cause will do. The oxygen and the spark were both causes of the fire, but normally only the spark explains it. What then distinguishes explanatory from unexplanatory causes? One might attempt to characterise this distinction in terms of intrinsic features of the causes. For example, some causes are changes while others are standing conditions, and one might claim that only the changes explain. Both the spark and the oxygen (...) are causes of the fire, but only the spark is a change, and perhaps this is the reason only the spark explains. On the other hand, one might attempt to characterise the distinction between explanatory and unexplanatory causes in terms of the relation between cause and effect. For example, only some causes are sufficient for their effects, and perhaps only sufficient causes explain. There is, however, an elementary feature of the distinction between explanatory and unexplanatory causes that neither an intrinsic nor a relational approach are well-suited to capture. This is the so-called `interest-relativity' of explanation: the very same cause may be explanatory for one person but not for another. When there is a famine in India, an Indian peasant may explain this by citing the drought, while a member of the World Health Organization may instead cite the failure of the Indian government to stock adequate reserves of food (Hart and Honore, 1985, pp. (shrink)

From a reliabilist point of view, our inferential practices make us into instruments for determining the truth value of hypotheses where, like all instruments, reliability is a central virtue. I apply this perspective to second-order inductions, the inductive assessments of inductive practices. Such assessments are extremely common, for example whenever we test the reliability of our instruments or our informants. Nevertheless, the inductive assessment of induction has had a bad name ever since David Hume maintained that any attempt to justify (...) induction by means of an inductive argument must beg the question. I will consider how the inductive justification of induction fares from the reliabilist point of view. I will also consider two other wellknown arguments that can be construed as inductive assessments of induction. One is the miracle argument, according to which the truth of scientific theories should be inferred as the best explanation of their predictive success; the other is the disaster argument, according to which we should infer that all present and future theories are false on the grounds that all past theories have been found to be false. (shrink)

From a reliabilist point of view, our inferential practices make us into instruments for determining the truth value of hypotheses where, like all instruments, reliability is a central virtue. I apply this perspective to second-order inductions, the inductive assessments of inductive practices. Such assessments are extremely common, for example whenever we test the reliability of our instruments or our informants. Nevertheless, the inductive assessment of induction has had a bad name ever since David Hume maintained that any attempt to justify (...) induction by means of an inductive argument must beg the question. I will consider how the inductive justification of induction fares from the reliabilist point of view. I will also consider two other well-known arguments that can be construed as inductive assessments of induction. One is the miracle argument, according to which the truth of scientific theories should be inferred as the best explanation of their predictive success; the other is the disaster argument, according to which we should infer that all present and future theories are false on the grounds that all past theories have been found to be false. \\\ [John Worrall] Science seems in some ways to have been remarkably successful. What does this success tell us about the epistemological status of current scientific claims? Peter Lipton considers various meta-inductive arguments each of which start from premises about science's 'track record'. I show that his endorsements of the 'strongest' of these are, on analysis, remarkably weak. I argue that this is a reflection of difficulties within the general epistemological framework that he adopts-that of reliabilism. Finally, I briefly outline the quite different approach that I take to this issue, in the process responding to Lipton's criticisms of the 'pessimistic meta-induction'. (shrink)

Bas van Fraassen wants to be an empiricist, but he is deeply dissatisfied with traditional versions of empiricism. So he is developing a new approach: epistemological voluntarism. Let me be blunt. Van Fraassen is an outstanding philosopher, and his new epistemology is important. But The Empirical Stance is a difficult book, because voluntarism is a difficult position to articulate. In what follows I attempt to clarify the situation a little, or at least to explain why it resists clarification.

This essay focuses on the cognitive tension between science and religion, in particular on the contradictions between some of the claims of current science and some of the claims in religious texts. My aim is to suggest how some work in the philosophy of science may help to manage this tension. Thus I will attempt to apply some work in the philosophy of science to the philosophy of religion, following the traditional gambit of trying to stretch the little one does (...) understand to cover what one does not understand. My own views on science and religion are hardly views from nowhere. My scientific perspective is that of a hopeful realist. Scientific realism is the view that science, though fallible through and through, is in the truth business, attempting to find out about a world independent of ourselves, and it is the view that business is, on the whole, going pretty well. My religious perspective is that of a progressive Jew. The problem I am worrying in this essay is my own problem. I take my other philosophical problems seriously too, but for the me the question of the relationship between science and religion has a personal edge I do not feel in my other philosophical obsessions with the likes of the problems of induction or the content of ceteris paribus laws. My reply to a charge of self-indulgence would be that my cognitive predicament is, I believe, widely shared. (shrink)

This paper considers a central objection to evolutionary epistemology. The objection is that biological and epistemic development are not analogous, since while biological variation is blind, epistemic variation is not. The generation of hypotheses, unlike the generation of genotypes, is not random. We argue that this objection is misguided and show how the central analogy of evolutionary epistemology can be preserved. The core of our reply is that much epistemic variation is indeed directed by heuristics, but these heuristics are analogous (...) to biological preadaptations which account for the evolution of complex organs. We also argue that many of these heuristics or epistemic preadaptations are not innate but were themselves generatedby a process of blind variation and selective retention. (shrink)

Abstract Evidence that supports a theory may be available to the scientist who constructs the theory and used as a guide to that construction, or it may only be discovered in the course of testing the theory. The central claim of this essay is that information about whether the evidence was accommodated or predicted affects the rational degree of confidence one ought to have in the theory. Only when the evidence is accommodated is there some reason to believe that the (...) theoretical system was ?fudged? to fit the evidence in a way that weakens support. This weakening is an objective matter, but not one that can be conclusively determined by examining the contents of the theory and its logical relationship to the evidence. Consequently, there is less reason to believe a theory on the basis of that evidence when it is known that the evidence was accommodated than there would be if it was known instead that the same evidence had been predicted. (shrink)

At a New York cocktail party shortly after the war, a young and insecure physics postgraduate was heard to blurt out to a woman he had met there: ‘I just want to know what Truth is!’ This was Thomas Kuhn and what he meant was that specific truths such as those of physics mattered less to him than acquiring metaphysical knowledge of the nature of truth. Soon afterwards, he gave up physics, but rather than take up philosophy directly, he approached (...) it by way of the history of science. The work that followed, especially The Structure of Scientific Revolutions , published in 1962 and now with sales of well over a million copies, makes his the most important contribution to the history and philosophy of science of the twentieth century. (shrink)

Frederick Suppe would have us reject hypothetico-deductivism, Bayesianism, and Inference to the Best Explanation, on the grounds that none of these philosophical models can account for the argumentative structure that virtually all data-based papers in science share, a structure exemplified by W. Jason Morgan's landmark paper in plate tectonics. At the core of that putative universal structure is a strategy whereby recalcitrant data are given interpretations designed to show that the theory or scientific model being advanced need not take them (...) into account. Pity Karl Popper: immunizing stratagems are the soul of scientific argument. (shrink)

This work on the philosophy of science shows how it cuts across the core areas of philosophy. Topics covered include the nature of natural laws; aleatory explanations; epistemology (The Dogma that Didn't Bark by Fodor); logic versus historical theories of confirmation; and much more.

The attitudes of scientists towards the philosophy of science is mixed and includes considerable indifference and some hostility. This may be due in part to unrealistic expectation and to misunderstanding. Philosophy is unlikely directly to improve scientific practices, but scientists may find the attempt to explain how science works and what it achieves of considerable interest nevertheless. The present state of the philosophy of science is illustrated by recent work on the ‘truth hypothesis’, according to which, science is generating increasingly (...) accurate representations of a mind-independent and largely unobservable world. According to Karl Popper, although truth is the aim of science, it is impossible to justify the truth hypothesis. According to Thomas Kuhn, the truth hypothesis is false, because scientists can only describe a world that is partially constituted by their own theories and hence not mind-independent. The failure of past scientific theories has been used to argue against the truth hypothesis; the success of the best current theories has been used to argue for it. Neither argument is sound. (shrink)

The stimulating programme of The Dappled World is metaphysics in the service of methodology. To say that the world is dappled is to say that the laws of nature only apply to certain regions. A central argument for this claim is epistemic. Although the laws, especially laws of physics, are typically thought of as universal, in fact we have only managed to construct precise quantitative models for a very limited range of cases, most of which lie within the artificially simplified (...) environment of the laboratory. We lack models for many real-word situations not because we haven’t tried to build them, but because we have tried and failed. This failure is compatible with the existence of a complete set of physical laws, perhaps never to be known, which governs all regions; but the evidence of our history of failures points the other way, to a dappled world. (shrink)

It was David Hume’s great sceptical argument about non-demonstrative reasoning—the problem of induction—that hooked me on philosophy. I am still wriggling, but in the present essay I will not consider how the Humean challenge to justify our inductive practices might be met; rather, I ask why we had to wait until Hume for the challenge to be raised. The question is a natural one to ask, given the intense interest in scepticism before Hume for as far back as we can (...) see in the history of philosophy, and given that Hume’s sceptical argument is so simple and so fundamental. It is not so easy to answer. I am no historian of philosophy, and given the pull that the problem of induction exerts on my own philosophical thinking, I know there is a considerable risk that the historical speculations I consider here will turn out to be worthlessly anachronistic. But I hope not. (shrink)

Is there anything you know entirely off your own bat? Your knowledge depends pervasively on the word of others. Knowledge of events before you were born or outside your immediate neighborhood are the obvious cases, but your epistemic dependence on testimony goes far deeper that this. Mundane beliefs — such as that the earth is round or that you think with your brain — almost invariably depend on testimony, and even quite personal facts — such as your birthday or the (...) identity of your biological parents — can only be known with the help of others. Science is no refuge from the ubiquity of testimony. At least most of the theories that a scientist accepts, she accepts because of what others say. The same goes for almost all the data, since she didn't perform those experiments herself. Even in those experiments she did perform, she relied on testimony hand over fist: just think of all those labels on the chemicals. Even her personal observations may have depended on testimony, if observation is theory- laden, since those theories with which it is laden. (shrink)