The incredible achievements of modern scientific theories lead most of us to embrace scientific realism: the view that our best theories offer us at least roughly accurate descriptions of otherwise inaccessible parts of the world like genes, atoms, and the big bang. In Exceeding Our Grasp, Stanford argues that careful attention to the history of scientific investigation invites a challenge to this view that is not well represented in contemporary debates about the nature of the scientific enterprise. The historical record (...) of scientific inquiry, Stanford suggests, is characterized by what he calls the problem of unconceived alternatives. Past scientists have routinely failed even to conceive of alternatives to their own theories and lines of theoretical investigation, alternatives that were both well-confirmed by the evidence available at the time and sufficiently serious as to be ultimately accepted by later scientific communities. Stanford supports this claim with a detailed investigation of the mid-to-late 19th century theories of inheritance and generation proposed in turn by Charles Darwin, Francis Galton, and August Weismann. He goes on to argue that this historical pattern strongly suggests that there are equally well-confirmed and scientifically serious alternatives to our own best theories that remain currently unconceived. Moreover, this challenge is more serious than those rooted in either the so-called pessimistic induction or the underdetermination of theories by evidence, in part because existing realist responses to these latter challenges offer no relief from the problem of unconceived alternatives itself. Stanford concludes by investigating what positive account of the spectacularly successful edifice of modern theoretical science remains open to us if we accept that our best scientific theories are powerful conceptual tools for accomplishing our practical goals, but abandon the view that the descriptions of the world around us that they offer are therefore even probably or approximately true. (shrink)

This book examines the philosophical conception of abductive reasoning as developed by Charles S. Peirce, the founder of American pragmatism. It explores the historical and systematic connections of Peirce's original ideas and debates about their interpretations. Abduction is understood in a broad sense which covers the discovery and pursuit of hypotheses and inference to the best explanation. The analysis presents fresh insights into this notion of reasoning, which derives from effects to causes or from surprising observations to explanatory theories. The (...) author outlines some logical and AI approaches to abduction as well as studies various kinds of inverse problems in astronomy, physics, medicine, biology, and human sciences to provide examples of retroductions and abductions. The discussion covers also everyday examples with the implication of this notion in detective stories, one of Peirce’s own favorite themes. The author uses Bayesian probabilities to argue that explanatory abduction is a method of confirmation. He uses his own account of truth approximation to reformulate abduction as inference which leads to the truthlikeness of its conclusion. This allows a powerful abductive defense of scientific realism. This up-to-date survey and defense of the Peircean view of abduction may very well help researchers, students, and philosophers better understand the logic of truth-seeking. (shrink)

The problem of simplicity involves three questions: How is the simplicity of a hypothesis to be measured? How is the use of simplicity as a guide to hypothesis choice to be justified? And how is simplicity related to other desirable features of hypotheses -- that is, how is simplicity to be traded-off? The present paper explores these three questions, from a variety of viewpoints, including Bayesianism, likelihoodism, and the framework of predictive accuracy formulated by Akaike (1973). It may turn out (...) that simplicity has no global justification -- that its justification varies from problem to problem. (shrink)

This paper formulates what I think is the basic problem of any attempt to characterise the abstract structure of scientific method, viz., that it has to satisfy two conflicting desiderata: it should be ampliative (contentincreasing) and it should confer epistemic warrant on its outcomes. Then, after two extreme solutions to the problem of the method, viz., Enumerative Induction and the Method of Hypothesis, are examined, the paper argues that abduction, suitably understood as Inference to the Best Explanation, offers the best (...) description of scientific method and solves the foregoing problem in the best way: it strikes the best balance between ampliation and epistemic warrant. (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)

I develop an account of predictive similarity that allows even Antirealists who accept a correspondence conception of truth to answer the Realist demand (recently given sophisticated reformulations by Musgrave and Leplin) to explain the success of particular scientific theories by appeal to some intrinsic feature of those theories (notwithstanding the failure of past efforts by van Fraassen, Fine, and Laudan). I conclude by arguing that we have no reason to find truth a better (i.e., more plausible) explanation of a theory's (...) success than predictive similarity, even of its success in making novel predictions. (shrink)

Attempts to show that the simplicity of a hypothesis can be measured by attending to how well it answers certain kinds of questions.

P. Kyle Stanford (2000) attempts to offer a truth-linked explanation of the success of science which, he thinks, can be welcome to antirealists. He proposes an explanation of the success of a theory T1 in terms of its predictive similarity to the true theory T of the relevant domain. After raising some qualms about the supposed antirealist credentials of Stanford's account, I examine his explanatory story in some detail and show that it fails to offer a satisfactory explanation of the (...) success of science. (shrink)

Traditionally, philosophers have focused mostly on the logical template of inference. The paradigm-case has been deductive inference, which is topic-neutral and context-insensitive. The study of deductive rules has engendered the search for the Holy Grail: syntactic and topic-neutral accounts of all prima facie reasonable inferential rules. The search has hoped to find rules that are transparent and algorithmic, and whose following will just be a matter of grasping their logical form. Part of the search for the Holy Grail has been (...) to show that the so-called scientific method can be formalised in a topic-neutral way. We are all familiar with Carnap’s inductive logic, or Popper’s deductivism or the Bayesian account of scientific method. (shrink)

In a seminar with the title “Deduction and Induction in the Sciences”, it is intriguing to ask the following questions: Is there a third type of inference besides deduction and induction? Does this third type of inference play a significant role within scientific inquiry? A positive answer to both of these questions was advocated by Charles S. Peirce throughout his career, even though his opinions changed in important ways during the fifty years between 1865 and 1914. Peirce called the third (...) kind of inference “hypothesis”, “abduction”, or “retroduction”.1 In this paper, I shall follow Peirce’s steps in discussing abduction by analyzing its logical form , its role in science , and the grounds of its validity . We shall see that Peirce’s discussion is more insightful than many recent attempts to analyze abductive inference. Still, recently some progress has been made in the treatment of abduction within the Bayesian theory of epistemic probability and truth-approximation . The results of this work support the view of scientific realism: abduction or inference to the best explanation , combined with empirical and experimental testing of scientific theories, is the best method of seeking informative truths in science. (shrink)

The No-Miracles Argument (NMA) is often used to support scientific realism. We can formulate this argument as an inference to the best explanation this accusation of circularity by appealing to reliabilism, an externalist epistemology. In this paper I argue that this retreat fails. Reliabilism suffers from a potentially devastating difficulty known as the Generality Problem and attempts to solve this problem require adopting both epistemic and metaphysical assumptions regarding local scientific theories. Although the externalist can happily adopt the former, if (...) he adopts the latter then the Generality Problem arises again, but now at the level of scientific methodology. Answering this new version of the Generality Problem is impossible for the scientific realist without making the important further assumption that there exists the possibility of a unique rule of Doing this however would make the NMA viciously premise circular. (shrink)

Over the past years, in books and journals (this journal included), N. Maxwell launched a ferocious attack on B. C. van Fraassen’s view of science called Constructive Empiricism (CE). This attack has been totally ignored. Must we conclude from this silence that no defence is possible and that a fortiori Maxwell has buried CE once and for all? Or is the attack too obviously flawed as not to merit exposure? A careful dissection of Maxwell’s reasoning will make it clear that (...) neither is the case. This dissection includes an analysis of Maxwell’s ‘aberrance–argument’ (omnipresent in his many writings) for the claim that science implicitly and permanently accepts a substantial, metaphysical thesis about the universe, which then paves the way for his own metaphysical-realist hierarchy-view of science. This aberrance-claim, which Maxwell directs against a widely shared and harmful ideology of science called ‘Standard Empiricism’, generally has been ignored too, for more than a quarter of a century. Our conclusions will be that (i) Maxwell’s attacks on CE can be beaten off, and (ii) his ‘aberrance–arguments’ do not establish what Maxwell believes they establish, but (iii) we can draw a number of valuable lessons from these attacks about the nature of science and of the libertarian nature of CE. (shrink)

Scientific realists have claimed that the posit that our theories are (approximately) true provides the best or the only explanation for their success . In response, I revive two non-realists explanations. I show that realists, in discarding them, have either misconstrued the phenomena to be explained or mischaracterized the relationship between these explanations and their own. I contend nonetheless that these non-realist competitors, as well as their realist counterparts, should be rejected; for none of them succeed in explaining a significant (...) list of successes. I propose a related non-realist explanation of success that appears to be the most suitable among those considered. (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)

Why be a scientific realist? The predominant motivation is explanationist: we need realism to understand the successfulness of science. Why be an antirealist? The predominant motivation is skeptical: theory systematically exceeds the reach of empirical warrant. Antirealists deny that explanatory power is evidential; realists deny that the reach of empirical warrant summarily terminates at the boundary of the observable. But these counterarguments are mere protection of philosophical stances to which the adversaries independently incline.

Leplin attempts to reinstate the common sense idea that theoretical knowledge is achievable, indeed that its achievement is part of the means to progress in empirical knowledge. He sketches the genesis of the skeptical position, then introduces his argument for Minimalist Scientific Realism -- the requirement that novel predicitons be explained, and the claim that only realism about scientific theories can explain the importance of novel prediction.

Laudan constructs a fresh approach to a longtime problem for the philosopher of science: how to explain the simultaneous and widespread presence of both agreement and disagreement in science. Laudan critiques the logical empiricists and the post-positivists as he stresses the need for centrality and values and the interdependence of values, methods, and facts as prerequisites to solving the problems of consensus and dissent in science.

According to the “no-miracles argument” (NMA), truth is the best explanation of the predictive-instrumental success of scientific theories. A standard objection against NMA is that it is viciously circular. In Scientific Realism: How Science Tracks Truth Stathis Psillos has claimed that the circularity objection can be met when NMA is supplemented with a reliabilist approach to justification. I will try to show, however, that scientific realists cannot take much comfort from this policy: if reliabilism makes no qualifications about the domain (...) where inference to the best explanation is reliable, scientific realists flagrantly beg the question. A qualified version of reliabilism, on the other side, does not entitle us to infer the realist conclusion. I conclude, then, that Psillos’s proposal does not make any significant progress for scientific realism. (shrink)

The paper deals with the relation between abduction and inference to the best explanation (IBE). A heuristic and a normative interpretation of IBE are distinguished. Besides, two different normative interpretations —those vindicated by I. Niiniluoto and S. Psillos— are discussed. I conclude that, in principle, Aliseda's theory of abduction fits better with a heuristic account of IBE.

The paper deals with the relation between abduction and inference to the best explanation. A heuristic and a normative interpretation of IBE are distinguished. Besides, two different normative interpretations —those vindicated by I. Niiniluoto and S. Psillos— are discussed. I conclude that, in principle, Aliseda’s theory of abduction fits better with a heuristic account of IBE.

I argue that a certain type of naturalist should not accept a prominent version of the no-miracles argument (NMA). First, scientists (usually) do not accept explanations whose explanans-statements neither generate novel predictions nor unify apparently disparate established claims. Second, scientific realism (as it appears in the NMA) is an explanans that makes no new predictions and fails to unify disparate established claims. Third, many proponents of the NMA explicitly adopt a naturalism that forbids philosophy of science from using any methods (...) not employed by science itself. Therefore, such naturalistic philosophers of science should not accept the version of scientific realism that appears in the NMA. (shrink)

The realist programme has degenerated by now to the point where it is quite beyond salvage. A token of this degeneration is that there are altogether too many realisms. It is as though by splitting into a confusing array of types and kinds, realism has hoped that some one variety might yet escape extinct. I shall survey the debate, and some of these realisms, below. Here I would just point out the obvious; that in so far as the successes of (...) science mount while realism continues to decline we must conclude that scientific success lends no support to realism. Since it is unlikely to support anti-realism, we have some reason to suspect that the philosophical debate over realism does not concern issues that can be settled by developments in the sciences, no matter how successful science may be. Further, since that success grounds a culture of acceptance for science and its entities, we have reason to believe that the existence of those entities is also not actually the issue that concerns realism. Afortiori, it is not the issue that concerns anti-realism either; nor, I might add, is anti-realism the winner in the philosophical debate that realism has lost. (shrink)

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 Peireean 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 Peireean 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 Peireean 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. (shrink)

Philosophy and Phenomenological Research (forthcoming).

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 of IBE we may be able to eliminate all but one of the hypotheses. In such cases we have a form of eliminative induction takes place, which I call ‘Holmesian inference’. I argue that Lipton’s example in which Ignaz Semmelweis identified a cause of puerperal fever better illustrates Holmesian inference than Liptonian IBE. I (...) consider in detail the conditions under which Holmesian inference is possible and conclude by considering the epistemological relations between Holmesian inference and Liptonian IBE.Keywords: Inference to the best explanation; Peter Lipton; Abduction; Holmesian inference; Eliminative induction. (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)

Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships. For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that (...) aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science. Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments. (shrink)

This book offers a superbly clear analysis of the standard arguments for and against scientific realism. In surveying claims on both sides of the debate, Kukla organizes them in ways that expose unnoticed connections. He identifies broad patterns of error, reconciles seemingly incompatible positions, and discovers unoccupied positions with the potential to influence further debate. Kukla's overall assessment is that neither the realists nor the antirealists may claim a decisive victory.

Many introductions to this field start with the problem of justifying scientific knowledge but Alexander Bird begins by examining the subject matter, or metaphysics, of science. Using topical scientific debates he vividly elucidates what it is for the world to be governed by laws of nature. This idea provides the basis for explanations and causes and leads to a discussion of natural kinds and theoretical entities. With this foundation in place he goes on to consider the epistemological issues of how (...) science arrives at knowledge, favouring a treatment of scientific reasoning based on inference to the best explanation. Drawing on contemporary work in epistemology, Bird argues that scepticism about induction should not be a problem for science and examines the consequences of this position for controversies surrounding the ideas of scientific progress and scientific revolution. Bird's insightful treatment makes Philosophy of Science an ideal text for undergraduate courses. The guides to further reading provided in each chapter help the reader pursue interesting topics and facilitate the use of the book in conjunction with primary sources. (shrink)

Scientific Realism is the optimistic view that modern science is on the right track: that the world really is the way our best scientific theories describe it to be. In his book, Stathis Psillos gives us a detailed and comprehensive study, which restores the intuitive plausibility of scientific realism. We see that throughout the twentieth century, scientific realism has been challenged by philosophical positions from all angles: from reductive empiricism, to instrumentalism and modern skeptical empiricism. Scientific Realism explains that the (...) history of science does not undermine the notion of scientific realism, and instead makes it reasonable to accept scientific as the best philosophical account of science, its empirical success, its progress and its practice. Anyone wishing to gain a deeper understanding of the state of modern science and why scientific realism is plausible, should read this book. (shrink)

This is an introductory survey to the philosophy of science suitable for beginners and nonspecialists. Its point of departure is the question: why should we believe what science tells us about the world? In this attempt to justify the claims of science the book treats such topics as observation data, confirmation of theories, and the explanation of phenomena. The writing is clear and concrete with detailed examples drawn from contemporary science: solar neutrinos, the gravitational bending of light, and the creation/evolution (...) debate, for example. What emerges is a view of science in which observation relies on theory to give it meaning and credibility, while theory relies on observation for its motivation and validation. (shrink)

The aim of this paper is to rebut two major criticisms of the No-Miracles Argument for Realism. The first comes from Musgrave. The second comes from Colin Howson. Interestingly enough, these criticisms are the mirror image of each other. Yet, they both point to the conclusion that NMA is fallacious. Musgrave’s misgiving against NMA is that if it is seen as an inference to the best explanation, it is deductively fallacious. Being a deductivist, he tries to correct it by turning (...) it into a valid deductive argument. Howson’s misgiving against NMA is that if it is seen as an inference to the best explanation, it is inductively fallacious. Being a subjective Bayesian, he tries to correct it by turning it into a sound subjective Bayesian argument. I will argue that both criticisms are unwarranted. (shrink)