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.

In this contribution, I comment on Raffaella Campaner’s defense of explanatory pluralism in psychiatry (in this volume). In her paper, Campaner focuses primarily on explanatory pluralism in contrast to explanatory reductionism. Furthermore, she distinguishes between pluralists who consider pluralism to be a temporary state on the one hand and pluralists who consider it to be a persisting state on the other hand. I suggest that it would be helpful to distinguish more than those two versions of pluralism – different understandings (...) of explanatory pluralism both within philosophy of science and psychiatry – namely moderate/temporary pluralism, anything goes pluralism, isolationist pluralism, integrative pluralism and interactive pluralism. Next, I discuss the pros and cons of these different understandings of explanatory pluralism. Finally, I raise the question of how to implement or operationalize explanatory pluralism in scientific practice; how to structure the “genuine dialogue” or shape “the pluralistic attitude” Campaner is referring to. As tentative answers, I explore a question-based framework for explanatory pluralism as well as social-epistemological procedures for interaction among competing approaches and explanations. (shrink)

Przedmowa Problematyka związana z zależnościami przyczynowymi, ich modelowaniem i odkrywa¬niem, po długiej nieobecności w filozofii i metodologii nauk, budzi współcześnie duże zainteresowanie. Wiąże się to przede wszystkim z dynamicznym rozwojem, zwłaszcza od lat 1990., technik obli¬czeniowych. Wypracowane w tym czasie sieci bayesowskie uznaje się za matematyczny język przyczynowości. Pozwalają one na daleko idącą auto¬matyzację wnioskowań, co jest także zachętą do podjęcia prób algorytmiza¬cji odkrywania przyczyn. Na potrzeby badań naukowych, które pozwalają na przeprowadzenie eksperymentu z randomizacją, standardowe metody ustalania zależności przyczynowych (...) opracowano na początku XX wieku. Zupełnie inaczej sprawa przedstawia się w przypadku badań nieeksperymentalnych, gdzie podobne rozwiązania pozostają kwestią przyszłości. Zadaniem tej książki jest podanie warunków, które powinny być spełnione przez te rozwiązania, oraz sformułowanie proceduralnego kryterium zależności przy¬czynowych jako szczegółowej realizacji tych warunków. Pociąga ono waż¬kie konsekwencje dla filozofii i metodologii nauk, które ujawnia – podany w Części II – zarys me-todolo¬gii proceduralnej. W literaturze przedmiotu brakuje w miarę wszechstronnego i systema¬tycznego omówie¬nia najnowszych filozoficznych i metodologicznych dys¬kusji na temat przy¬czynowości, co niech będzie wytłumaczeniem, dlaczego w niektórych punktach obecnej książki szczegółowo referuję trudno dos¬tępne teksty źró¬dłowe. Przymiotnik „proceduralny” używam tu w znaczeniu węższym niż Huw Price (w którego pracach właściwszy byłby termin „kryterialny”) dla podkre¬ślenia – zgodnie z łacińskim źródłosłowem procedo – że dla ustalenia przy¬czyny niezbędne jest podjęcie przez uczonych określonych interakcji z ba¬daną rzeczywistością. Zalążki zamysłu prezentowanego w tej książce przedstawiłem podczas warsztatów filozoficznych „Philosophy and Probability” w roku 2002, zor¬ganizowanych przez Instytut Filozofii Uniwersytetu w Konstancji. Wdzięczny jestem uczestnikom tych warsztatów za uwagi, a przede wszyst¬kim następującym osobom: Luc Bovens, Brandon Fitelson, Alan Hájek, Stephan Hartmann oraz Jon Williamson. Podczas międzynarodowej konferencji „Analytical Pragmatism”, zorgani¬zowanej w Lublinie w roku 2003 przez Wydział Filo¬zofii Katolickiego Uniwersytetu Lubelskiego, odniosłem swoją koncepcję do prac Nancy Cartwright. Szczególnie inspirujący okazał się komentarz Huw Price’a do mojego referatu i przeprowadzona z nim dysku¬sja. Ujęcie koncepcji metodologii proceduralnej na szerszym tle współ-czes¬nego nurtu empirystycznego w filozofii nauki przedstawiłem w roku 2004 podczas konferencji „5th Quadrennial Fellows Conference”, zorgani¬zowanej przez Instytut Filozofii Uniwersytetu Jagiellońskiego oraz Centrum Filozofii Nauki w Pittsburghu. Szczególnie pomocne w dalszych moich pra¬cach były uwagi Jamesa Bogena, Janet Kourany, Jamesa Lennoxa, Johna Nortona, Thomasa Bonka, Jana Woleńskiego i Johna Worralla, za które wyra¬żam swoją wdzięczność. Korpus książki powstał podczas mojego stażu w Centrum Filozofii Nauki w Pittsburghu, który odbyłem jako stypendysta Fundacji na Rzecz Nauki Polskiej w roku akademickim 2004-2005. Uczestniczyłem w tym czasie w życiu naukowym Centrum i w pracach badawczych zespołu z Instytutu Filozofii Uniwersytetu Carnegie-Mellon w Pittsburghu kierowanego przez Clarka Glymoura. Na jego ręce składam podziękowanie za wiele po¬mocnych uwag do moich wy¬stąpień oraz tekstów i za dyskusje przede wszystkim z nim samym i z jego najbliższymi współpracownikami: Peterem Spirtesem oraz Richardem Scheinesem, a także pozostałymi członkami tego zes¬połu, doktorantami i uczestnikami seminarium badawczego „Causality in the Social Sciences”. Za wieloletnie wsparcie, wielopłaszczyznowe inspiracje towarzyszące pi¬saniu tej książki, a także liczne pomocne uwagi do jej wcześniejszych wersji dziękuję przede wszystkim Księdzu Profesorowi Andrzejowi Bronkowi oraz Księdzu Profesorowi Józefowi Herbutowi, współprowadzącemu seminarium doktorskie w Katedrze Metodologii Nauk Katolickiego Uniwersytetu Lubel¬skiego im. Jana Pawła II, jak również pozostałym uczestnikom tego semina¬rium. Dziękuję mojej Żonie, dr Annie Kawalec za wiele wysiłku włożonego w ulepszenie redakcji – językowej i merytorycznej – obecnej książki. Książkę tę można czytać na kilka sposobów. Czytelnikom zainteresowa¬nym przede wszystkim prowadzeniem badań empirycznych polecałbym rozpoczęcie od Rozdziału 2. i kontynuację pozostałych rozdziałów Części I, a następnie Dodatków. Czytelnikom zainteresowanym problemami filozo¬fii i metodologii nauk polecałbym rozpoczęcie lektury książki od Części II i uzupełniającą lekturę Rozdziału 2., a następnie Wprowadzenia i Zakończenia. Czytelnikom mniej zainteresowanym zagadnieniami teoretycznymi pole¬całbym zapoznanie się z fascynującymi dziejami odkrycia przyczyn cholery przez Johna Snowa, które rekonstruuję w Rozdziale 1. W dalszej części nato¬miast polecałbym przejście do Wprowadzenia i Zakończenia, które w mniej specjalistyczny sposób przybliżają proponowane tu rozstrzygnięcia. Tekst książki nie był dotąd publikowany. Wyjątkiem są pewne fragmenty Rozdziału 8. oraz 9., które w zmienionej postaci ukazały się w Rocznikach Filozoficznych (Kawalec 2004). -/- Lublin, luty 2006 r. (shrink)

This paper investigates the nature of scientific realism. I begin by considering the anomalous fact that Bas van Fraassen’s account of scientific realism is strikingly similar to Arthur Fine’s account of scientific non-realism. To resolve this puzzle, I demonstrate how the two theorists understand the nature of truth and its connection to ontology, and how that informs their conception of the realism debate. I then argue that the debate is much better captured by the theory of truthmaking, and not by (...) any particular theory of truth. To be a scientific realist is to adopt a realism-relevant account of what makes true the scientific theories one accepts. The truthmaking approach restores realism’s metaphysical core—distancing itself from linguistic conceptions of the debate—and thereby offers a better characterization of what is at stake in the question of scientific realism. (shrink)

Abstract Not much scholarly work is needed in order to stumble across many passages where Edmund Husserl seems to advocate an anti-realist attitude towards the natural sciences. This tendency, however, is not well-received within the secondary literature. While some commentators criticize Husserl for his alleged scientific anti-realism, others argue that Husserl's position is much more realist than the first impression indicates. It is against this background that I want to argue for the following theses: a) The basic outlook of Husserl's (...) epistemology as well as his more substantial comments regarding the natural sciences indeed result in a (sophisticated version of) scientific anti-realism which bears certain resemblances to Bas van Fraassen's constructive empiricism; b) This scientific anti-realism can be defended against the two most common objections raised in the secondary literature; c) It is only by means of this sophisticated version of scientific anti-realism that phenomenology can circumvent the problem of ?scientific objectivism? (shrink)

The concept of approximate truth plays a prominent role in most versions of scientific realism. However, adequately conceptualizing ?approximate truth? has proved challenging. This article argues that the goal of articulating the concept of approximate truth can be advanced by first investigating the processes by which science accords theories the status of accepted or rejected. Accordingly, this article uses a path diagram model as a visual heuristic for the purpose of showing the processes in science that are involved in determining (...) a theory's status. This ?inductive realist? model of theory status then serves as a starting point for explicating an inductive realist view of approximate truth that, it is argued, can explain instances of the success of science, but does not (1) require science's theories to be strictly true in any world or (2) require a metric for measuring how close an approximately true theory is to some strictly true theory. To show the advantages of the inductive realist approach to approximate truth, an example of a major success story of science, the successful eradication of smallpox, is reviewed and then explained. (shrink)

neglected aspect: knowledge of error, or ‘‘negative’’ knowledge. The development of knowledge of what counts as error occurs via a kind of internal variation and selection, or quasi-evolutionary, process. Processes of reflection generate a hierarchy of principles of error.

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)

In this paper I defend the claim that Paul Feyerabend held a robust metaphilosophical position for most of his philosophical career. This position I call Decision-Based Epistemology and reconstruct it in terms of three key components: a form of epistemic voluntarism concerning the justification of philosophical positions and a behaviorist account of philosophical beliefs, which allows him to cast normative arguments concerning philosophical beliefs in scientific methodology, such as realism, in terms of means-ends relations. I then introduce non-naturalist and naturalist (...) variants of his conception of normativity, which I trace back to his mentors Viktor Kraft and Karl Popper, respectively. This distinction, introduced on the metaphilosophical level, can can be put to use to explain key changes in Feyerabend’s philosophical proposals, such as the viability of his methodological argument for realism. I conclude that this Decision-Based Epistemology should be further explored by historically embedding Feyerabend’s metaphilosophy in a voluntarist tradition of scientific philosophy. (shrink)

There has been a great deal of skepticism towards the value of the realism/anti-realism debate. More specifically, many have argued that plausible formulations of realism and anti-realism do not differ substantially in any way. In this paper, I argue against this trend by demonstrating how a hypothetical resolution of the debate, through deeper engagement with the historical record, has important implications for our criterion of theory pursuit and science policy. I do this by revisiting Arthur Fine’s ‘small handful’ argument for (...) realism and show how the debate centers on whether continuity should be an indicator for the future fruitfulness of a theory. I then demonstrate how these debates work in practice by considering the case of the Human Brain Project. I close by considering some potential practical considerations of formulating meta-inductions. By doing this, I contribute three insights to the current debate: 1) demonstrate how the realism/anti-realism debate is a substantive debate, 2) connect debates about realism/anti-realism to debates about theory choice and pursuit, and 3) show the practical significance of meta-inductions. (shrink)

O argumento da inferência da melhor explicação enuncia que dado que uma evidência necessita de explicação e dado que uma hipótese, baseada em conhecimento anterior verdadeiro, ofereceu uma melhor explicação do que suas rivais para, conclui-se que temos boas razões para acreditar que H é verdadeira. Este argumento é apresentado com a finalidade de apresentar razões em favor da confiabilidade da metodologia científica: a melhor teoria teria emergido num contexto competitivo no qual foram eliminadas teorias rivais. Para antirrealistas, porém, este (...) argumento apresenta sérias deficiências. Um dos problemas deste argumento está na premissa, que descreve uma competição entre hipóteses rivais; de acordo com Kyle Stanford, o registro histórico mostra que a competição nem sempre teria ocorrido, já que algumas hipóteses rivais a uma hipótese proposta nem mesmo teriam sido concebidas, e portanto a confiabilidade da ciência não seria, ao menos em alguns casos, um produto de inferências eliminativas; com isso estaríamos diante do “problema das alternativas não concebidas”. Neste artigo pretendemos descrever sumariamente o argumento da inferência da melhor explicação. Em seguida, apresentamos o problema das alternativas não concebidas. Após, mostramos um estudo de caso oferecido por Stanford para uma defesa de sua posição. Por fim, na conclusão, apontamos um pequeno problema na proposta de Stanford. (shrink)

First, I answer the controversial question ’What is scientific realism?’ with extensive reference to the varied accounts of the position in the literature. Second, I provide an overview of the key developments in the debate concerning scientific realism over the past decade. Third, I provide a summary of the other contributions to this special issue.

The present study attempts to provide a consistent and coherent account of what the world could be like, given the conceptual framework and results of contemporary quantum theory. It is suggested that standard quantum mechanics can, and indeed should, be understood as a realist theory within its domain of application. It is pointed out, however, that a viable realist interpretation of quantum theory requires the abandonment or radical revision of the classical conception of physical reality and its traditional philosophical presuppositions. (...) It is argued, in this direction, that the conceptualization of the nature of reality, as arising out of our most basic physical theory, calls for a kind of contextual realism. Within the domain of quantum mechanics, knowledge of 'reality in itself', 'the real such as it truly is' independent of the way it is contextualized, is impossible in principle. In this connection, the meaning of objectivity in quantum mechanics is analyzed, whilst the important question concerning the nature of quantum objects is explored. (shrink)

[This paper is part of a book symposium on Bas van Fraassen's Scientific Representation: Paradoxes of Perspective (OUP, 2010)].

Realist evaluation has gained prominence in the field of evaluation in recent years. Its theory-driven approach to explaining how and why programmes work or not makes it attractive to many novices, early career researchers, and organizations implementing various programmes globally and relevant to policymakers and programme implementers. While realist evaluation seeks to be pragmatic, adopting principles and methods that can be used to help focus an evaluation, its deep ontological and epistemological foundations make its application in real-life situations challenging. In (...) this paper, we seek to unpack the key tenets scaffolding the practical application of realist evaluation. Although Pawson and Tilley foreground realist evaluation in applied scientific realism, we argue that an amalgam of scientific and critical realist principles underpins realist evaluation. We unpack these principles and illustrate how they fit into each other to provide a cogent theoretical foundation for realist evaluation. (shrink)

Benacerraf has presented two problems for the philosophy of mathematics. These are the problem of identification and the problem of representation. This paper aims to reconstruct the latter problem and to unpack its undermining bearing on the version of Ontic Structural Realism that frames scientific representations in terms of abstract structures. I argue that the dichotomy between mathematical structures and physical ones cannot be used to address the Benacerraf problem but strengthens it. I conclude by arguing that versions of OSR (...) that do not rely on mathematical frameworks for representational purposes need not be vulnerable to Benacerraf’s second problem. (shrink)

In this paper, I assess recent claims in philosophy of science about scientific perspectivism being compatible with realism. I clarify the rationale for scientific perspectivism and the problems and challenges that perspectivism faces in delivering a form of realism. In particular, I concentrate my attention on truth, and on ways in which truth can be understood in perspectival terms. I offer a cost-benefit analysis of each of them and defend a version that in my view is most promising in living (...) up to realist expectations. (shrink)

Neuroscience is a multidisciplinary effort to understand the structures and functions of the brain and brain-mind relations. This effort results in an increasing amount of data, generated by sophisticated technologies. However, these data enhance our descriptive knowledge, rather than improve our understanding of brain functions. This is caused by methodological gaps both within and between subdisciplines constituting neuroscience, and the atomistic approach that limits the study of macro- and mesoscopic issues. Whole-brain measurement technologies do not resolve these issues, but rather (...) aggravate them by the complexity problem. The present article is devoted to methodological and epistemic problems that obstruct the development of human neuroscience. We neither discuss ontological questions nor review data, except when it is necessary to demonstrate a methodological issue. As regards intradisciplinary methodological problems, we concentrate on those within neurobiology and psychology. As regards interdisciplinary problems, we suggest that core disciplines of neuroscience can be integrated using systemic concepts that also entail human-environment relations. We emphasize the necessity of a meta-discussion that should entail a closer cooperation with philosophy as a discipline of systematic reflection. The atomistic reduction should be complemented by the explicit consideration of the embodiedness of the brain and the embeddedness of humans. The discussion is aimed at the development of an explicit methodology of integrative human neuroscience, which will not only link different fields and levels, but also help in understanding clinical phenomena. (shrink)

In this article, I present a case study of underdetermination in nineteenth-century electrodynamics between a pure field theory and a formulation in terms of action at a distance. A particular focus is on the question if and how this underdetermination is eventually resolved. It turns out that after a period of overt underdetermination, during which the approaches are developed separately, the two programmes are merged. On the basis of this development, I argue that the original underdetermination survives in hidden form (...) in ontological and methodological redundancies of the subsequent particle–field electrodynamics. Implications regarding criteria for theory choice and the realism debate are briefly addressed. (shrink)

Epistemologists have recently debated whether understanding is a species of knowledge. However, because they have offered little in the way of a detailed analysis of understanding, they lack the resources to resolve this issue. In this paper, I propose that S understands why p if and only if S has the non-Gettierised true belief that p, and for some proposition q, S has the non-Gettierised true belief that q is the best available explanation of p, S can correctly explain p (...) with q, and S can identify the features that make q the best explanation of p. On this analysis, understanding is reducible to knowing that p and that q is the best available explanation of p. (shrink)

This article outlines a philosophy of science in practice that focuses on the engineering sciences. A methodological issue is that these practices seem to be divided by two different styles of scientific reasoning, namely, causal-mechanistic and mathematical reasoning. These styles are philosophically characterized by what Kuhn called ?disciplinary matrices?. Due to distinct metaphysical background pictures and/or distinct ideas of what counts as intelligible, they entail distinct ideas of the character of phenomena and what counts as a scientific explanation. It is (...) argued that the two styles cannot be reduced to each other. At the same time, although they are incompatible, they must not be regarded as competing. Instead, they produce different kinds of epistemic results, which serve different kinds of epistemic functions. Moreover, some scientific breakthroughs essentially result from relating them. This view of complementary styles of scientific reasoning is supported by pluralism about metaphysical background pictures. (shrink)

In this dissertation, I examine a view called ‘Epistemic Structural Realism’, which holds that we can, at best, have knowledge of the structure of the physical world. Put crudely, we can know physical objects only to the extent that they are nodes in a structure. In the spirit of Occam’s razor, I argue that, given certain minimal assumptions, epistemic structural realism provides a viable and reasonable scientific realist position that is less vulnerable to anti-realist arguments than any of its rivals.

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...) aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed. (shrink)

This paper considers how explanatory factors can play a role in our ampliative inferential practices. Van Fraassen has argued that there is no possible rational rule that governs ampliative inferences and includes weightings for explanatory beauty. In opposition to van Fraassen, Douven has argued that ampliative inferential rules that include weightings for explanatory factors can be rationally followed. There is, however, a crucial difficulty with Douven's approach: applying the ampliative rule that he suggests leads into irrational belief states. A way (...) to remove the difficulties with Douven's account is suggested. The implications of rationally including explanatory factors in ampliative inferences are then considered. Introduction The proposed interpretations Van Fraassen's justification of the Bayesian approach A rational non-Bayesian interpretation? Further restrictions on D(A,B) Conclusion. (shrink)