Proponents of the no-miracles argument contend that scientific realism is “the only philosophy that doesn’t make the success of science a miracle.” Bas van Fraassen argued, however, that the success of our best theories can be explained in Darwinian terms—by the fact they are survivors of a winnowing process in which unsuccessful theories are rejected. Critics of this selectionist explanation complain that while it may account for the fact we have chosen successful theories, it does not explain why any particular (...) one of those theories succeeds. Against this I defend the claim that if selectionists manage to account for the former, they have no burden to account for the latter; the success of any particular one of our best theories might as well be an extraordinary coincidence. I refer to this enhanced selectionist reply as “coincidentalism” and argue that it is a serious but underappreciated response to the no-miracles argument. More generally, I argue the considerations raised in favor of this response show that versions of the no-miracles argument focusing on the success of particular theories are misguided. (shrink)

Predictivists use the no miracle argument to argue that “novel” predictions are decisive evidence for theories, while mere accommodation of “old” data cannot confirm to a significant degree. But deductivists claim that since confirmation is a logical theory-data relationship, predicted data cannot confirm more than merely deduced data, and cite historical cases in which known data confirmed theories quite strongly. On the other hand, the advantage of prediction over accommodation is needed by scientific realists to resist Laudan’s criticisms of the (...) no miracle argument. So, if the deductivists are right, the most powerful argument for realism collapses. There seems to be an inescapable contradiction between these prima facie plausible arguments of predictivists and deductivists; but this puzzle can be solved by understanding what exactly counts as novelty, if novel predictions must support the no miracle argument, i.e., if they must be explainable only by the truth of theories. Taking my cues from the use-novelty tradition, I argue that (1) the predicted data must not be used essentially in building the theory or choosing the auxiliary assumptions. This is possible if the theory and its auxiliary assumptions are plausible independently of the predicted data, and I analyze the consequences of this requirement in terms of best explanation of diverse bodies of data. Moreover, the predicted data must be (2) a priori improbable, and (3) heterogeneous to the essentially used data. My proposed notion of novelty, therefore, is not historical, but functional. Hence, deductivists are right that confirmation is independent of time and of historical contingencies such as if the theorist knew a datum, used it, or intended to accommodate it. Predictivists, however, are right that not all consequences confirm equally, and confirmation is not purely a logical theory-data relation, as it crucially involves background epistemic conditions and the notion of best explanation. Conditions (1)–(3) make the difference between prediction and accommodation, and account for the confirming power of theoretical virtues such as non ad-hocness, non-fudging, non-overfitting, independence and consilience. I thus show that functional novelty (a) avoids the deductivist objections to predictivism, (b) is a gradual notion, in accordance with the common intuition that confirmation comes in degrees, and (c) supports the no miracle argument, so vindicating scientific realism. (shrink)

My target is the epistemological dimension of the realism debate. After establishing a stance voluntarist framework with a Jamesian background, drawing mostly on Wylie, Chakravarty, and van Fraassen, I argue that current voluntarists are too permissive. I show that especially various anti-realist stances but also some realist and selective realist stances block themselves from refutation by the history of science. I argue that such stances should be rejected. Finally, I propose that any disagreement that cannot be resolved by this strategy (...) frequently boils down to an epistemic value disagreement about balancing the truth-goal. (shrink)

This dissertation is about human knowledge of reality. In particular, it argues that scientific knowledge is bounded by historically available instruments and theories; nevertheless, the use of several independent instruments and theories can provide access to the persistent potentialities of reality. The replicability of scientific observations and experiments allows us to obtain explorable evidence of robust entities and properties. The dissertation includes seven chapters. It also studies three cases – namely, Higgs bosons and hypothetical Ϝ-particles (section 2.4), the Ptolemaic and (...) Kepler model of the planets (section 6.7), and the special theory of relativity (chapter 7). -/- Chapter 1 is the introduction of the dissertation. Chapter 2 clarifies the notion of the real on the basis of two concepts: persistence and resistance. These concepts enable me to explain my ontological belief in the real potentialities of human-independent things and the implications of this view for the perceptual and epistemological levels of discussion. On the basis of the concept of “overlapping perspectives”, chapter 3 argues that entity realism and perspectivism are complementary. That is, an entity that manifests itself through several experimental/observational methods is something real, but our knowledge of its nature is perspectival. Critically studying the recent views of entity realism, chapter 4 extends the discussion of entity realism and provides a criterion for the reality of property tokens. Chapter 5, in contrast, develops the perspectival aspects of my view on the basis of the phenomenological-hermeneutical approaches to the philosophy of science. This chapter also elaborates my view of empirical evidence, as briefly expressed in sections 2.5 and 4.5. Chapter 6 concerns diachronic theoretical perspectives. It first explains my view of progress, according to which current perspectives are broader than past ones. Second, it argues that the successful explanations and predictions of abandoned theories can be accounted for from our currently acceptable perspectives. The case study of Ptolemaic astronomy supports the argument of this chapter. Chapter 7 serves as the conclusion of the dissertation by applying the central themes of the previous chapters to the case study of special relativity theory. I interpret frame-dependent properties, such as length and time duration, and the constancy of the speed of light according to realist perspectivism. (shrink)

According to the no-miracle argument, scientific realism is the only view that does not render the predictive success of scientific theories miraculous. Against the no-miracle argument, selectionists argue that the predictive success of scientific theories is a product of them being subject to a selection process that weeds out predictively unsuccessful theories. Against selectionism, I argue that the selectionist explanation is not an alternative to the realist one. More precisely, I draw on a standard framework in behavioral biology, known as (...) “Tinbergen’s four questions,” and argue that selectionism and realism are aimed at answering different questions and that they are complementary to each other, instead. (shrink)

Surrealism holds that observables behave as if T were true, whereas scientific realism holds that T is true. Surrealism and scientific realism give different explanations of why T is empirically adequate. According to surrealism, T is empirically adequate because observables behave as if it were true. According to scientific realism, T is empirically adequate because it is true. I argue that the surrealist explanation merely clarifies the concept of empirical adequacy, whereas the realist explanation makes an inductive inference about T. (...) Therefore, the surrealist explanation is a conceptual one, whereas the realist explanation is an empirical one, and the former is not an alternative to the latter. (shrink)

According to a defense of scientific realism known as the “divide et impera move”, mature scientific theories enjoying predictive success are partially true. This paper investigates a paradigmatic historical case: the prediction, based on Fresnel’s wave theory of light, that a bright spot should figure in the shadow of a disc. Two different derivations of this prediction have been given by both Poisson and Fresnel. I argue that the details of these derivations highlight two problems of indispensability arguments, which state (...) that only the indispensable constituents of this success are worthy of belief and retained through theory-change. The first problem is that, contrary to a common claim, Fresnel’s integrals are not needed to predict the bright spot phenomenon. The second problem is that the hypotheses shared by to these two derivations include problematic idealizations. I claim that this example leads us to be skeptical about which aspects of our current theories are worthy of belief. (shrink)

Van Fraassen (2007, 2017) consistently uses the English view of rationality to parry criticisms from scientific realists. I assume for the sake of argument that the English view of rationality is tenable, and then argue that it has disastrous implications for van Fraassen’s (1980) contextual theory of explanation, for the empiricist position that T is empirically adequate, and for scientific progress. If you invoke the English view of rationality to rationally disbelieve that your epistemic colleagues’ theories are true, they might, (...) in turn, invoke it to rationally disbelieve that your positive theories are true. (shrink)

A prominent type of scientific realism holds that some important parts of our best current scientific theories are at least approximately true. According to such realists, radically distinct alternatives to these theories or theory-parts are unlikely to be approximately true. Thus one might be tempted to argue, as the prominent anti-realist Kyle Stanford recently did, that realists of this kind have little or no reason to encourage scientists to attempt to identify and develop theoretical alternatives that are radically distinct from (...) currently accepted theories in the relevant respects. In other words, it may seem that realists should recommend that scientists be relatively conservative in their theoretical endeavors. This paper aims to show that this argument is mistaken. While realists should indeed be less optimistic of finding radically distinct alternatives to replace current theories, realists also have greater reasons to value the outcomes of such searches. Interestingly, this holds both for successful and failed attempts to identify and develop such alternatives. (shrink)

Nickles (2017) advocates scientific antirealism by appealing to the pessimistic induction over scientific theories, the illusion hypothesis (Quoidbach, Gilbert, and Wilson, 2013), and Darwin’s evolutionary theory. He rejects Putnam’s (1975: 73) no-miracles argument on the grounds that it uses inference to the best explanation. I object that both the illusion hypothesis and evolutionary theory clash with the pessimistic induction and with his negative attitude towards inference to the best explanation. I also argue that Nickles’s positive philosophical theories are subject to (...) Park’s (2017a) pessimistic induction over antirealists. (shrink)

State of the art paper on the topic realism/anti-realism. The first part of the paper elucidates the notions of existence and independence of the metaphysical characterization of the realism/anti-realism dispute. The second part of the paper presents a critical taxonomy of the most important positions and doctrines in the contemporary literature on the domains of science and mathematics: scientific realism, scientific anti-realism, constructive empiricism, structural realism, mathematical Platonism, mathematical indispensability, mathematical empiricism, intuitionism, mathematical fictionalism and second philosophy.

The pessimistic induction over scientific theories holds that present theories will be overthrown as were past theories. The pessimistic induction over scientists holds that present scientists cannot conceive of future theories just as past scientists could not conceive of present theories. The pessimistic induction over realists :4321–4330, 2013) holds that present realists are wrong about present theories just as past realists were wrong about past theories. The pessimistic induction over antirealist theories :3–21, 2014) holds that the latest antirealist explanation of (...) the success of science :891–901, 2003) has hidden problems just as its eight predecessors did. In this paper, I criticize the pessimistic inductions over scientific theories, scientists, and realists, introduce a pessimistic induction over antirealist theories, and then construct two new pessimistic inductions. One is a pessimistic induction over antirealists according to which the author of the latest antirealist proposal cannot see hidden problems with his proposal just as his antirealist predecessors could not see hidden problems with their proposals. The other is the pessimistic induction over pessimists according to which since past pessimists have been wrong about their present scientific theories from the early twentieth century to the early twenty-first century, future pessimists will also be wrong about their present scientific theories from the early twenty-first century to the early twenty-second century. (shrink)

It has become apparent that the debate between scientific realists and constructive empiricists has come to a stalemate. Neither view can reasonably claim to be the most rational philosophy of science, exclusively capable of making sense of all scientific activities. On one prominent analysis of the situation, whether we accept a realist or an anti-realist account of science actually seems to depend on which values we antecedently accept, rather than our commitment to “rationality” per se. Accordingly, several philosophers have attempted (...) to argue in favour of scientific realism or constructive empiricism by showing that one set of values is exclusively best, for anyone and everyone, and that the downstream choice of the philosophy of science which best serves those values is therefore best, for anyone and everyone. These efforts, however, seem to have failed. In response, I suggest that philosophers of science should suspend the effort to determine which philosophy of science is best for everyone, and instead begin investigating which philosophy of science is best for specific people, with specific values, in specific contexts. I illustrate how this might be done by briefly sketching a single case study from the history of science, which seems to show that different philosophies of science are better at motivating different forms of scientific practice. (shrink)

Scientific Realism (SR) has three crucial aspects: 1) the centrality of the concept of truth, 2) the idea that success is a reliable indicator of truth, and 3) the idea that the Inference to the Best Explanation is a reliable inference rule. It will be outlined how some realists try to overcome the difficulties which arise in justifying such crucial aspects relying on an adaptationist view of evolutionism, and why such attempts are inadequate. Finally, we will briefly sketch some of (...) the main difficulties the realist has to face in defending those crucial aspects, and how such difficulties are deeply related: they derive from the inability of SR to satisfyingly avoid the sceptical challenge of the criterion of truth. Indeed, SR seems not to be able to fill the so-called ‘epistemic gap’ (Sankey 2008). In fact, the epistemic gap cannot be filled in no way other than obtaining a criterion of truth, but such a criterion cannot be obtained if the epistemic gap obtains. (shrink)

Realism and surrealism claim, respectively, that a scientific theory is successful because it is true, and because the world operates as if it is true. Lyons :891–901, 2003) criticizes realism and argues that surrealism is superior to realism. I reply that Lyons’s criticisms against realism fail. I also attempt to establish the following two claims: Realism and surrealism lead to a useful prescription and a useless prescription, respectively, on how to make an unsuccessful theory successful. Realism and surrealism give the (...) credit for the success of a theory to an appropriate factor and to an inappropriate factor, respectively. Finally, I point out that surrealism is vulnerable to my pessimistic induction :3–21, 2014a) against antirealism. (shrink)

There are nine antirealist explanations of the success of science in the literature. I raise difficulties against all of them except the latest one, and then construct a pessimistic induction that the latest one will turn out to be problematic because its eight forerunners turned out to be problematic. This pessimistic induction is on a par with the traditional pessimistic induction that successful present scientific theories will be revealed to be false because successful past scientific theories were revealed to be (...) false. (shrink)

In this paper, I argue that the ultimate argument for Scientific Realism, also known as the No-Miracles Argument (NMA), ultimately fails as an abductive defence of Epistemic Scientific Realism (ESR), where (ESR) is the thesis that successful theories of mature sciences are approximately true. The NMA is supposed to be an Inference to the Best Explanation (IBE) that purports to explain the success of science. However, the explanation offered as the best explanation for success, namely (ESR), fails to yield independently (...) testable predictions that alternative explanations for success do not yield. If this is correct, then there seems to be no good reason to prefer (ESR) over alternative explanations for success. (shrink)

This paper proposes to carve out a new position in the scientific realism/antirealism debate and argue that it captures some of the most important realist and some of the most important antirealist considerations. The view, briefly stated, is that there is always a fact of the matter about whether the singular statements science gives us are literally true, but there is no fact of the matter about whether the non-singular statements science gives us are literally true. I call this view (...) singularist semirealism. Singularist semirealism sides with scientific realism with regards to singular statements but it is an antirealist view with regards to non-singular statements. In this sense, singularist semirealism could be considered to be ‘the best of both worlds’. 1 Introduction2 Truth and Correctness3 Property-Tokens and Property-Types4 Singularist Semirealism5 Encouragement from Actual Scientific Practice6 Some Potentially Worrying Consequences of Singularist Semirealism7 Some Promising Consequences of Singularist Semirealism8 Conclusion: Theory Change. (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)

A scientific theory is successful, according to Stanford (2000), because it is suficiently observationally similar to its corresponding true theory. The Ptolemaic theory, for example, is successful because it is sufficiently similar to the Copernican theory at the observational level. The suggestion meets the scientific realists' request to explain the success of science without committing to the (approximate) truth of successful scientific theories. I argue that Stanford's proposal has a conceptual flaw. A conceptually sound explanation, I claim, respects the ontological (...) order between properties. A dependent property is to be explained in terms of its underlying property, not the other way around. The applicability of this point goes well beyond the realm of the debate between scientific realists and antirealists. Any philosophers should keep the point in mind when they attempt to give an explanation of a property in their field whatever it may be. (shrink)

The central motivation behind the scientific realism debate is explaining the impressive success of scientific theories. The debate has been dominated by two rival types of explanations: the first relies on some sort of static, referentially transparent relationship between the theory and the unobservable world, such as truthlikeness, representation, or structural similarity; the second relies on no robust relationship between the theory and unobservable reality at all, and instead draws on predictive similarity and the stringent methodology of science to explain (...) success. I argue that this is a false dichotomy, at least insofar as dynamical theories are concerned. The best explanation of the success of dynamical theories, I argue, must appeal to a robust but referentially opaque theory–world relation. The dynamical notion of ‘tracking’ fulfills this promise. I formulate a modified no miracles argument that is liberated from the false dichotomy and show how tracking responds to the modified argument. (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)

Van Fraassen (1980) claims that successful theories exist today because successful theories survive and unsuccessful ones die. Wray (2007, 2010) appeals to Stanford’s new pessimistic induction (2006), arguing that van Fraassen’s selectionist explanation is better than the realist explanation that successful theories exist because they are approximately true. I argue that if the pessimistic induction is correct, then the evolutionary explanation is neither true nor empirically adequate, and that realism is better than selectionism because realism explains more phenomena in science (...) than selectionism. (shrink)

Suppose that scientific realists believe that a successful theory is approximately true, and that constructive empiricists believe that it is empirically adequate. Whose belief is more likely to be false? The problem of underdetermination does not yield an answer to this question one way or the other, but the pessimistic induction does. The pessimistic induction, if correct, indicates that successful theories, both past and current, are empirically inadequate. It is arguable, however, that they are approximately true. Therefore, scientific realists overall (...) take less epistemic risk than constructive empiricists. (shrink)

In this article, against the background of a notion of ‘assembled’ truth, the evolutionary progressiveness of a theory is suggested as novel and promising explanation for the success of science. A new version of realism in science, referred to as ‘naturalised realism’ is outlined. Naturalised realism is ‘fallibilist’ in the unique sense that it captures and mimics the self-corrective core of scientific knowledge and its progress. It is argued that naturalised realism disarms Kyle Stanford’s anti-realist ‘new induction’ threats by showing (...) that ‘explanationism’ and his ‘epistemic instrumentalism’ are just two positions among many on a constantly evolving continuum of options between instrumentalism and full-blown realism. In particular it is demonstrated that not only can naturalised realism redefine the terms of realist debate in such a way that no talk of miracles need enter the debate, but it also promises interesting defenses against inductive- and under-determination-based anti-realist arguments. (shrink)

In this paper, I will show that the Miracle Argument is unsound if one assumes a certain form of transient underdetermination. For this aim, I will first discuss and formalize several variants of underdetermination, especially that of transient underdetermination, by means of measure theory. I will then formalize a popular and persuasive form of the Miracle Argument that is based on "use novelty". I will then proceed to the proof that the miracle argument is unsound by means of a mathematical (...) example. Finally, I will expose two hidden presuppositions of the Miracle Argument that make it so immensely though deceptively persuasive. (shrink)

The aim or goal of science has long been discussed by both philosophers of science and scientists themselves. In The Scientific Image (van Fraassen 1980), the aim of science is famously employed to characterize scientific realism and a version of anti-realism, called constructive empiricism. Since the publication of The Scientific Image, however, various changes have occurred in scientific practice. The increasing use of machine learning technology, especially deep learning (DL), is probably one of the major changes in the last decade. (...) This paper aims to explore the implications of DL-aided research for the aim of science debate. I argue that, while the emerging DL-aided research is unlikely to change the state of classic opposition between constructive empiricism and scientific realism, it could offer interesting cases regarding the opposition between those who espouse truth as the aim of science and those oriented to understanding (of the kind that sacrifices truth). (shrink)

We argue that the societal consequences of the scientific realism debate, in the context of science-to-public communication are often overlooked and careful theorizing about it needs further empirical groundwork. As such, we conducted a survey experiment with 130 academics (from physics, chemistry, and biology) and 137 science communicators. We provided them with an 11-item questionnaire probing their views of scientific realism and related concepts. Contra theoretical expectations, we find that (a) science communicators are generally more inclined towards scientific antirealism when (...) compared to scientists in the same academic fields, though both groups show an inclination towards realism and (b) academics who engage in more theoretical work are not less (or more) realist than experimentalists. Lastly, (c), we fail to find differences with respect to selective realism but find that science communicators are significantly less epistemically voluntarist compared to their academic counterparts. Overall, our results provide first empirical evidence on the views of scientists and science communicators on scientific realism, with some results running contra to the theoretical expectations, opening up new empirical and theoretical research directions. (shrink)

Zygon®, Volume 57, Issue 1, Page 114-140, March 2022.

Entity realism is the view that ‘a good many theoretical entities do really exist’. The main novelty of entity realism was that it provided an account of scientific realism that did not have to endorse realism about theories—the general proposal was that entity realism is noncommittal about whether we should be realist about scientific theories. I argue that the only way entity realists can resist the pull of straight scientific realism about theories is by endorsing a recent new player in (...) the scientific realism debate: singularist semirealism. (shrink)

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 “sloppy models” program originated in systems biology, but has seen applications across a range of fields. Sloppy models are dependent on a large number of parameters, but highly insensitive to the vast majority of parameter combinations. Sloppy models proponents claim that the program may explain the success of science. I argue that the sloppy models program can at best provide a very partial explanation. Drawing a parallel with renormalization group realism, I argue that it would only give us grounds (...) for a minimal kind of scientific realism. Nonetheless, the program can offer certain epistemic virtues. (shrink)

Bas van Fraassen’s Darwinian explanation for the success of science has sparked four decades of discussion, with scientific realists and antirealists alike using biologically inspired reasoning to support their points of view. Based on critical engagement with van Fraassen’s proposal itself and later contributions by Stathis Psillos and K. Brad Wray, we claim that central arguments on both sides of this controversy suffer from an insufficient understanding of Darwinism and its underlying biological concepts. Adding the necessary biological background turns out (...) to subvert the argumentative force of viewing the success of scientific theories as analogous to the behaviour of biotic entities. In conclusion, we sketch more productive ways of viewing the relationship between biology and scientific realism. (shrink)

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)