Fabio Sterpetti

Mizrahi’s argument against Stanford’s challenge to scientific realism is analyzed. Mizrahi’s argument is worth of attention for at least two reasons: (1) unlike other criticisms that have been made to Stanford’s view so far, Mizrahi’s argument does not question any specific claim of Stanford’s argument, rather it puts into question the very coherence of Stanford’s position, because it argues that since Stanford’s argument rests on the problem of the unconceived alternatives, Stanford’s argument is self-defeating. Thus, if Mizrahi’s argument is effective in countering Stanford’s view, it may be able to question the validity of other philosophical positions which similarly rest on the problem of the unconceived alternatives; (2) Mizrahi’s argument against Stanford’s view is in part based on the development of a Stanford-like argument for the field of philosophy. This makes Mizrahi’s argument potentially relevant to the metaphilosophical debate. After careful examination, Mizrahi’s argument against Stanford’s instrumentalism is found wanting. Moreover, a Stanford-like argument is developed, which aims at challenging the metaphilosophical stance implied by Mizrahi’s argument against Stanford’s instrumentalism.

This paper briefly outlines key elements of the heuristic view, which primarily seeks to rationally explain how knowledge is acquired. Central to this view is the concept of discovery, linking the heuristic view to the historical search for a logic of discovery that has been a focal point of philosophy from its early stages through the late nineteenth century. The discussion then centers on comparing Lakatos’ and Cellucci’s versions of the heuristic view, highlighting both shared elements and distinguishing features between the two. Finally, an overview of the trends and future prospects of the heuristic view that emerge by the contributions to this volume is presented.

According to exceptionalism about logic, logic is special, it enjoys a privileged position among the sciences, its method is not the method of the natural sciences, and the knowledge it provides is different in kind from the knowledge provided by the natural sciences. According to anti-exceptionalism about logic, instead, logic isn’t special, its method is continuous with the method of the natural sciences, logical theories are revisable, and when they are revised, they are revised on the same grounds on which scientific theories are revised. The heuristic view of logic can be regarded as an anti-exceptionalist stance on logic since it too conceives of logic as continuous with the natural sciences. Nevertheless, there are at least two main differences between the heuristic view of logic and standard formulations of anti-exceptionalism about logic: 1) anti-exceptionalists share with exceptionalists the idea that logic is the study of logical consequence, while according to the heuristic view of logic, logic should be understood as a logic of discovery; 2) anti-exceptionalists usually claim that the method of logic is abductive, while according to the heuristic view of logic the method of logic is not abductive. It will be argued that the heuristic view of logic provides a more adequate anti-exceptionalist view on logic than standard formulations of anti-exceptionalism about logic.

This volume offers a series of thought-provoking analyses that explore non-mainstream perspectives on knowledge acquisition and scientific discovery. A central theme across many of the contributions is the heuristic conception of method, a view that finds its philosophical roots in Imre Lakatos’s work in the philosophy of mathematics and, more distantly, in Platonic notions of philosophical inquiry. Knowledge acquisition is frequently conceptualized as a form of problem-solving, and problem-solving is typically regarded as a rational endeavor. However, the dominant view in the philosophy of science has long held that scientific discovery resists systematic explanation in terms of logic and rationality. If an alternative account of discovery grounded in these terms can be developed, it would represent a significant theoretical advancement. The essays collected in this book critically assess the promise and limitations of such non-standard accounts, and they investigate the possibility of framing scientific discovery within a logical and rational framework. This volume will be of particular interest to scholars in the philosophy of science, epistemology, philosophy of mathematics, and philosophy of logic, as well as to scientists engaged in theoretical inquiry and graduate students working in these or related areas.

Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on average and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). An interesting debate has arisen with regard to which is the more adequate epistemological interpretation of PEM. Indeed, Hohwy maintains that given that PEM supports an inferential view of perception and cognition, PEM has to be considered as conveying an internalist epistemological perspective. Contrary to this view, Clark maintains that it would be incorrect to interpret in such a way the indirectness of the link between the world and our inner model of it, and that PEM may well be combined with an externalist epistemological perspective. The aim of this paper is to assess those two opposite interpretations of PEM. Moreover, it will be suggested that Hohwy’s position may be considerably strengthened by adopting Carlo Cellucci’s view on knowledge (2013).

Counterfactuals abound in science, especially when one deals with models. Some models, namely highly idealized models, have assumptions that are metaphysically impossible. This means that in science one has often to deal with counterpossibles. According to the standard semantics for counterfactuals, all counterpossibles are vacuously true. But scientific practice shows that counterpossibles are not always regarded as vacuously true by scientists. To do justice of the use of counterpossibles in science, some authors think that we should adopt a semantics that allows for impossible worlds. It seems difficult to reconcile the role played by highly idealized models in science with scientific realism. Nevertheless, some authors think that it is possible to provide a realist account of highly idealized models. In this view, scientific realism should not be interpreted as the claim that models aim to provide accurate representations of models’ targets, rather it should be interpreted as the claim that models aim to provide true modal information about models’ targets. This variant of scientific realism seems to imply a commitment to some form of modal realism. I develop an objection to that variant of scientific realism by elaborating on some arguments originally developed to show that there are unknowable facts.

This article aims at discussing an interesting variant of scientific realism recently proposed and defended by Sandra Mitchell (forthcoming), namely an affordances-based and pragmatist variant of scientific realism. We firstly place Mitchell’s proposal in the context of the current state of the debate over scientific realism. Secondly, we summarize the salient features of Mitchell’s proposal. Thirdly, we point out some aspects of that proposal that might require some further refinement and clarification in order to make it less prone to criticisms by both realists and antirealists. More precisely, in this paper we address the following issues: 1) whether Mitchell’s proposal can be classified as a genuine form of scientific realism; 2) whether the fact that in Mitchell’s proposal figure some variants of the no miracle argument is in tension with some other of its features.

This article presents a challenge that those philosophers who deny the causal interpretation of explanations provided by population genetics might have to address. Indeed, some philosophers, known as statisticalists, claim that the concept of natural selection is statistical in character and cannot be construed in causal terms. On the contrary, other philosophers, known as causalists, argue against the statistical view and support the causal interpretation of natural selection. The problem I am concerned with here arises for the statisticalists because the debate on the nature of natural selection intersects the debate on whether mathematical explanations of empirical facts are genuine scientific explanations. I argue that if the explanations provided by population genetics are regarded by the statisticalists as non-causal explanations of that kind, then statisticalism risks being incompatible with a naturalist stance. The statisticalist faces a dilemma: either she maintains statisticalism but has to renounce naturalism; or she maintains naturalism but has to content herself with an account of the explanations provided by population genetics that she deems unsatisfactory. This challenge is relevant to the statisticalists because many of them see themselves as naturalists.

This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book rethink and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution. The volume covers examples from various fields and areas, such as molecular biology, climate modeling, clinical medicine, and artificial intelligence. The explosion of technological tools and drivers for scientific research calls for a renewed understanding of the human character of science. This book aims precisely to contribute to such a renewed understanding of science.

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science.

This chapter tries to answer the following question: How should we conceive of the method of mathematics, if we take a naturalist stance? The problem arises since mathematical knowledge is regarded as the paradigm of certain knowledge, because mathematics is based on the axiomatic method. Moreover, natural science is deeply mathematized, and science is crucial for any naturalist perspective. But mathematics seems to provide a counterexample both to methodological and ontological naturalism. To face this problem, some authors tried to naturalize mathematics by relying on evolutionism. But several difficulties arise when we try to do this. This chapter suggests that, in order to naturalize mathematics, it is better to take the method of mathematics to be the analytic method, rather than the axiomatic method, and thus conceive of mathematical knowledge as plausible knowledge.

There are three main accounts of scientific progress: 1) the epistemic account, according to which an episode in science constitutes progress when there is an increase in knowledge; 2) the semantic account, according to which progress is made when the number of truths increases; 3) the problem-solving account, according to which progress is made when the number of problems that we are able to solve increases. Each of these accounts has received several criticisms in the last decades. Nevertheless, some authors think that the epistemic account is to be preferred if one takes a realist stance. Recently, Dellsén proposed the noetic account, according to which an episode in science constitutes progress when scientists achieve increased understanding of a phenomenon. Dellsén claims that the noetic account is a more adequate realist account of scientific progress than the epistemic account. This paper aims precisely at assessing whether the noetic account is a more adequate realist account of progress than the epistemic account.

Le principali concezioni del progresso scientifico sono tre: la concezione epistemica, secondo cui il progresso si verifica quando si verifica un incremento della conoscenza; la concezione semantica, secondo cui il progresso si verifica quando vi è un incremento delle verità; la concezione problem-solving, secondo cui il progresso si verifica quando si verifica un incremento del numero dei problemi che si è in grado di risolvere. La concezione epistemica è ritenuta la più compatibile con una prospettiva realista. Di recente, Dellsén ha proposto la concezione “noetica”, secondo cui il progresso si verifica quando vi è un incremento dell’understanding di un fenomeno da parte degli scienziati. Dellsén sostiene che la concezione noetica sia una concezione realista del progresso più adeguata di quella epistemica. Scopo di questo articolo è valutare se la concezione noetica sia più adeguata della concezione epistemica.

Mizrahi’s argument against Stanford’s challenge to scientific realism is analyzed. Mizrahi’s argument is worth of attention for at least two reasons: unlike other criticisms that have been made to Stanford’s view so far, Mizrahi’s argument does not question any specific claim of Stanford’s argument, rather it puts into question the very coherence of Stanford’s position, because it argues that since Stanford’s argument rests on the problem of the unconceived alternatives, Stanford’s argument is self-defeating. Thus, if Mizrahi’s argument is effective in countering Stanford’s view, it may be able to question the validity of other philosophical positions which similarly rest on the problem of the unconceived alternatives; Mizrahi’s argument against Stanford’s view is in part based on the development of a Stanford-like argument for the field of philosophy. This makes Mizrahi’s argument potentially relevant to the metaphilosophical debate. After careful examination, Mizrahi’s argument against Stanford’s instrumentalism is found wanting. Moreover, a Stanford-like argument is developed, which aims at challenging the metaphilosophical stance implied by Mizrahi’s argument against Stanford’s instrumentalism.

In the process of scientific discovery, knowledge ampliation is pursued by means of non-deductive inferences. When ampliative reasoning is performed, probabilities cannot be assigned objectively. One of the reasons is that we face the problem of the unconceived alternatives: we are unable to explore the space of all the possible alternatives to a given hypothesis, because we do not know how this space is shaped. So, if we want to adequately account for the process of knowledge ampliation, we need to develop an account of the process of scientific discovery which is not exclusively based on probability calculus. We argue that the analytic view of the method of science advocated by Cellucci is interestingly suited to this goal, since it rests on the concept of plausibility. In this perspective, in order to account for how probabilities are in fact assigned in uncertain contexts and knowledge ampliation is really pursued, we have to take into account plausibility-based considerations.

How should one conceive of the method of mathematics, if one takes a naturalist stance? Mathematical knowledge is regarded as the paradigm of certain knowledge, since mathematics is based on the axiomatic method. Natural science is deeply mathematized, and science is crucial for any naturalist perspective. But mathematics seems to provide a counterexample both to methodological and ontological naturalism. To face this problem, some naturalists try to naturalize mathematics relying on Darwinism. But several difficulties arise when one tries to naturalize in this way the traditional view of mathematics, according to which mathematical knowledge is certain and the method of mathematics is the axiomatic method. This paper suggests that, in order to naturalize mathematics through Darwinism, it is better to take the method of mathematics not to be the axiomatic method.

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives.

The aim of this article is to explore the impact of Darwinism in metaethics and dispel some of the confusion surrounding it. While the prospects for a Darwinian metaethics appear to be improving, some underlying epistemological issues remain unclear. We will focus on the so-called Evolutionary Debunking Arguments (EDAs) which, when applied in metaethics, are defined as arguments that appeal to the evolutionary origins of moral beliefs so as to undermine their epistemic justification. The point is that an epistemic disanalogy can be identified in the debate on EDAs between moral beliefs and other kinds of beliefs, insofar as only the former are regarded as vulnerable to EDAs. First, we will analyze some significant debunking positions in metaethics in order to show that they do not provide adequate justification for such an epistemic disanalogy. Then, we will assess whether they can avoid the accusation of being epistemically incoherent by adopting the same evolutionary account for all kinds of beliefs. In other words, once it is argued that Darwinism has a corrosive impact on metaethics, what if its universal acid cannot be contained?

In the last decades two different and apparently unrelated lines of research have increasingly connected mathematics and evolutionism. Indeed, on the one hand different attempts to formalize darwinism have been made, while, on the other hand, different attempts to naturalize logic and mathematics have been put forward. Those researches may appear either to be completely distinct or at least in some way convergent. They may in fact both be seen as supporting a naturalistic stance. Evolutionism is indeed crucial for a naturalistic perspective, and formalizing it seems to be a way to strengthen its scientificity. The paper shows that, on the contrary, those directions of research may be seen as conflicting, since the conception of knowledge on which they rest may be undermined by the consequences of accepting an evolutionary perspective.

Evolutionary Debunking Arguments are defined as arguments that appeal to the evolutionary genealogy of our beliefs to undermine their justification. Recently, Helen De Cruz and her co-authors supported the view that EDAs are self-defeating: if EDAs claim that human arguments are not justified, because the evolutionary origin of the beliefs which figure in such arguments undermines those beliefs, and EDAs themselves are human arguments, then EDAs are not justified, and we should not accept their conclusions about the fact that human arguments are unjustified. De Cruz's objection to EDAs is similar to the objection raised by Reuben Hersh against the claim that, since by Gödel's second incompleteness theorem the purpose of mathematical logic to give a secure foundation for mathematics cannot be achieved, mathematics cannot be said to be absolutely certain. The response given by Carlo Cellucci to Hersh's objection shows that the claim that by Gödel's results mathematics cannot be said to be absolutely certain is not self-defeating, and can be adopted to show that EDAs are not self-defeating as well in a twofold sense: an argument analogous to Cellucci's one may be developed to face De Cruz's objection, and such argument may be further refined incorporating Cellucci's response itself in it, to make it stronger. This paper aims at showing that the accusation of being self-defeating moved against EDAs is inadequate by elaborating an argument which can be considered an EDA and which can also be shown not to be self-defeating

Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on aver-age and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). An interesting debate has arisen with regard to which is the more adequate epistemological interpretation of PEM. Indeed, Hohwy maintains that given that PEM supports an inferential view of perception and cognition, PEM has to be considered as conveying an internalist epistemological perspective. Contrary to this view, Clark maintains that it would be incorrect to interpret in such a way the indirectness of the link between the world and our inner model of it, and that PEM may well be combined with an externalist epistemological perspective. The aim of this paper is to assess those two opposite interpretations of PEM. Moreover, it will be suggested that Hohwy’s position may be considerably strengthened by adopting Carlo Cellucci’s view on knowledge (2013).

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.

Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain controversial in many respects. The attempt to understand the ways models and inferences are made basically opens two roads. The first one is to produce an analysis of the role that models and inferences play in science. The second one is to produce an analysis of the way models and inferences are constructed, especially in the light of what science tells us about our cognitive abilities. The papers collected in this volume go both ways.

The semantic view of theories is normally considered to be an account of theories congenial to Scientific Realism. Recently, it has been argued that Ontic Structural Realism could be fruitfully applied, in combination with the semantic view, to some of the philosophical issues peculiarly related to biology. Given the central role that models have in the semantic view, and the relevance that mathematics has in the definition of the concept of model, the focus will be on population genetics, which is one of the most mathematized areas in biology. We will analyse some of the difficulties which arise when trying to use Ontic Structural Realism to account for evolutionary biology.