Ioannis Votsis

This volume showcases the best of recent research in the philosophy of science. A compilation of papers presented at the EPSA 13, it explores a broad distribution of topics such as causation, truthlikeness, scientific representation, gender-specific medicine, laws of nature, science funding and the wisdom of crowds. Papers are organised into headings which form the structure of the book. Readers will find that it covers several major fields within the philosophy of science, from general philosophy of science to the more specific philosophy of physics, philosophy of chemistry, philosophy of the life sciences, philosophy of psychology, and philosophy of the social sciences and humanities, amongst others. This volume provides an excellent overview of the state of the art in the philosophy of science, as practiced in different European countries and beyond. ​It will appeal to researchers with an interest in the philosophical underpinnings of their own discipline, and to philosophers who wish to explore the latest work on the themes explored.

A structuralist perspective is one that sees the investigation of the structural features of a domain of interest as the primary goal of enquiry. This vision has shaped research programmes in fields as diverse as linguistics, literary criticism, aesthetics, sociology, anthropology, psychology, and various branches of philosophy. The focus of this paper is structuralism in the philosophy of science, and in particular those movements that have endeavoured to articulate a structural version of scientific realism, now commonly referred to as structural realism (SR). The paper provides a critical survey of the debates raging over structural realism: it provides explicit statements of the different positions as well as the arguments put forward to support them, clarifies how the different positions relate to one another, draws attention to hitherto neglected arguments, and evaluates criticisms launched against different strands of SR. Attention to the history of the field is paid in as far as this is essential to understanding the contemporary scene, but documenting the long and intricate development of SR is beyond the scope of this paper. We begin by introducing the set theoretic conception of structure on which many of the positions that we are concerned with rely (Section 2). In Section 3 we introduce the two main strands of epistemic structural realism, discuss the central objections levelled against them, most notably Newman's objection, and present the Ramsey sentence formulation. Section 4 is dedicated to a discussion of ontic structural realism. In Section 5 we offer some concluding remarks.

This paper discusses the role of data within scientific reasoning and as evidence for theoretical claims, arguing for the idea that data can yield theoretically grounded models and be inferred, predicted, or explained from/by such models. Contrary to Bogen and Woodward’s rejection of data-to-theory and theory-to-data inferences/predictions, we draw upon artificial intelligence as applied to science literature to argue that (a) many models are routinely inferred and predicted from the data and routinely used to infer and predict data, and (b) such models can, at least in some contexts, play the role of theories.

Scientific discovery is a neglected topic in the philosophy of science. Since around the middle of the last century, the received view has been that discovery is not governed by logic or, more generally, by rationality, but is a largely elusive and inscrutable process. Thankfully, not everyone has been persuaded by this mystical view. Given the recent cascade of developments in automation and AI, this means that now more than ever we need to carefully re-evaluate our attitude towards this view. This paper aims to do precisely that by exploring how such developments are reinvigorating the search for a logic of scientific discovery. Neuro-symbolic approaches to AI, it will be argued, offer hope in the reinstatement of the rationalist model, and the automation, of scientific discovery. A neuro-symbolic system is proposed that seeks to integrate several identified desiderata like the ability to detect patterns and to reason. On this proposal, both neural and symbolic methods are employed in the service of automating scientific discovery, but the former are conceived of as subservient to the latter. The rationale for this subservience relation is that any discoveries made need to be integrated into an accessible body of knowledge, and that can more easily be achieved with symbolic methods at the helm.

Empiricism has a long and venerable history. Aristotle, the Epicureans, Sextus Empiricus, Bacon, Locke, Hume, Mill, Mach and the Logical Empiricists, among others, represent a long line of historically influential empiricists who, one way or another, placed an emphasis on knowledge gained through the senses. In recent times the most highly articulated and influential edition of empiricism is undoubtedly Bas van Fraassen’s constructive empiricism. Science, according to this view, aims at empirically adequate theories, i.e. theories that save all and only the observable phenomena. Roughly put, something is observable in van Fraassen’s view if members of the human epistemic community can detect it with their unaided senses. Critics have contested this notion, citing, among other reasons, that much of what counts as knowledge for scientists, especially in the natural sciences, concerns things that are detectable only with instruments, i.e. things that are unobservable and hence unknowable by van Fraassen’s lights. The current paper seeks to overcome this objection by putting forth and defending a liberalised conception of observability and an associated, and accordingly liberalised, conception of empiricism. ‘Grounded observability’ and ‘grounded empiricism’, as we call them, unchain themselves from the burdens of traditional conceptions of experience, while at the same time tethering themselves to the source of epistemic credibility in the senses, and, hence to the true spirit of empiricism.

This volume showcases the best of recent research in the philosophy of science. A compilation of papers presented at the EPSA 13, it explores a broad distribution of topics such as causation, truthlikeness, scientific representation, gender-specific medicine, laws of nature, science funding and the wisdom of crowds. Papers are organised into headings which form the structure of the book. Readers will find that it covers several major fields within the philosophy of science, from general philosophy of science to the more specific philosophy of physics, philosophy of chemistry, philosophy of the life sciences, philosophy of psychology, and philosophy of the social sciences and humanities, amongst others. This volume provides an excellent overview of the state of the art in the philosophy of science, as practiced in different European countries and beyond. ​It will appeal to researchers with an interest in the philosophical underpinnings of their own discipline, and to philosophers who wish to explore the latest work on the themes explored.

This volume showcases the best of recent research in the philosophy of science. A compilation of papers presented at the EPSA 13, it explores a broad distribution of topics such as causation, truthlikeness, scientific representation, gender-specific medicine, laws of nature, science funding and the wisdom of crowds. Papers are organised into headings which form the structure of the book. Readers will find that it covers several major fields within the philosophy of science, from general philosophy of science to the more specific philosophy of physics, philosophy of chemistry, philosophy of the life sciences, philosophy of psychology, and philosophy of the social sciences and humanities, amongst others. This volume provides an excellent overview of the state of the art in the philosophy of science, as practiced in different European countries and beyond. ​It will appeal to researchers with an interest in the philosophical underpinnings of their own discipline, and to philosophers who wish to explore the latest work on the themes explored.

The recent surge of populism, nationalism and authoritarian tendencies in the political arena as well as the widespread propagation of fake news, conspiracy theories and disinformation in social media are increasingly worrisome and pose a severe threat to democratic societies and the rule of law. Political decisions in such societies must, first and foremost, be guided by evidence and reason. Unfortunately, the events of the last years have shown that the existing institutions and mechanisms are more and more vulnerable to misuse, leaving democracies well and truly adrift from wholesome deliberative practices. It thus seems imperative that those institutions and mechanisms be redesigned in an attempt to make democracy “foolproof” – that is, to provide some sort of immunisation against these threats, even if it is, of course, nearly impossible to achieve such a lofty ideal in practice. This is the second part of a special issue that explores ways to make democracies “foolproof” in the explicated sense.

The events of the year 2016 have led many critical observers to doubt the stability and longevity of democracy. Ideally, democracy effectuates the rule of reason. Debates in elected assemblies and in society as a whole should serve the process of finding best reasons for political decisions. However, the mechanisms that currently produce such decisions are vulnerable to misuse. Arguably, they need to be redesigned in an attempt to make them “foolproof” - i.e., to design them in a way to make misuse inherently impossible or to minimize its negative consequences.

In this paper, I investigate two potential ways to experimentally test the thesis that observation is theory-laden. One is a proposal due to Schurz (J Gen Philos Sci 46:139–153, 2015) and the other my own. The two are compared and found to have some features in common. One such feature is that both proposals seek to create conditions that compel test subjects with diverse theoretical backgrounds to resort to bare (or at least as bare as possible) observational judgments. Thus, if judgments made under those conditions are convergent across test subjects, the said convergence would lend credibility to the view that theory-neutral observations are feasible. This still leaves the question of why any such convergence exists unanswered. Towards the end of the paper, it is argued that the best explanation for observational judgment convergence is the veridicality of those judgments.

Of all the sub-disciplines of philosophy, the philosophy of science has perhaps the most privileged relationship to information theory. This relationship has been forged through a common interest in themes like induction, probability, confirmation, simplicity, non-ad hocness, unification and, more generally, ontology. It also has historical roots. One of the founders of algorithmic information theory, Ray Solomonoff, produced his seminal work on inductive inference as a direct result of grappling with problems first encountered as a student of the influential philosopher of science Rudolf Carnap. There are other such historical connections between the two fields. Alas, there is no space to explore them here. Instead this essay will restrict its attention to a broad and accessible overview of the aforementioned common themes, which, given their nature, mandate an emphasis on AIT as opposed to general information theory.

Science is increasingly becoming automated. Tasks yet to be fully automated include the conjecturing, modifying, extending and testing of hypotheses. At present scientists have an array of methods to help them carry out those tasks. These range from the well-articulated, formal and unexceptional rules to the semi-articulated and variously understood rules-of-thumb and intuitive hunches. If we are to hand over at least some of the aforementioned tasks to machines, we need to clarify, refine and make formal, not to mention computable, even the more obscure of the methods scientists successfully employ in their inquiries. The focus of this essay is one such less-than-transparent methodological rule. I am here referring to the rule that ad hoc hypotheses ought to be spurned. This essay begins with a brief examination of some notable conceptions of ad hoc-ness in the philosophical literature. It is pointed out that there is a general problem afflicting most such conceptions, namely the intuitive judgments that are supposed to motivate them are not universally shared. Instead of getting bogged down in what ad hoc-ness exactly means, I shift the focus of the analysis to one undesirable feature often present in alleged cases of ad hoc-ness. I call this feature the ‘monstrousness’ of a hypothesis. A fully articulated formal account of this feature is presented by specifying what it is about the internal constitution of a hypothesis that makes it monstrous. Using this account, a monstrousness measure is then proposed and somewhat sketchily compared with the minimum description length approach.

This is certainly true. Simulationists and experimentalists face equally relevant challenges when it comes to establishing that the results of their simulation or experiment are informative about the real world. But it is one thing to point this fact out, and it is another to understand how those challenges are overcome, under differing circumstances, and in varying contexts. It is here that Marcel Boumans’ contribution becomes especially valuable. He presents an example from economics in which a mathematical model performs the role, not of a representational entity, but of a data sensor. Boumans argues, and I concur, that the manner in which such models are assessed is particularly interesting. They cannot be assessed merely by being confronted with facts about the world, since these models are themselves used in generating the relevant data about the phenomena in question. The relevant strategy for assessing these models is calibration. In other words, rather than being held side by side with the relevant bit of the world, the models are held up against other instruments that are antecedently believed to be reliable sources of data.

When it comes to name-calling, structural realists have heard pretty much all of it. Among the many insults, they have been called ‘empiricist anti-realists’ but also ‘traditional scientific realists’. Obviously the collapse accusations that motivate these two insults cannot both be true at the same time. The aim of this paper is to defend the epistemic variety of structural realism against the accusation of collapse to traditional scientific realism. In so doing, I turn the tables on traditional scientific realists by presenting them with a dilemma. They can either opt for a construal of their view that permits epistemic access to non-structural features of unobservables but then face the daunting task of substantiating a claim that seems to have little hope of being true or they can drop the requirement of epistemic access to non-structural features but then face a collapse to epistemic structural realism. There is thus only one well supported way to be a realist. No wonder then that many traditional scientific realists have over the years expressed views that are strikingly similar to epistemic structural realism. It is high time to let these epistemic structural realists out of the closet.

The paper is divided into three parts. The first part identifies one of the main problems with many current accounts of the notion of explanation: The unreasonable demand, proposed by Michael Scriven and subsequently adopted by many philosophers, that we must square our account of scientific explanation to our intuitions about explanations in everyday contexts. It is first pointed out that the failure to provide a satisfactory account is not endemic to the notion of explanation, i.e. it is widespread amongst notions. Many of the notions considered in philosophical contexts originate and have a function in broader everyday contexts. Indeed, in evaluating accounts of these notions we rely on intuitions that originate in these broader contexts. Yet, we rarely seem to question the appropriateness of these intuitions in more restricted, in this case scientific, contexts. I argue against this complacency, pointing out that our intuitions can often be inconsistent.

In the first part of this paper we investigate how scientific theories can be represented by frames. Different kinds of scientific theories can be distinguished in terms of the systematic power of their frames. In the second part we outline the central questions and goals of our research project. In the third and final part of this paper we show that frame-representation is a useful tool in the comparison of the theories of phlogiston and oxygen, despite those theories being traditionally conceived as incommensurable. The frame-theoretic representation reveals common attributes, values and ultimately structural correspondence relations between the two theories. In our view this outcome lends credence to a structural realist view of science.

An integral part of the schooling of scientists, especially experimental ones, is the cultivation of the significance and role of scientific evidence. Naturally this schooling is not conducted in vacuuo. Budding scientists already have experiences of, and intuitions about, the use of evidence in everyday life. In this talk I take a sustained look at the relations between common-sense notions of evidence and scientific ones. Among other things, I argue that scientific notions of evidence and associated practices are in many ways conservative extensions of what is best about our common-sense notions and practices. This contradicts a rather widely held but often tacit view that science and its notions are largely insular.

Structural realists of nearly all stripes endorse the structural continuity claim. Roughly speaking, this is the claim that the structure of successful scientific theories survives theory change because it has latched on to the structure of the world. In this paper I elaborate, elucidate and modify the structural continuity claim and its associated argument. I do so without presupposing a particular conception of structure that favours this or that kind of structural realism. Instead I focus on how structural realists can best account for the neutrally formulated historical facts. The result, I hope, crystallises some of the shared commitments, desiderata and limits of structural realists

Most scientific realists nowadays would endorse an argument like the following: The empirical and explanatory success of theories or theory-parts is a good indicator of their approximate truth. In turn, approximate truth is a good indicator of referential success. Successor theories typically preserve all of the empirical and explanatory success of their predecessors as well as add to it. They are thus in general strictly more approximately true than their predecessors. Moreover, by preserving their predecessors’ approximately true parts they preserve the referential success the predecessors probably enjoy. This implies that successor theories that are more approximately true than their predecessors are typically also referentially continuous with them

This chapter traces the development of structural realism within the scientific realism debate and the wider current of structuralism that has swept the philosophy of the natural sciences in the twentieth century.1 The primary aim is to make perspicuous the many manifestations of structural realism and their underlying claims. Among other things, I will compare structural realism’s various manifestations in order to throw more light onto the relations between them. At the end of the chapter, I will identify the main objections raised against the epistemic form of structural realism. This last task will pave the way for the evaluation of the structural realist answer to the main epistemological question, an evaluation that will be central to the rest of this dissertation.