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.

The epistemic form of structural realism asserts that our knowledge of the world is restricted to its structural features. Several proponents of this view assume that the world possesses non-structural features; features which, according to their view, cannot be known. In other words, they assume that there is, or, there ought to be (on the basis of normative arguments in epistemology), always a gap between our epistemological and ontological commitments. The ontic form of structural realism denies that this is, or ought to be, the case. Proponents of this view argue that the perfect alignment of epistemological and ontological commitments is a highly desirable metatheoretical feature. They argue this on the basis of the prima facie sensible principle that our ontological commitments ought never to overreach our epistemic ones. Naturally the issue of alignment transcends the debate between the epistemic and the ontic structural realists. Is it in principle impossible for there to be circumstances under which we ought to subscribe to the misalignment of epistemological and ontological commitments? What do the different answers to this question entail for ontic structural realism?

Are sensory experiences, perceptual beliefs and observation reports faithful encoders of truthful information about the world? The theory-ladenness thesis poses an important challenge to answering this question in the affirmative. Roughly the thesis holds that theoretical factors affect the content of those experiences, beliefs and reports. In other words, it holds that their content is laden with theory. Theoretical factors here are construed broadly so as to include scientific theories, beliefs and cognitive processes. Two crucial questions arise in relation to the theory-ladenness thesis. First, how pervasive is theory-ladenness? And, second, what is the extent of the distortion theory-ladenness has on the content of experiences, perceptual beliefs and observation reports? If theory-ladenness is not only pervasive, i.e. if it affects the majority of experiences, perceptual beliefs and observation reports, but also highly distortive way then those experiences, beliefs and reports can ..

This paper elaborates on the following correspondence theorem : if theory T has been empirically successful in a domain of applications A, but was superseded later on by a different theory T* which was likewise successful in A, then under natural conditions T contains theoretical expressions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varphi}$$\end{document} which were responsible for T’s success and correspond to certain theoretical expressions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varphi}^{*}$$\end{document} of T*. I illustrate this theorem at hand of the phlogiston versus oxygen theories of combustion, and the classical versus relativistic theories of mass. The ontological consequences of the theorem are worked out in terms of the indirect reference and partial truth. The final section explains how the correspondence theorem may justify a weak version of scientific realism without presupposing the no-miracles argument.

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.

Structural realism is a rather popular view in philosophy of science. As with many popular views, sprouting is never far behind. No sprout has had as much grip on the view’s image as ontic structural realism. Indeed its supporters have such a stranglehold that ‘structural realism’ has almost become a byword for their views. In this talk, I want to redress this imbalance by returning to structural realism’s humble epistemic beginnings to examine exactly what made the view so attractive in the first place. To this effect, I will reconstruct several arguments – some of which little known – proposed in the early part of the twentieth century in support of the epistemic version of structural realism. Not wanting to dwell too much on the past, I will then switch to more recent arguments both for and against the position. A careful evaluation of these arguments will hopefully provide useful information as to what form, if any, epistemic structural realism must take in order to be a viable alternative to its direct competitors, namely standard scientific realism and constructive empiricism

The aim of this paper is to put in place some cornerstones in the foundations for an objective theory of confirmation by considering lessons from the failures of predictivism. Discussion begins with a widely accepted challenge, to find out what is needed in addition to the right kind of inferential–semantical relations between hypothesis and evidence to have a complete account of confirmation, one that gives a definitive answer to the question whether hypotheses branded as “post hoc monsters” can be confirmed. The predictivist view is then presented as a way to meet this challenge. Particular attention is paid to Worrall’s version of predictivism, as it appears to be the most sophisticated of the lot. It is argued that, despite its faults, his view turns our heads in the right direction by attempting to remove contingent considerations from confirmational matters. The demand to remove such considerations becomes the first of four cornerstones. Each cornerstone is put in place with the aim to steer clear of the sort of failures that plague various kinds of predictivism. In the process, it becomes obvious that the original challenge is wrongheaded and in need of revision. The paper ends with just such a revision

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

The pessimistic induction argument, most often associated with Larry Laudan, is now widely considered to be one of the main obstacles for realism. Put simply, the argument holds that since past predictively successful scientific theories have eventually been discarded, we have inductive evidence that our current theories will also be discarded one day. More precisely, Laudan undermines the inference from the explanatory and predictive success of a theory to its approximate truth and referential success. This paper criticises a particular kind of realist reply to Laudan.

Under what circumstances, if any, are we warranted to assert that a theory is true or at least approximately true? Scientific realists answer that such assertions are warranted only for those theories that enjoy explanatory and predictive success. A number of challenges to this answer have emerged, chief among them the argument from pessimistic meta-induction. According to this challenge, the history of science supplies ample evidence against realism in the form of successful theories that are now considered false. The main realist reaction to this challenge questions the legitimacy of the pessimistic meta-inductivist inference. Advocates of this approach argue that upon closer scrutiny the historical record can be reconciled with scientific realism. When a successful theory is abandoned, not all of its components are discarded but only those that are inessential or idle for the theory’s success. Their abandonment is thus inconsequential for the realist. So long as the essential components survive into the new theory there is no cause for alarm. More precisely, an outdated theory T which enjoyed some measure of success must, according to the realist, be: (i) partially true precisely because some of its theoretical claims are..

The concern with the objectivity of observations is as old as empirical study itself. Protagoras’ assertion that ‘Man is the measure of all things’, though not particularly directed at observations, raised general doubts concerning the objectivity of our beliefs. In the twentieth century, the concern has been brought under the banner ‘theory-ladenness of observation’. In simple terms, since observations are conducted and formulated in theory-specific contexts, they are inadvertently imbued with the prejudices of those contexts - the terms ‘theory’ and ‘observation’ are here broadly construed. Accordingly, it is argued, observations cannot be neutral in the testing of theories.

Consider the aims of the following three influential philosophical views. The semantic view of theories aims to supply the proper form and content of scientific theories. Structural realism aspires to delimit the epistemology and ontology of science. Mathematical structuralism seeks to reveal the epistemological and ontological nature of – you guessed it – mathematical objects. Given their divergent aims they may seem like unlikely bedfellows, but the semantic view of theories, structural realism and mathematical structuralism share enough ground to be able to benefit or suffer from some of the same reasons. What unites the three views is the purely structural analysis of their respective subject matter. The semantic view sees theories as nothing more than families of models, i.e. sets of structures. Representation, according to this view, is a matter of establishing mappings between some models of the theory and target domains. Structural realism judges scientific knowledge and perhaps even ontology to be wholly structural. Mathematical structuralism proclaims that the objects of mathematics are specifiable only up to isomorphism.