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.

In a recent paper James Bogen and James Woodward denounce a set of views on confirmation that they collectively brand 'IRS'. The supporters of these views cast confirmation in terms of Inferential Relations between observational and theoretical Sentences. Against 1RS accounts of confirmation, Bogen and Woodward unveil two main objections: (a) inferential relations are not necessary to model confirmation relations since many data are neither in sentential form nor can they be put in such a form and (b) inferential relations are not sufficient to model confirmation relations because the former cannot capture evidentially relevant factors about the detection processes and instruments that generate the data. In this paper I have a two-fold aim: (i) to show that Bogen and Woodward fail to provide compelling grounds for the rejection of IRS models and (ii) to highlight some of the models' neglected merits

Scientific realists endeavour to secure inferences from empirical success to approximate truth by arguing that despite the demise of empirically successful theories the parts of those theories responsible for their success do in fact survive theory change. If, as some anti-realists have recently suggested, those parts of theories that are responsible for their success are only identifiable in retrospect, namely as those that have survived, then the realist approach is trivialised for now success and survival are guaranteed to coincide. The aim of this paper is to counter this argument by identifying successful theory parts independently from their survival

Everything you always wanted to know about structural realism but were afraid to ask Content Type Journal Article Pages 227-276 DOI 10.1007/s13194-011-0025-7 Authors Roman Frigg, Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE UK Ioannis Votsis, Philosophisches Institut, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, Geb. 23.21/04.86, 40225 Düsseldorf, Germany Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 2

Barring some civilisation-ending natural or man-made catastrophe, future scientists will likely incorporate fully fledged artificially intelligent agents in their ranks. Their tasks will include the conjecturing, extending and testing of hypotheses. At present human scientists have a number of methods to help them carry out those tasks. These range from the well-articulated, formal and unexceptional rules to the semi-articulated rules-of-thumb and intuitive hunches. If we are to hand over at least some of the aforementioned tasks to artificially intelligent agents, we need to find ways to make explicit and ultimately formal, not to mention computable, the more obscure of the methods that scientists currently employ with some measure of success in their inquiries. The focus of this talk is a problem for which the available solutions are at best semi-articulated and far from perfect. It concerns the question of how to conjecture new hypotheses or extend existing ones such that they do not save phenomena in gerrymandered or ad hoc ways. This talk puts forward a fully articulated formal solution to this problem by specifying what it is about the internal constitution of the content of a hypothesis that makes it gerrymandered or ad hoc. In doing so, it helps prepare the ground for the delegation of a full gamut of investigative duties to the artificially intelligent scientists of the future

A stalwart view in the philosophy of science holds that, even when broadly construed so as to include theoretical auxiliaries, theories cannot make direct contact with observations. This view owes much to Bogen and Woodward’s influential distinction between data and phenomena. According to them, data are typically the kind of things that are observable or measurable like "bubble chamber photographs, patterns of discharge in electronic particle detectors and records of reaction times and error rates in various psychological experiments". Phenomena are physical processes that are typically unobservable. Examples of the latter category include "weak neutral currents, the decay of the proton, and chunking and recency effects in human memory". Theories, in Bogen and Woodward’s view, are utilised to systematically explain, infer and predict phenomena, not data. The relationship between theories and data is rather indirect. Data count as evidence for phenomena and the latter in turn count as evidence for theories. This view is becoming increasingly influential, Stathis Psillos and Mauricio Suárez ). In this paper I argue contrary to this view that in various significant and well-known cases theories do make direct contact with the help of suitable auxiliaries

Empiricism has been a pivotal philosophical topic for more than two millennia. Several Sophists, Aristotle, the Epicureans, Sextus Empiricus, Francis Bacon, Locke, Hume, Mill, Mach and the Logical Empiricists represent a long line of historically influential empiricists who share a prioritising of the sensory over all other forms of knowledge. The latest influential incarnation, Bas van Fraassen’s constructive empiricism, takes science to aim at empirically adequate theories, i.e. theories that save all and only the observable phenomena. Roughly put, an object or phenomenon is observable in van Fraassen’s view if a properly functioning member of the human epistemic community can detect it via their unaided senses. Scientific realists and other critics have contested this notion citing among other reasons that most knowledge in natural science concerns objects that can only be detected with instruments, i.e. those that are unobservable and hence unknowable by van Fraassen’s lights. Beg-the-question accusations fly back and forth. As a consequence a stalemate has ensued in the scientific realism debate

A question in the philosophy of science that has engrossed the minds of many eminent thinkers is the epistemological one of what kind of knowledge, if any, science reveals of the physical world. Answers to this question are typically classified as either realist or anti-realist.1 Structural Realism, as part of its name suggests, is a position on the realist side of the divide. In very simple terms, its advocates hold that our epistemic access to the world, so far as its non-observable part is concerned, is restricted to its structural features. The position can be traced back at least to the beginning of the twentieth century and has recently been attracting renewed interest

Structural realism is arguably one of the most influential movements to have emerged in philosophy of science in the last decade or so. Advocates of this movement attempt to answer epistemological and/or ontological questions concerning science by arguing that the key to all such questions is the mathematical formalism of a theory. This is so, according to structural realists, because the mathematical formalism encodes all and only what is important about a theory’s target domain, namely its structure. Almost without exception, discussions of structural realism centre on the natural sciences and in particular on modern physics. Given that a number of other sciences are less – indeed in some cases much less – mathematised than modern physics, does structural realism have anything informative to say about them? In this talk, I take up the task of articulating what structural realists ought to say about the social sciences if they are to consider themselves as offering a coherent philosophy for the whole of science

This paper examines what exactly amounts to the view commonly known as ‘perspectivism’, sometimes also known as ‘perspectivalism’. Of the various possible conceptions of perspectivism, four are singled out for closer inspection. Each makes clearly separable claims of varying strength. Their strength is judged against how much doubt they throw on key claims made by the view’s presumed arch-nemesis, namely realism. It is argued that the first two offer no serious challenge to realism. To be precise, it is argued that the first one is blatantly false, while the second is true but utterly harmless. Things are less clear cut with the other two conceptions of perspectivism. If true, they appear to present a more serious challenge to realism. It is argued that considerable doubts hang over their truth as to date arguments raised in their support are far from compelling. Even so, these conceptions cannot be summarily dismissed. Indeed, under more reasonable construals, they need not be for realism to survive and thrive. This is because claims these conceptions make in the name of perspectivism turn out to be easily coupled with realism.