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.

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.

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.

Kyle Stanford (2006) argues that the most serious and powerful challenge to scientific realism has been neglected. The problem of unconceived alternatives (PUA), as he calls it, holds that throughout history scientists have failed to conceive alternative theories roughly equally wellconfirmed (by the available evidence) to the theories of the day and, crucially, that such alternatives eventually were conceived and adopted by some section of the scientific community. PUA is a version of the argument from the underdetermination of theories by evidence (UTE) but departs from it in two significant ways: (i) there is a shift from artificially produced rival theories - of the kind typically talked about in the underdetermination debate - to actual rivals and (ii) there is a shift from empirically equivalent rivals to rivals that are equally well-confirmed by the available evidence at a given point in time. In this talk I will argue that by these shifts Stanford successfully manages to find more historical evidence for PUA (than do proponents of UTE), but only at the expense of making his thesis ineffectual.

This paper counters an objection raised against one of Bertrand Russell’s lesser-known epistemological views, viz. ‘‘structural realism’’ (SR). In short, SR holds that at most we have knowledge of the structure of the external (i.e., physical) world. M. H. A. Newman’s allegedly fatal objection is that SR is either trivial or false. I argue that the accusation of triviality is itself empty since it fails to establish that SR knowledge claims are uninformative. Moreover, appealing to Quine’s notion of ontological relativity, I suggest that far from being false, SR knowledge claims seem to be the most that we can hope for.

Structural realism has various diverse manifestations. One of the things that structural realists of all stripes have in common is their endorsement of what I call 'the structural continuity claim'. Roughly, this is the idea that the structure of successful scientific theories survives theory change because it has latched on to the structure of the world. In this talk 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 but instead by concentrating on neutrally formulated historical facts. The result, I hope, throws light on what a structural realist must do to evidentially benefit from the historical record of science. The implicit argument underwriting the structural continuity claim can be reconstructed as follows: Premise (1) Only structural elements of predictively and explanatorily successful scientific theories have been (and will be) preserved through theory change. Premise (2): Preservation of an element implies or at least is good evidence for its (approximate) truth. Premise (3): Non-preservation of an element implies or at least is good evidence for its falsity. Conclusion: It is probably the case that only structural elements are (approximately) true. In this summary I restrict my comments to the first premise. Several points can be raised with respect to it. First, not all structures are created equal. Some play no active role in the predictive and explanatory success of a theory because they do not correspond to any structure in the world. Their non-preservation would therefore not encumber the structural realist. Traditional scientific realists have long employed a distinction between essential and idle posits to weed out those elements of theories that played no substantial role in their predictive and explanatory success..

Let us call ‘veridicalism’ the view that perceptual beliefs and observational reports are largely truthful. This paper aims to make a case for veridicalism by, among other things, examining in detail and ultimately deflating in import what many consider to be the view’s greatest threat, the so-called ‘theory-ladenness’ of perception and/or observation. In what follows, it is argued that to the extent that theoretical factors influence the formation of perceptual beliefs and observational reports, as theory-ladenness demands, that influence is typically not detrimental to their veridicality or at least not irreversibly so. Central to the defence of veridicalism are two principles: that of internal similarities and that of internal dissimilarities

In scientific realist eyes we are only warranted to assert that a theory is true or approximately true if that theory enjoys considerable explanatory and predictive success. The most well known challenge to this claim, the pessimistic meta-induction, holds that the history of science is replete with successful theories that are now considered false. In effect, this challenge raises doubts about the reliability of inferences from explanatory and predictive success to (approximate) truth. The main realist reaction has been to 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. In this talk I consider what the modern kinetic theory of heat managed to salvage from the outdated caloric theory and whether the inter-theoretic relations between the two theories support a realist view of science

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

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

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.

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