For many years—and with some energy since Laudan’s “Confutation of Convergent Realism” —the scientific realist has sought to accommodate examples of false-yet-successful theories in the history of science. One of the most prominent strategies is to identify ‘success fueling’ components of false theories that themselves are at least approximately true. In this article I develop both sides of the debate, introducing new challenges from the history of science as well as suggesting adjustments to the divide et impera realist strategy. A (...) new ‘recipe’ for the prospective identification of working/idle posits is considered. (shrink)

Synthetic biology is an umbrella term that covers a range of aims, approaches, and techniques. They are all brought together by common practices of analogizing, synthesizing, mechanicizing, and kludging. With a focus on kludging as the connection point between biology, engineering, and evolution, I show how synthetic biology’s successes depend on custom-built kludges and a creative, “make-it-work” attitude to the construction of biological systems. Such practices do not fit neatly, however, into synthetic biology’s celebration of rational design. Nor do they (...) straightforwardly embody Richard Feynman’s “last blackboard” statement that without creating something it cannot be understood. Reflecting further on the relationship between synthetic construction and knowledge making gives philosophy of science new avenues of insight into scientific practice. (shrink)

Proponents of Bayesian confirmation theory believe that they have the solution to a significant, recalcitrant problem in philosophy of science. It is the identification of the logic that governs evidence and its inductive bearing in science. That is the logic that lets us say that our catalog of planetary observations strongly confirms Copernicus’ heliocentric hypothesis; or that the fossil record is good evidence for the theory of evolution; or that the 3oK cosmic background radiation supports big bang cosmology. The definitive (...) solution to this problem would be a significant achievement. The problem is of central importance to philosophy of science, for, in the end, what distinguishes science from myth making is that we have good evidence for the content of science, or at least of mature sciences, whereas myths are evidentially ungrounded fictions. The core ideas shared by all versions of Bayesian confirmation theory are, at a good first approximation, that a scientist’s beliefs are or should conform to a probability measure; and that the incorporation of new evidence is through conditionalization using Bayes’ theorem. While the burden of this chapter will be to inventory why critics believe this theory may not be the solution after all, it is worthwhile first to summarize here the most appealing virtues of this simple account. There are three. First, the theory reduces the often nebulous notion of a logic of.. (shrink)

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...) light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. 1 Introduction2 A Climate Case Study3 The Bayesian Method vis-à-vis Intuitions4 Classical Tests vis-à-vis Intuitions5 Classical Model-Selection Methods vis-à-vis Intuitions 5.1 Introducing classical model-selection methods 5.2 Two cases6 Re-examining Our Case Study7 Conclusion. (shrink)

Recent contributions to the philosophy of the social sciences have motivated ontological commitments using appeals to the social sciences (_naturalized_ social ontologies). These arguments rely on social scientific realism about the social sciences, the view that our social scientific theories are approximately true. I apply a distinction formulated in metaontology between ontologically loaded and unloaded meanings of existential quantification to argue that there is a pragmatic approach to naturalized social ontology that is minimally realist (it treats existence claims as true (...) or false) but that is ontologically austere. I argue that the extant arguments may be construed in terms of this pragmatic approach. The result is an approach to social ontology that is deflationist about naturalized social ontology. (shrink)

The main point of this paper is to underscore the link between simplicity and truth in an unsupervised machine learning context. More precisely, we argue that parametric and dimensional simplicity are not indicators of truth but the methodological principle that urges us to pay attention to such notions of simplicity is truth conducive. The truth that we are looking for are specific geometrical shapes and we know which algorithm can find which shapes provided that we pay attention to parametric and (...) dimensional simplicity. (shrink)

Empirical success is a central criterion for scientific decision-making. Yet its understanding in philosophical studies of science deserves renewed attention: Should philosophers think differently about the advancement of science when they deal with the uncertainty of outcome in ongoing research in comparison with historical episodes? This paper argues that normative appeals to empirical success in the evaluation of competing scientific explanations can result in unreliable conclusions, especially when we are looking at the changeability of direction in ongoing investigations. The challenges (...) we encounter arise from the inherent dynamics of disciplinary and experimental objectives in research practice. In this paper we discuss how these dynamics inform the evaluation of empirical success by analyzing three of its requirements: data accommodation, instrumental reliability, and predictive power. We conclude that the assessment of empirical success in developing inquiry is set against the background of a model’s interactive success and prospective value in an experimental context. Our argument is exemplified by the analysis of an apparent controversy surrounding the model of a quantum nose in research on olfaction. Notably the public narrative of this controversy rests on a distorted perspective on measures of empirical success. (shrink)

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...) light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. (shrink)

Among philosophical analyses of Darwin’s Origin, a standard view says the theory presented there had no concrete observational consequences against which it might be checked. I challenge this idea with a new analysis of Darwin’s principal geographical distribution observations and how they connect to his common ancestry hypothesis.

Bayesian model selection has frequently been the focus of philosophical inquiry (e.g., Forster, Br J Philos Sci 46:399–424, 1995; Bandyopadhyay and Boik, Philos Sci 66:S390–S402, 1999; Dowe et al., Br J Philos Sci 58:709–754, 2007). This paper argues that Bayesian model selection procedures are very diverse in their inferential target and their justification, and substantiates this claim by means of case studies on three selected procedures: MML, BIC and DIC. Hence, there is no tight link between Bayesian model selection and (...) Bayesian philosophy. Consequently, arguments for or against Bayesian reasoning based on properties of Bayesian model selection procedures should be treated with great caution. (shrink)

The scientific realism debate in philosophy of science raises some intriguing methodological issues. Scientific realism posits a link between a scientific theory's observational and referential success. This opens the possibility of testing the thesis empirically, by searching for evidence of such a link in the record of theories put forward in the history of science. Many realist philosophers working today propose case study methodology as a way of carrying out such a test. This article argues that a qualitative method such (...) as case study methodology is not adequate for this purpose, for two reasons: to test scientific realism is to pose an effects‐of‐causes question, and observational and referential success are quantities that theories possess to a greater or lesser degree. The article concludes that an empirical test of scientific realism requires a quantitative method. (shrink)

Machine learning is a scientific discipline that can be divided into two main branches: supervised machine learning and unsupervised machine learning. In this paper, we aim to show just how simplicity matters in unsupervised contexts. This is important because unsupervised machine learning algorithms have barely received any attention in philosophy. Yet, there is a direct link between simplicity and truth in unsupervised contexts that we do not find in their supervised counterparts. This has thus far evaded philosophical discussions on simplicity.

According to a popular strategy amongst economists and philosophers, in order to solve the problem of interpersonal utility comparisons, we have to look at how ordinary people make such comparisons in everyday life. The most recent attempt to develop this strategy has been put forward by Goldman in his “Simulation and Interpersonal Utility” (Ethics 4:709–726, 1995). Goldman claims, first, that ordinary people make interpersonal comparisons by simulation and, second, that simulation is reliable for making interpersonal comparisons. In this paper, I (...) focus on Goldman’s latter claim. After updating Goldman’s account of how ordinary people make interpersonal comparisons in the light of Goldman’s newest formulation of his simulation theory of mental ascription (Goldman, Simulating Minds. Oxford University Press, Oxford, 2006), I develop and assess Goldman’s arguments in favour of the reliability of simulation for interpersonal comparisons. I argue that, under certain conditions, there may be room for a scientifically acceptable solution to the problem of interpersonal utility comparisons. (shrink)

This article introduces the notion of a kind of inference called black box measurement and argues that it is both historically and philosophically significant. Thinking about certain classic cases of underdetermination using this notion can give us a better understanding of how these cases are resolved. I take the main philosophical problem of black box measurement to be the justification of assumptions that are needed in order to make these measurements. I sketch some ways in which such enabling assumptions might (...) be justified. (shrink)

In my dissertation I define historical disciplines as disciplines that aim to give a historical interpretation of the evidence. Phylogenetic systematics is a historical discipline and therefore in my definition phylogenies should be thought of as historical interpretations of relationships between taxa.

In this paper I compare parametric and nonparametric regression models with the help of a simulated data set. Doing so, I have two main objectives. The first one is to differentiate five concepts of simplicity and assess their respective importance. The second one is to show that the scope of the existing philosophical literature on simplicity and model selection is too narrow because it does not take the nonparametric approach into account, S112–S123, 2002; Forster and Sober in The British Journal (...) for the Philosophy of Science 45, 1–35, 1994; Forster, 2001, in Philosophy of Science 74, 588–600, 2007; Hitchcock and Sober in The British Journal for the Philosophy of Science 55, 1–34, 2004; Mikkelson in Philosophy of Science 73, 440–447, 2006; Baker 2013). More precisely, I point out that a measure of simplicity in terms of the number of adjustable parameters is inadequate to characterise nonparametric models and to compare them with parametric models. This allows me to weed out false claims about what makes a model simpler than another. Furthermore, I show that the importance of simplicity in model selection cannot be captured by the notion of parametric simplicity. ‘Simplicity’ is an umbrella term. While parametric simplicity can be ignored, there are other notions of simplicity that need to be taken into consideration when we choose a model. Such notions are not discussed in the previously mentioned literature. The latter therefore portrays an incomplete picture of why simplicity matters when we choose a model. Overall I support a pluralist view according to which we cannot give a general and interesting justification for the importance of simplicity in science. (shrink)