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  1. A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Olav B. Vassend - 2020 - British Journal for the Philosophy of Science 71 (4):1359-1383.
    Bayesianism and likelihoodism are two of the most important frameworks philosophers of science use to analyse scientific methodology. However, both frameworks face a serious objection: much scientific inquiry takes place in highly idealized frameworks where all the hypotheses are known to be false. Yet, both Bayesianism and likelihoodism seem to be based on the assumption that the goal of scientific inquiry is always truth rather than closeness to the truth. Here, I argue in favour of a verisimilitude framework for inductive (...)
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  • An Argument for the Principle of Indifference and Against the Wide Interval View.John E. Wilcox - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (1):65-87.
    The principle of indifference has fallen from grace in contemporary philosophy, yet some papers have recently sought to vindicate its plausibility. This paper follows suit. In it, I articulate a version of the principle and provide what appears to be a novel argument in favour of it. The argument relies on a thought experiment where, intuitively, an agent’s confidence in any particular outcome being true should decrease with the addition of outcomes to the relevant space of possible outcomes. Put simply: (...)
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  • An Argument for the Principle of Indifference and Against the Wide Interval View.John E. Wilcox - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (1):65-87.
    The principle of indifference has fallen from grace in contemporary philosophy, yet some papers have recently sought to vindicate its plausibility. This paper follows suit. In it, I articulate a version of the principle and provide what appears to be a novel argument in favour of it. The argument relies on a thought experiment where, intuitively, an agent’s confidence in any particular outcome being true should decrease with the addition of outcomes to the relevant space of possible outcomes. Put simply: (...)
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  • Explanatory Consolidation: From 'Best' to 'Good Enough'.Finnur Dellsén - forthcoming - Philosophy and Phenomenological Research.
    In science and everyday life, we often infer that something is true because it would explain some set of facts better than any other hypothesis we can think of. But what if we have reason to believe that there is a better way to explain these facts that we just haven't thought of? Wouldn't that undermine our warrant for believing the best available explanation? Many philosophers have assumed that we can solve such underconsideration problems by stipulating that a hypothesis should (...)
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  • Confirmation by Explanation: A Bayesian Justification of IBE.Marko Tesic, Benjamin Eva & Stephan Hartmann - unknown
    We provide a novel Bayesian justification of inference to the best explanation. More specifically, we present conditions under which explanatory considerations can provide a significant confirmatory boost for hypotheses that provide the best explanation of the relevant evidence. Furthermore, we show that the proposed Bayesian model of IBE is able to deal naturally with the best known criticisms of IBE such as van Fraassen?s?bad lot? argument.
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  • The Big Data razor.Ezequiel López-Rubio - 2020 - European Journal for Philosophy of Science 10 (2):1-20.
    Classic conceptions of model simplicity for machine learning are mainly based on the analysis of the structure of the model. Bayesian, Frequentist, information theoretic and expressive power concepts are the best known of them, which are reviewed in this work, along with their underlying assumptions and weaknesses. These approaches were developed before the advent of the Big Data deluge, which has overturned the importance of structural simplicity. The computational simplicity concept is presented, and it is argued that it is more (...)
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  • The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - forthcoming - Philosophy and Technology:1-21.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...)
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  • Does IBE Require a ‘Model’ of Explanation?Frank Cabrera - 2017 - British Journal for the Philosophy of Science 71 (2):727-750.
    In this article, I consider an important challenge to the popular theory of scientific inference commonly known as ‘inference to the best explanation’, one that has received scant attention.1 1 The problem is that there exists a wide array of rival models of explanation, thus leaving IBE objectionably indeterminate. First, I briefly introduce IBE. Then, I motivate the problem and offer three potential solutions, the most plausible of which is to adopt a kind of pluralism about the rival models of (...)
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  • Humean Laws, Explanatory Circularity, and the Aim of Scientific Explanation.Chris Dorst - 2019 - Philosophical Studies 176 (10):2657-2679.
    One of the main challenges confronting Humean accounts of natural law is that Humean laws appear to be unable to play the explanatory role of laws in scientific practice. The worry is roughly that if the laws are just regularities in the particular matters of fact (as the Humean would have it), then they cannot also explain the particular matters of fact, on pain of circularity. Loewer (2012) has defended Humeanism, arguing that this worry only arises if we fail to (...)
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  • A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Vassend Olav Benjamin - unknown
    Bayesianism and likelihoodism are two of the most important frameworks philosophers of science use to analyse scientific methodology. However, both frameworks face a serious objection: much scientific inquiry takes place in highly idealized frameworks where all the hypotheses are known to be false. Yet, both Bayesianism and likelihoodism seem to be based on the assumption that the goal of scientific inquiry is always truth rather than closeness to the truth. Here, I argue in favor of a verisimilitude framework for inductive (...)
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  • String Theory, Non-Empirical Theory Assessment, and the Context of Pursuit.Frank Cabrera - 2018 - Synthese:1-29.
    In this paper, I offer an analysis of the radical disagreement over the adequacy of string theory. The prominence of string theory despite its notorious lack of empirical support is sometimes explained as a troubling case of science gone awry, driven largely by sociological mechanisms such as groupthink (e.g. Smolin 2006). Others, such as Dawid (2013), explain the controversy by positing a methodological revolution of sorts, according to which string theorists have quietly turned to nonempirical methods of theory assessment given (...)
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  • Cladistic Parsimony, Historical Linguistics and Cultural Phylogenetics.Frank Cabrera - 2017 - Mind and Language 32 (1):65-100.
    Here, I consider the recent application of phylogenetic methods in historical linguistics. After a preliminary survey of one such method, i.e. cladistic parsimony, I respond to two common criticisms of cultural phylogenies: that cultural artifacts cannot be modeled as tree-like because of borrowing across lineages, and that the mechanism of cultural change differs radically from that of biological evolution. I argue that while perhaps remains true for certain cultural artifacts, the nature of language may be such as to side-step this (...)
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