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  1. On the Imprecision of Full Conditional Probabilities.Gregory Wheeler & Fabio G. Cozman - 2021 - Synthese 199 (1-2):3761-3782.
    The purpose of this paper is to show that if one adopts conditional probabilities as the primitive concept of probability, one must deal with the fact that even in very ordinary circumstances at least some probability values may be imprecise, and that some probability questions may fail to have numerically precise answers.
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  • Probability Without Tears.Julia Staffel - forthcoming - Teaching Philosophy.
    This paper is about teaching probability to students of philosophy who don’t aim to do primarily formal work in their research. These students are unlikely to seek out classes about probability or formal epistemology for various reasons, for example because they don’t realize that this knowledge would be useful for them or because they are intimidated by the material. However, most areas of philosophy now contain debates that incorporate probability, and basic knowledge of it is essential even for philosophers whose (...)
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  • Computational modeling in philosophy: introduction to a topical collection.Simon Scheller, Christoph Merdes & Stephan Hartmann - 2022 - Synthese 200 (2):1-10.
    Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we (...)
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  • Scientific Self-Correction: The Bayesian Way.Felipe Romero & Jan Sprenger - 2020 - Synthese (Suppl 23):1-21.
    The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...)
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  • What is epistemically wrong with research affected by sponsorship bias? The evidential account.Alexander Reutlinger - 2020 - European Journal for Philosophy of Science 10 (2):1-26.
    Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...)
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  • A New Proposal How to Handle Counterexamples to Markov Causation À la Cartwright, Or: Fixing the Chemical Factory.Nina Retzlaff & Alexander Gebharter - 2020 - Synthese 197 (4):1467-1486.
    Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that kicks in once (...)
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  • A Battle in the Statistics Wars: A Simulation-Based Comparison of Bayesian, Frequentist and Williamsonian Methodologies.Mantas Radzvilas, William Peden & Francesco De Pretis - 2021 - Synthese 199 (5-6):13689-13748.
    The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...)
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  • Schema-Centred Unity and Process-Centred Pluralism of the Predictive Mind.Nina Poth - 2022 - Minds and Machines 32 (3):433-459.
    Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical coding’ into a (...)
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  • The Selective Confirmation Answer to the Paradox of the Ravens.William Peden - 2019 - International Studies in the Philosophy of Science 32 (3-4):177-193.
    Philosophers such as Goodman, Scheffler and Glymour aim to answer the Paradox of the Ravens by distinguishing between confirmation simpliciter and selective confirmation. In the latter concept, the evidence both supports a hypothesis and undermines one of its "rivals". In this article, I argue that while selective confirmation does seem to be an important scientific notion, no attempt to formalise it thus far has managed to solve the Paradox of the Ravens.
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  • Bayesian Epistemology.Jürgen Landes - 2022 - Kriterion – Journal of Philosophy 36 (1):1-7.
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  • Non-Classical Probabilities for Decision Making in Situations of Uncertainty.Dominik Klein, Ondrej Majer & Soroush Rafiee Rad - 2020 - Roczniki Filozoficzne 68 (4):315-343.
    Analyzing situations where information is partial, incomplete or contradictory has created a demand for quantitative belief measures that are weaker than classic probability theory. In this paper, we compare two frameworks that have been proposed for this task, Dempster-Shafer theory and non-standard probability theory based on Belnap-Dunn logic. We show the two frameworks to assume orthogonal perspectives on informational shortcomings, but also provide a partial correspondence result. Lastly, we also compare various dynamical rules of the two frameworks, all seen as (...)
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  • The Rationality of Science and the Inevitability of Defining Prior Beliefs in Empirical Research.Ulrich Dettweiler - 2019 - Frontiers in Psychology 10.
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  • Don't Trust Fodor's Guide in Monte Carlo: Learning Concepts by Hypothesis Testing Without Circularity.Michael Deigan - forthcoming - Mind and Language.
    Fodor argued that learning a concept by hypothesis testing would involve an impossible circularity. I show that Fodor's argument implicitly relies on the assumption that actually φ-ing entails an ability to φ. But this assumption is false in cases of φ-ing by luck, and just such luck is involved in testing hypotheses with the kinds of generative random sampling methods that many cognitive scientists take our minds to use. Concepts thus can be learned by hypothesis testing without circularity, and it (...)
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  • Abductive Reasoning in Cognitive Neuroscience: Weak and Strong Reverse Inference.Fabrizio Calzavarini & Gustavo Cevolani - 2022 - Synthese 200 (2):1-26.
    Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of cognitive processes from patterns of brain activation. While widely employed in experimental studies, it is now viewed with increasing scepticism within the neuroscience community. One problem with reverse inference is that it is logically invalid, being an instance of abduction in Peirce’s sense. In this paper, we offer the first systematic analysis of reverse inference as a form of abductive reasoning and highlight (...)
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  • In Defence of an Inferential Account of Extrapolation.Tudor M. Baetu - 2021 - International Studies in the Philosophy of Science 34 (2):81-100.
    According to the hypothesis-generator account, valid extrapolations from a source to a target system are circular, since they rely on knowledge of relevant similarities and differences that can onl...
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  • Why Subjectivism?Chloé de Canson - manuscript
    In response to two trenchant objections, radical subjective Bayesianism has been widely rejected. In this paper, I seek, if not to rehabilitate subjectivism, at least to show its critic what is attractive about the position. I argue that what is at stake in the subjectivism/anti-subjectivism debate is not, as is commonly thought, which norms of rationality are true, but rather, the conception of rationality that we adopt: there is an alternative approach to the widespread telic approach to rationality, which I (...)
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  • How to Assess the Epistemic Wrongness of Sponsorship Bias? The Case of Manufactured Certainty.Jon Leefmann - 2021 - Frontiers In 6 (Article 599909):1-13.
    Although the impact of so-called “sponsorship bias” has been the subject of increased attention in the philosophy of science, what exactly constitutes its epistemic wrongness is still debated. In this paper, I will argue that neither evidential accounts nor social–epistemological accounts can fully account for the epistemic wrongness of sponsorship bias, but there are good reasons to prefer social–epistemological to evidential accounts. I will defend this claim by examining how both accounts deal with a paradigm case from medical epistemology, recently (...)
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  • Bayesian Epistemology.William Talbott - 2006 - Stanford Encyclopedia of Philosophy.
    ‘Bayesian epistemology’ became an epistemological movement in the 20th century, though its two main features can be traced back to the eponymous Reverend Thomas Bayes (c. 1701-61). Those two features are: (1) the introduction of a formal apparatus for inductive logic; (2) the introduction of a pragmatic self-defeat test (as illustrated by Dutch Book Arguments) for epistemic rationality as a way of extending the justification of the laws of deductive logic to include a justification for the laws of inductive logic. (...)
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  • Scientific Method.Brian Hepburn & Hanne Andersen - 2015 - Stanford Encyclopedia of Philosophy.
    1. Overview and organizing themes 2. Historical Review: Aristotle to Mill 3. Logic of method and critical responses 3.1 Logical constructionism and Operationalism 3.2. H-D as a logic of confirmation 3.3. Popper and falsificationism 3.4 Meta-methodology and the end of method 4. Statistical methods for hypothesis testing 5. Method in Practice 5.1 Creative and exploratory practices 5.2 Computer methods and the ‘third way’ of doing science 6. Discourse on scientific method 6.1 “The scientific method” in science education and as seen (...)
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  • Rethinking the Acceptability and Probability of Indicative Conditionals.Michał Sikorski - forthcoming - In Stefan Kaufmann, Over David & Ghanshyam Sharma (eds.), Conditionals: Logic, linguistics, and psychology. Palgrave.
    The chapter is devoted to the probability and acceptability of indicative conditionals. Focusing on three influential theses, the Equation, Adams’ thesis, and the qualitative version of Adams’ thesis, Sikorski argues that none of them is well supported by the available empirical evidence. In the most controversial case of the Equation, the results of many studies which support it are, at least to some degree, undermined by some recent experimental findings. Sikorski discusses the Ramsey Test, and Lewis’s triviality proof, with special (...)
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  • Conditionals, Causal Claims and Objectivity.Michał Sikorski - 2020 - Dissertation, Università di Torino
    In my thesis, I develop two distinct themes. The first part of my thesis is devoted to indicative conditionals and approaching them from an empirically informed perspective. In the second part, I am developing classical topics of philosophy of science, specifically, scientific objectivity and the role of values in science, in connection to recent methodological developments, revolving around the Replication Crisis.
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  • Bayes Nets and Rationality.Stephan Hartmann - forthcoming - In The Handbook of Rationality. Boston, Massachusetts, USA:
    Bayes nets are a powerful tool for researchers in statistics and artificial intelligence. This chapter demonstrates that they are also of much use for philosophers and psychologists interested in (Bayesian) rationality. To do so, we outline the general methodology of Bayes nets modeling in rationality research and illustrate it with several examples from the philosophy and psychology of reasoning and argumentation. Along the way, we discuss the normative foundations of Bayes nets modeling and address some of the methodological problems it (...)
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  • Revisiting the Two Predominant Statistical Problems: The Stopping-Rule Problem and the Catch-All Hypothesis Problem.Yusaku Ohkubo - 2021 - Annals of the Japan Association for Philosophy of Science 30:23-41.
    The history of statistics is filled with many controversies, in which the prime focus has been the difference in the “interpretation of probability” between Fre- quentist and Bayesian theories. Many philosophical arguments have been elabo- rated to examine the problems of both theories based on this dichotomized view of statistics, including the well-known stopping-rule problem and the catch-all hy- pothesis problem. However, there are also several “hybrid” approaches in theory, practice, and philosophical analysis. This poses many fundamental questions. This paper (...)
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  • Meta-Empirical Support for Eliminative Reasoning.C. D. McCoy - 2021 - Studies in History and Philosophy of Science Part A 90:15-29.
    Eliminative reasoning is a method that has been employed in many significant episodes in the history of science. It has also been advocated by some philosophers as an important means for justifying well-established scientific theories. Arguments for how eliminative reasoning is able to do so, however, have generally relied on a too narrow conception of evidence, and have therefore tended to lapse into merely heuristic or pragmatic justifications for their conclusions. This paper shows how a broader conception of evidence not (...)
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  • Assessing Scientific Theories: The Bayesian Approach.Stephan Hartmann & Radin Dardashti - 2019 - In Radin Dardashti, Richard Dawid & Karim Thebault (eds.), Epistemology of Fundamental Physics: Why Trust a Theory? Cambridge, Vereinigtes Königreich: pp. 67–83.
    Scientific theories are used for a variety of purposes. For example, physical theories such as classical mechanics and electrodynamics have important applications in engineering and technology, and we trust that this results in useful machines, stable bridges, and the like. Similarly, theories such as quantum mechanics and relativity theory have many applications as well. Beyond that, these theories provide us with an understanding of the world and address fundamental questions about space, time, and matter. Here we trust that the answers (...)
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  • A New Approach to Testimonial Conditionals.Stephan Hartmann & Ulrike Hahn - 2020 - In CogSci 2020 Proceedings. Toronto, Ontario, Kanada: pp. 981–986.
    Conditionals pervade every aspect of our thinking, from the mundane and everyday such as ‘if you eat too much cheese, you will have nightmares’ to the most fundamental concerns as in ‘if global warming isn’t halted, sea levels will rise dramatically’. Many decades of research have focussed on the semantics of conditionals and how people reason from conditionals in everyday life. Here it has been rather overlooked how we come to such conditionals in the first place. In many cases, they (...)
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