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Evidence and Inductive Inference

In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. Routledge (2021)

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  1. Everything is Self-Evident.Steven Diggin - forthcoming - Logos and Episteme: An International Journal of Epistemology.
    Plausible probabilistic accounts of evidential support entail that every true proposition is evidence for itself. This paper defends this surprising principle against a series of recent objections from Jessica Brown. Specifically, the paper argues that: (i) explanationist accounts of evidential support convergently entail that every true proposition is self-evident, and (ii) it is often felicitous to cite a true proposition as evidence for itself, just not under that description. The paper also develops an objection involving the apparent impossibility of believing (...)
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  • Molinism: Explaining Our Freedom Away.Nevin Climenhaga & Daniel Rubio - forthcoming - Mind:fzab042.
    Molinists hold that there are contingently true counterfactuals about what agents would do if put in specific circumstances, that God knows these prior to creation, and that God uses this knowledge in choosing how to create. In this essay we critique Molinism, arguing that if these theses were true, agents would not be free. Consider Eve’s sinning upon being tempted by a serpent. We argue that if Molinism is true, then there is some set of facts that fully explains both (...)
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  • Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity.Patrick Grim, Frank Seidl, Calum McNamara, Hinton Rago, Isabell Astor, Caroline Diaso & Peter Ryner - forthcoming - Philosophy of Science.
    We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the (...)
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