Learning from Conditionals.

In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayes...

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Publicado en:Mind (0026-4423) Vol. 129; no. 514; pp. 461 - 509
Autores principales: Eva, Benjamin, Hartmann, Stephan, Rad, Soroush Rafiee
Formato: Artículo
Publicado: Oxford University Press / USA Apr2020
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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          Eva, Benjamin
          Hartmann, Stephan
          Rad, Soroush Rafiee
        affil:
          University of Konstanz
          Munich Centre for Mathematical Philosophy, LMU Munich
          University of Bayreuth
      su:
        Conditionals (Logic)
        Theory of knowledge
        Bayesian analysis
        Formalization (Linguistics)
        Comparative grammar
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          Conditionals (Logic)
          Theory of knowledge
          Bayesian analysis
          Formalization (Linguistics)
          Comparative grammar
      ab: In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian norms is sufficient to identify a normatively privileged updating procedure for this kind of learning. Along the way, we justify a privileged formalization of the notion of 'epistemic conservativity', offer a new analysis of the Judy Benjamin problem, and emphasize the distinction between interpreting the content of new evidence and updating one's beliefs on the basis of that content.
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    language: English
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