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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Detalles Bibliográficos
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
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Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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.