An Improved Argument for Superconditionalization.

Standard arguments for Bayesian conditionalizing rely on assumptions that many epistemologists have criticized as being too strong: (i) that conditionalizers must be logically infallible, which rules out the possibility of rational logical learning, and (ii) that what is learned with certainty must...

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Publicado en:Erkenntnis Vol. 89; no. 8; pp. 3247 - 3274
Autores principales: Staffel, Julia, De Bona, Glauber
Formato: Artículo
Publicado: Springer Nature Dec2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: An Improved Argument for Superconditionalization.
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          Staffel, Julia
          De Bona, Glauber
        affil:
          https://ror.org/02ttsq026 Department of Philosophy, University of Colorado at Boulder, Hellems 169 UCB 232, 80309-0232, Boulder, CO, USA
          https://ror.org/036rp1748 Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto, tv 3, 158 - Butantã, 05508-010, São Paulo, SP, Brazil
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        Certainty
        Argument
        Possibility
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          Certainty
          Argument
          Possibility
      ab: Standard arguments for Bayesian conditionalizing rely on assumptions that many epistemologists have criticized as being too strong: (i) that conditionalizers must be logically infallible, which rules out the possibility of rational logical learning, and (ii) that what is learned with certainty must be true (factivity). In this paper, we give a new factivity-free argument for the superconditionalization norm in a personal possibility framework that allows agents to learn empirical and logical falsehoods. We then discuss how the resulting framework should be interpreted. Does it still model norms of rationality, or something else, or nothing useful at all? We discuss five ways of interpreting our results, three that embrace them and two that reject them. We find one of each kind wanting, and leave readers to choose among the remaining three.
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