Bayesian Convergence for Computably Bounded Agents.
In this article, we pursue two goals. First, we argue that computable probability theory offers a fitting framework for modeling the credences of computably bounded—and, thus, more realistic—Bayesian reasoners. Second, we develop a Bayesian perspective on algorithmic randomness: a branch of computab...
| Publicado en: | Philosophy & Phenomenological Research Vol. 112; no. 1; pp. 307 - 325 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=190936535&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 190936535 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318205 PPO jtl: Philosophy & Phenomenological Research issn: 00318205 maglogo: Y pubinfo: dt: Jan2026 vid: 112 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 190936535 10.1111/phpr.70079 ppf: 307 ppct: 18 formats: tig: atl: Bayesian Convergence for Computably Bounded Agents. aug: au: Huttegger, Simon M. Walsh, Sean Zaffora Blando, Francesca affil: Department of Logic and Philosophy of Science, University of California, Irvine California,, USA Department of Philosophy, University of California, Los Angeles California,, USA Department of Philosophy, Carnegie Mellon University, Pittsburgh Pennsylvania,, USA su: Bayesian analysis Probability theory Algorithms Theory of knowledge Truth sug: subj: Bayesian analysis Probability theory Algorithms Theory of knowledge Truth ab: In this article, we pursue two goals. First, we argue that computable probability theory offers a fitting framework for modeling the credences of computably bounded—and, thus, more realistic—Bayesian reasoners. Second, we develop a Bayesian perspective on algorithmic randomness: a branch of computability theory that provides a formal account of what it takes for a sequence of observations (a data stream) to be probabilistically typical in an algorithmically specifiable way. In particular, we argue that adopting such a perspective leads to novel insights for one of the pillars of Bayesian epistemology: Bayesian convergence to the truth. In a companion article, we showed that, for Bayesian agents whose credences are given by computable probability measures, the data streams that guarantee convergence to the truth coincide with the algorithmically random ones. Here, we put these results to use to counter various skeptical arguments which target the philosophical significance of Bayesian convergence‐to‐the‐truth theorems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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