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...
| Publicado en: | Erkenntnis Vol. 89; no. 8; pp. 3247 - 3274 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Dec2024
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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=180628817&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180628817 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Dec2024 vid: 89 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 180628817 10.1007/s10670-023-00676-5 ppf: 3247 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: An Improved Argument for Superconditionalization. aug: au: 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 su: Certainty Argument Possibility sug: subj: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2024. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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