Conditional learning through causal models.
Conditional learning, where agents learn a conditional sentence 'If A, then B,' is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, whi...
| Publicado en: | Synthese Vol. 199; no. 1/2; pp. 2415 - 2438 |
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| Formato: | Artículo |
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Springer Nature
Dec2021
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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=153650880&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 153650880 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2021 vid: 199 iid: 1/2 pid: 237 pub: Springer Nature artinfo: ui: 153650880 10.1007/s11229-020-02891-x ppf: 2415 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 376KB tig: atl: Conditional learning through causal models. aug: au: Vandenburgh, Jonathan affil: Northwestern University, 1880 Campus Dr., 60208, Evanston, IL, USA su: Causal models Conditional probability sug: subj: Causal models Conditional probability keyword: Bayesian Learning Causal Models Conditionals ab: Conditional learning, where agents learn a conditional sentence 'If A, then B,' is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model of conditional learning. This model extends traditional Bayesian learning by incorporating causal models into agents' epistemic states. On this theory, conditional learning proceeds in two steps. First, an agent learns a new causal model with the appropriate relationship between the antecedent and the consequent. Then, the agent narrows down the set of possible worlds to include only those which make the conditional proposition true. This model of learning can incorporate both standard cases of Bayesian learning and the non-uniform learning required to learn conditional information. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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