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Learning Conditional and Causal Information by Jeffrey Imaging on Stalnaker Conditionals

Organon F, 2017, vol. 24, No 4, pp. 456-486.
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We show that the learning of (uncertain) conditional and/or causal information may be modelled by (Jeffrey) imaging on Stalnaker conditionals. We adapt the method of learning uncertain conditional information proposed in Günther (2017) to a method of learning uncertain causal information. The idea behind the adaptation parallels Lewis (1973c)’s analysis of causal dependence. The combination of the methods provides a unified account of learning conditional and causal information that manages to clearly distinguish between conditional, causal and conjunctive information. Moreover, our framework seems to be the first general solution that generates the correct predictions for Douven (2012)’s benchmark examples and the Judy Benjamin Problem.

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Organon F - redakcia

Filozofický ústav SAV
Klemensova 19
813 64 Bratislava
Tel.:(+4212) 5292 1215
Fax: (+4212) 5292 1215
E-mail: organonf@gmail.com
Domovská stránka