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- W1607867748 abstract "In this article we demonstrate how algorithmic probability theoryis applied to situations that involve uncertainty. When peopleare unsure of their model of reality, then the outcome theyobserve will cause them to update their beliefs. We argue thatclassical probability cannot be applied in such cases, and thatsubjective probability must instead be used. In Experiment 1we show that, when judging the probability of lottery numbersequences, people apply subjective rather than classical probability.In Experiment 2 we examine the conjunction fallacy anddemonstrate that the materials used by Tverksy and Kahneman(1983) involve model uncertainty. We then provide a formalmathematical proof that, for every uncertain model, thereexists a conjunction of outcomes which is more subjectivelyprobable than either of its constituents in isolation." @default.
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- W1607867748 date "2014-05-08" @default.
- W1607867748 modified "2023-09-27" @default.
- W1607867748 title "A Computational Theory of Subjective Probability." @default.
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