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    A Computational Theory of Subjective Probability

    Maguire, Phil and Moser, Philippe and Maguire, Rebecca and Keane, Mark (2014) A Computational Theory of Subjective Probability. Working Paper. arXiv.

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    In this article we demonstrate how algorithmic probability theory is applied to situations that involve uncertainty. When people are unsure of their model of reality, then the outcome they observe will cause them to update their beliefs. We argue that classical probability cannot be applied in such cases, and that subjective probability must instead be used. In Experiment 1 we show that, when judging the probability of lottery number sequences, people apply subjective rather than classical probability. In Experiment 2 we examine the conjunction fallacy and demonstrate that the materials used by Tverksy and Kahneman (1983) involve model uncertainty. We then provide a formal mathematical proof that, for every uncertain model, there exists a conjunction of outcomes which is more subjectively probable than either of its constituents in isolation.

    Item Type: Monograph (Working Paper)
    Additional Information: Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 960-965). Austin, TX: Cognitive Science Society
    Keywords: Conjunction fallacy; algorithmic statistics; likelihood judgments; surprise; subjective probability;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 6268
    Identification Number: arXiv:1405.6142
    Depositing User: Philippe Moser
    Date Deposited: 17 Jul 2015 14:01
    Publisher: arXiv
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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