A Computational Theory of Subjective Probability [Featuring a Proof that the Conjunction Effect is not a Fallacy]
|Title:||A Computational Theory of Subjective Probability [Featuring a Proof that the Conjunction Effect is not a Fallacy]||Authors:||Maguire, Phil
Keane, Mark T.
|Permanent link:||http://hdl.handle.net/10197/6791||Date:||3-Aug-2013||Online since:||2015-08-13T09:25:47Z||Abstract:||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.||Type of material:||Conference Publication||Publisher:||Cognitive Science Society||Copyright (published version):||2013 the Authors||Keywords:||Media analytics; Conjunction fallacy; Algorithmic statistics; Likelihood judgments; Surprise; Subjective probability||Other versions:||http://cognitivesciencesociety.org/conference2013/index.html||Language:||en||Status of Item:||Peer reviewed||Conference Details:||35th Annual Conference of the Cognitive Science Society, Berlin, Germany, 31 July - 3 August 2013|
|Appears in Collections:||Insight Research Collection|
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