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

Additional Information: 
Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 960965). 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 
URI: 

Use Licence: 
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BYNCSA). Details of this licence are available
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