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Type: Conference paper
Title: Confirmation bias is rational when hypotheses are sparse
Author: Perfors, A.
Navarro, D.
Citation: Proceedings of the 31st Annual Conference of the Cognitive Science Society (COGSCI 2009): pp.2741-2746
Publisher: Cognitive Science Society
Publisher Place: Netherlands
Issue Date: 2009
ISBN: 9780976831853
Conference Name: Annual Conference of the Cognitive Science Society (31st : 2009 : Amsterdam, The Netherlands)
Statement of
Amy Perfors and Daniel Navarro
Abstract: We consider the common situation in which a reasoner must induce the rule that explains an observed sequence of data, but the hypothesis space of possible rules is not explicitly enumerated or identified; an example of this situation is the number game (Wason, 1960), or "twenty questions." We present mathematical optimality results showing that as long as hypotheses are sparse -- that is, as long as rules, on average, tend to be true only for a small proportion of entities in the world -- then confirmation bias is a near-optimal strategy. Experimental evidence suggests that at least in the domain of numbers, the sparsity assumption is reasonable.
Keywords: rational analysis; decision making; confirmation bias; information
Rights: © the authors
RMID: 0020097170
Appears in Collections:Psychology publications

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