Year: 2009
Type: article
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.
Authors: Andrew Perfors, Daniel J. Navarro
Institution: The University of Adelaide
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2522669694