# Confirmation bias is rational when hypotheses are sparse

- Amy Perfors,
*University of Adelaide*
- Daniel Navarro,
*University of Adelaide*

## 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.

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