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How can heuristic strategies emerge from smaller building blocks? We proposeApproximate Bayesian Computation as a computational solution to this problem.As a first proof of concept, we demonstrate how a heuristic decision strategysuch as Take The Best (TTB) can be learned from smaller, probabilisticallyupdated building blocks. Based on a self-reinforcing sampling scheme, differentbuilding blocks are combined and, over time, tree-like non-compensatoryheuristics emerge. This new algorithm, coined Approximately Bayesian ComputedTake The Best (ABC-TTB), is able to recover a data set that was generated byTTB, leads to sensible inferences about cue importance and cue directions, canoutperform traditional TTB, and allows to trade-off performance andcomputational effort explicitly.

Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation