  How can heuristic strategies emerge from smaller building blocks? We propose
Approximate Bayesian Computation as a computational solution to this problem.
As a first proof of concept, we demonstrate how a heuristic decision strategy
such as Take The Best (TTB) can be learned from smaller, probabilistically
updated building blocks. Based on a self-reinforcing sampling scheme, different
building blocks are combined and, over time, tree-like non-compensatory
heuristics emerge. This new algorithm, coined Approximately Bayesian Computed
Take The Best (ABC-TTB), is able to recover a data set that was generated by
TTB, leads to sensible inferences about cue importance and cue directions, can
outperform traditional TTB, and allows to trade-off performance and
computational effort explicitly.
