Proceedings of the Annual Meeting of the Cognitive Science Society

How can heuristic strategies emerge from smaller build-ing blocks? We propose Approximate Bayesian Com-putation (ABC) as a computational solution to thisproblem. As a first proof of concept, we demonstratehow a heuristic decision strategy such as Take The Best(TTB) can be learned from smaller, probabilisticallyupdated building blocks. Based on a self-reinforcingsampling scheme, different building blocks are com-bined and, over time, tree-like non-compensatory heuris-tics emerge. This new algorithm, coined ApproximatelyBayesian Computed Take The Best (ABC-TTB), is ableto recover data that was generated by TTB, leads tosensible inferences about cue importance and cue direc-tions, can outperform traditional TTB, and allows totrade-off performance and computational effort explic-itly.