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.

Notions of entropy and uncertainty are fundamental tomany domains, ranging from the philosophy of science tophysics. One important application is to quantify theexpected usefulness of possible experiments (or questions ortests). Many different entropy models could be used;different models do not in general lead to the sameconclusions about which tests (or experiments) are mostvaluable. It is often unclear whether this is due to differenttheoretical and practical goals or are merely due to historicalaccident. We introduce a unified two-parameter family ofentropy models that incorporates a great deal of entropies asspecial cases. This family of models offers insight intoheretofore perplexing psychological results, and generatespredictions for future research.