Simple trees in complex forests: Growing Take The Best by approximate Bayesian computation


How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation (ABC) 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 data 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.

In Papafragou, A., Grodner, D., Mirman, D., & Trueswell, J.C. (Eds.) Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2531–2536). Austin, TX: Cognitive Science Society.