{"pk":26488,"title":"Simple Trees in Complex Forests: Growing Take The Best by Approximate Bayesian Computation","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Heuristics"},{"word":"Take The Best"},{"word":"Approximate Bayesian Computation"},{"word":"Reinforcement Learning"}],"section":"Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6bj9g2vc","frozenauthors":[{"first_name":"Eric","middle_name":"","last_name":"Schulz","name_suffix":"","institution":"University College London","department":""},{"first_name":"Maarten","middle_name":"","last_name":"Speekenbrink","name_suffix":"","institution":"University College London","department":""},{"first_name":"Bjorn","middle_name":"","last_name":"Meder","name_suffix":"","institution":"Max Planck Institute for Human Development","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2016-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/26488/galley/16124/download/"}]}