{"pk":28658,"title":"Where Do Heuristics Come From?","subtitle":null,"abstract":"Human decision-making deviates from the optimal solution,i.e. the one maximizing cumulative rewards, in many sit-uations. Here we approach this discrepancy from the per-spective of computational rationality and our goal is to pro-vide justification for such seemingly sub-optimal strategies.More specifically we investigate the hypothesis, that humansdo not know optimal decision-making algorithms in advance,but instead employ a learned, resource-constrained approxima-tion. The idea is formalized through combining a recently pro-posed meta-learning model based on Recurrent Neural Net-works with a resource-rational objective. The resulting ap-proach is closely connected to variational inference and theMinimum Description Length principle. Empirical evidenceis obtained from a two-armed bandit task. Here we observepatterns in our family of models that resemble differences be-tween individual human participants.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Bounded rationality; computational rationality;variational inference; reinforcement learning; meta-learning;individual differences; multi-armed bandit"}],"section":"Papers with Poster Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/1gd685wb","frozenauthors":[{"first_name":"Marcel","middle_name":"","last_name":"Binz","name_suffix":"","institution":"Philipps-Universität Marburg","department":""},{"first_name":"Dominik","middle_name":"","last_name":"Endres","name_suffix":"","institution":"Philipps-Universität Marburg","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28658/galley/18529/download/"}]}