Heuristics, hacks, and habits: Boundedly optimal approaches to learning, 
reasoning and decision making

Dasgupta, I; Schulz, E; Hamrick, JB; Tenenbaum, JB

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Heuristics, hacks, and habits: Boundedly optimal approaches to learning, reasoning and decision making

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https://cognitivesciencesociety.org/wp-content/uploads/2019/07/cogsci19_proceedings-8July2019-compressed.pdf
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Dasgupta, I., Schulz, E., Hamrick, J., & Tenenbaum, J. (2019). Heuristics, hacks, and habits: Boundedly optimal approaches to learning, reasoning and decision making. In A. Goel, C. Seifert, & C. Freksa (Eds.), 41st Annual Meeting of the Cognitive Science Society (CogSci 2019): Creativity + Cognition + Computation (pp. 72-73). Red Hook, NY, USA: Curran.

Cite as: https://hdl.handle.net/21.11116/0000-0005-D623-2

Abstract

Humans regularly perform tasks that require combining infor-mation across several sources of information to learn, reason,and make decisions. Bayesian models provide a computa-tional framework, and a normative account, for how humanscarry out these tasks. However, exact inference is intractablein most real-world situations, and extensive empirical workshows that human behavior often deviates significantly fromthe Bayesian optimum. A promising possibility is that peopleinstead approximate rational solutions using bounded avail-able resources. In this workshop, we bring together lead-ing researchers from cognitive science, neuroscience and ma-chine learning to build a better understanding of boundedlyoptimality in how humans learn, reason and make decisions.