| 要約: | © 2021 Elsevier Ltd Across many domains of decision making, people seem both rational and irrational. We review recent work that aims to reconcile these apparently contradictory views by modeling human decisions as optimal under a set of cognitive resource constraints. This ⬜resource-rational⬢ analysis connects psychology and neuroscience to ideas from engineering, economics, and machine learning. Here, we focus on an information-theoretic formalization of cognitive resources, highlighting its implications for understanding three important and widespread phenomena: reference dependence, stochastic choice, and perseveration. While these phenomena have traditionally been viewed as irrational biases or errors, we suggest that they may arise from a rational solution to the problem of resource-limited decision making.
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