Bounded Rationality
Overview
Bounded rationality asks how decision-makers choose when they cannot consider every option or compute an ideal answer. Approaches include satisficing and attention-based heuristics, limited-depth simulation and Bayesian learning, level-k models of strategic behavior, and methods that allocate computation according to its expected value. Researchers test these models in settings such as unfamiliar games, consumer choice, information navigation, network coordination, human-robot control, and shared-budget reasoning tasks. A central challenge is capturing realistic limits without losing predictive accuracy, while determining when limited resources should be spent on better reasoning, more information, or simpler decisions.
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