Imperfect Cognition in Strategic Choices -- Theory and Experiments

Recent advances in neuroscience and machine learning have revitalized interest in models of noisy cognition, which depart from the classical view that decisions result from stable preferences. Instead, these models attribute behavioral variability to systematic limitations in cognitive processing. This framework has gained particular traction in the study of decision-making under risk and uncertainty, offering explanations for well-documented behavioral regularities, such as risk aversion, loss aversion, and probability weighting. In this project, we aim to extend the cognitively grounded models to simple strategic interactions, including social dilemmas, coordination problems, and bargaining/trust games, and then to design and implement behavioral experiments to empirically test the predictive validity of these models against traditional utility-based models.