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Endogenous barriers to learning
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AI
DOI：10.1016/j.geb.2025.06.003.png)
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Abstract
En 中文
Building on the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model (logit quantal response), leaving endogenous the accuracy of their choices. In some games, higher accuracy leads to unstable logitresponse dynamics. Starting from the lowest possible accuracy, we define the barrier to learning as the maximum accuracy which keeps the logit-response dynamic stable (for all lower accuracies). This defines a limit quantal response equilibrium. We apply the concept to centipede, travelers' dilemma, and 11-20 money-request games and to first-price and all-pay auctions, and discuss the role of strategy restrictions in reducing or amplifying barriers to learning.
Keywords:
Learning
Bounded rationality
Stochastic choice
