Endogenous Barriers to Learning

TL;DR

This paper introduces a model of endogenous learning barriers using stochastic choice dynamics, identifying stability limits in various game settings and analyzing how strategy restrictions influence these barriers.

Contribution

It develops a novel concept of endogenous barriers to learning based on stability of logit-response dynamics, applied across multiple game types and auction formats.

Findings

Higher accuracy can destabilize learning dynamics.

Strategy restrictions can reduce or increase learning barriers.

The concept of limit quantal response equilibrium is introduced.

Abstract

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 logit-response 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.