Congested Observational Learning
We study observational learning in environments with congestion costs: as more of one's predecessors choose an action, the payoff from choosing that action decreases. Herds cannot occur if congestion on an action can get so large that an agent would prefer to take a different action no matter what his beliefs about the state. To the extent that "switching" away from the more popular action also reveals some private information, social learning is improved. The absence of herding does not guarantee complete asymptotic learning, however, as information cascades can occur through perpetual but uninformative switching between actions. Our main contribution is to provide conditions on the nature of congestion costs that guarantee complete learning and conditions that guarantee bounded learning. We find that asymptotic learning can be virtually complete even if each agent has only an infinitesimal effect on congestion costs. We further show that congestion costs have ambiguous effects on the proportion of agents who choose the superior action. We apply our results to markets where congestion costs arise through responsive pricing and to queuing problems where agents dislike waiting for service.
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