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. If congestion on either action can get so large that an agent would prefer to take the other action no matter his beliefs about the state, then herds cannot occur. To the extent that "switching" away from the more popular action also reveals some private information, social learning is improved. The absence of herding is not enough to guarantee complete learning, however, as information cascades can occur through perpetual but uninformative switching between actions. For bounded private beliefs, we provide conditions that guarantee complete learning and conditions that guarantee bounded learning. Congestion costs have ambiguous effects on welfare as measured by 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