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. ∗Eyster: Department of Economics, London School of Economics, Houghton Street, London WC2A 2AE United Kingdom (email: [email protected]); Galeotti: Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3S, United Kingdom (email: [email protected]); Kartik: Department of Economics, Columbia University, 420 W. 118th Street, New York, NY 10027, USA (email: [email protected]); Rabin: Department of Economics, University of California, Berkeley, 549 Evans Hall #3880, Berkeley, CA 94720-3880 USA, (email: [email protected]). We thank Wouter Dessein for his discussion at the 2012 Utah Winter Business Economics Conference, and audiences at the LSE, University of Essex, and University of Vienna for their helpful comments. Andrea Galeotti is grateful to the European Research Council for support through ERC-starting grant (award no. 283454) and to The Leverhulme Trust for support through the Philip Leverhulme Prize.