Congested Observational Learning
- Congested Observational Learning
- Eyster, Eric
- Persistent URL:
- Department of Economics Discussion Papers
- Part Number:
- Department of Economics, Columbia University
- Publisher Location:
- New York
- 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 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 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 also 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.
- Item views
text | xml
- Suggested Citation:
- Eric Eyster, Andrea Galeotti, Navin Kartik, Matthew Rabin, 2012, Congested Observational Learning, Columbia University Academic Commons, https://doi.org/10.7916/D85T3TRS.