Presented By: Department of Economics
Economic Theory: Reputation Building under Obervational Learning
Harry Pei, Northwestern
Abstract:
I study a social learning model where a sequence of myopic players observe their predecessors’ actions as well as some private signals, and then forecast the behavior of a strategic long-run player. A sequence of buyers interact with a patient seller, who is either a strategic type or a commitment type that plays the optimal commitment action in every period. When each buyer observes all previous buyers’ actions and a bounded subset of the seller’s past actions, there exist equilibria in which the patient seller receives his minmax payoff since the speed of learning goes to zero as the seller becomes patient. When each buyer observes all previous buyers’ actions and an unboundedly informative private signal about the seller’s current-period action, the speed of learning is bounded away from zero and a patient seller receives at least his optimal commitment payoff in all equilibria.
To join the seminar, please contact at econ.theory-requests@umich.edu
I study a social learning model where a sequence of myopic players observe their predecessors’ actions as well as some private signals, and then forecast the behavior of a strategic long-run player. A sequence of buyers interact with a patient seller, who is either a strategic type or a commitment type that plays the optimal commitment action in every period. When each buyer observes all previous buyers’ actions and a bounded subset of the seller’s past actions, there exist equilibria in which the patient seller receives his minmax payoff since the speed of learning goes to zero as the seller becomes patient. When each buyer observes all previous buyers’ actions and an unboundedly informative private signal about the seller’s current-period action, the speed of learning is bounded away from zero and a patient seller receives at least his optimal commitment payoff in all equilibria.
To join the seminar, please contact at econ.theory-requests@umich.edu