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Models of Experimentation and Reputation
Friday, Jan. 7, 2022
10:00 AM - 12:00 PM (EST)
University of California-Los Angeles
Reputation Building under Observational Learning
A patient seller interacts with a sequence of myopic consumers. Each consumer decides whether to trust the seller after observing a bounded number of the seller's past actions and some or all previous consumers' choices. With positive probability, the seller is a type that commits to play his Stackelberg action. I show that consumers' ability to observe other consumers' choices can lead to reputation failures: There exist equilibria where every consumer imitates her predecessor with high probability and the patient seller receives his minmax payoff. Furthermore, the seller receives his minmax payoff in all equilibria where consumers do not trust him in the first period and do not trust him when the worst action profile occurred in the period before. In an extension where every consumer observes all previous consumers' choices and an unboundedly informative private signal about the seller's current-period action, the seller receives at least his Stackelberg payoff in all equilibria.
Early-Career Discrimination: Spiraling or Self-Correcting?
Do workers from social groups with comparable productivity distributions obtain comparable lifetime earnings? We study how a small amount of early-career discrimination propagates over time when workers’ productivity is revealed through employment. Breakdown learning environments that track on-the-job failures grant a disproportionately large advantage to marginally more favored groups, whereas breakthrough learning environments that track successes guarantee comparable earnings to groups of comparable productivity. This discrepancy persists with large labor markets, flexible wages, inconclusive signals, and misspecified employer beliefs. Allowing for investment in productivity exacerbates inequality between groups under breakdown learning.
Incentives and Performance With Optimal Money Management Contracts
I characterize the dynamics of incentives in an optimal contract with investment delegation, moral hazard, and uncertainty about the agent's productivity. The principal increases the agent's incentives after good performance in order to delegate more capital to an agent with higher perceived productivity, thus implementing a convex pay-for-performance scheme. Moreover, the principal commits to reduce the agent's future incentives in order to mitigate ex-ante investment distortions. Methodologically, I provide a duality-based strategy to overcome technical challenges common to continuous-time contracting models with state variables. I also derive a sufficient condition to verify the validity of the first-order approach.
Searching for "Arms": Experimentation with Endogenous Consideration Sets
We study the problem of a decision-maker sequentially choosing between exploring existing alternatives in her consideration set and searching for new ones. We characterize the optimal policy and identify implications for search and exploration dynamics. When the search technology is stationary or improves over time, search is equivalent to replacement. With deteriorating technologies, alternatives are revisited after search is launched and each expansion is treated as if it were the last one. We apply the model to the administration of medical treatments, clinical trials toward regulatory approval, Weitzman (1979)’s Pandora’s boxes problem, and online consumer search.
D8 - Information, Knowledge, and Uncertainty
L1 - Market Structure, Firm Strategy, and Market Performance