I am a graduate student in the Department of Economics at the Eller College of Management at the University of Arizona. I am interested in microeconomic theory, and in particular contract theory and mechanism design.
I can be reached via e-mail at firstname.lastname@example.org. Below, I provide a description of my current projects.
Robust Incentives for Diversely Risk-Averse Agents
I study a general moral hazard problem in which the agent's risk preferences are unknown to the principal. In particular, the principal knows only that the agent is weakly risk-averse and that he strictly prefers more money to less. The agent chooses not only the degree of effort to exert, but also potentially from several safe or risky alternative projects for each level of effort. Because these alternative projects are undesirable from the principal's perspective, she seeks a mechanism that is robust to risk-taking by the agent.
This is a difficult environment to contract in: in particular, no fully-contingent contract provides the principal with a positive expected payoff guarantee, and an analogous result obtains when the principal screens the agent with a menu of contracts. This result holds even if the agent is known by the principal to suffer no disutility from effort whatsoever. However, if the principal instead offers the agent a single binary-transfer contract, then she does not provide the agent with incentives to produce either very low or very high levels of output. Because of this feature, there exists a binary contract that provides the principal with a positive payoff guarantee whenever the agent exhibits bounded disutility from effort. Moreover, these contracts are the mechanisms that are maximally robust to risk-taking by the agent, in the sense that they provide incentives for the agent to take undesirable risks in the smallest class of environments. As a corollary, we exhibit environments in which the maxmin optimal mechanisms are binary contracts.
Contracting with Optimism and Introspection
ABSTRACT: We study a repeated contracting relationship between an agent who does not completely understand his own future behavior and a better-informed principal. Although the agent's initial beliefs are incorrect, he learns to better forecast his future behavior by reflecting on his previous choices. However, the principal is able to manipulate the evolution of the agent's beliefs by selectively pooling agent types, and this mechanic is the emphasis of the paper. In particular, the principal's problem implies two dynamic considerations. First, because the agent may initially hold some pessimistic beliefs, the principal is sometimes able to promote optimism in the agent, which yields information rents in future periods. Second, because the principal is subject to trade-offs between information rent and efficiency surplus extraction at the stage level, it is in general profitable for the principal to delay full exploitation of his informational advantage.
We conclude that, in many circumstances, learning does little to protect the agent or to promote the efficiency of contracts in the case of monopoly. Furthermore, if the agent's beliefs initially reflect some degree of pessimism, his ability to learn can ultimately exacerbate the welfare consequences of his mispredictions; i.e., the agent's ability to learn can sometimes be harmful to the agent. I show that while Bertrand competition between principals competing for the agent's business protects the agent with a favorable up-front transfer, the critical inefficiencies demonstrated in the monopoly case still apply, with particularly inefficient contracts offered in early periods. Finally, I conclude with an analysis of restrictions to the allowable contract space that improve social welfare and facilitate learning by the agent. More broadly, we argue that some of the qualitative insights from the behavioral contract theory literature with naive or optimistic agents still applies when the agent is able to learn.
Calibrating Informational Dynamics: Learning About the Sensitivity of Climate to Emissions, with Ivan Rudik and Derek Lemoine.