Hello, I am a graduate student in the Department of Economics at the University of Arizona. I am primarily interested in mechanism design, but have also worked in other areas of both theoretical and applied economics. I am on the job market during the 2018-2019 academic year.
Below: projects. Click on the title for a current draft.
This paper develops a data-driven approach to multidimensional screening. The principal observes a population of decision makers each choose from a finite number of exogenously-specified sets of allocations, and her beliefs about the agent's preferences are informed by this data. In my model, there are a multiplicity of preference distributions that are consistent with the principal's observations. Rather than assign privilege to any one distribution, she evaluates mechanisms by computing their worst-case payoff against the set of distributions that are compatible with the choice data.
I show that there are circumstances in which the principal can do better than using a mechanism that recreates one of the choice environments in her data set, even when she knows nothing about the agent's preferences beyond what's implied by the data. More broadly, I allow for arbitrary domains of preferences, and identify conditions under which update mechanisms that use only allocations that are vertically differentiated from the allocations in the data are optimal.
This paper studies the role of uninformative signals in aligning an agent's unobserved risk-taking behavior with a principal's preferences. In a departure from the existing literature, the principal in our model does not know the agent's risk preferences: instead, she is at least slightly uncertain. I characterize the set of risk-aligned contracts under which the agent chooses risks as if his goal were to maximize the principal's payoff. All risk-aligned contracts condition transfers to the agent on exogenous factors. I exhibit a general contracting environment in which these contracts are worst-case optimal.
This paper studies a general moral hazard problem in which the principal is uncertain about the agent's risk preferences and his production technology. In addition to choosing how much effort to exert, the agent might also choose from a variety of safe and risky actions. The principal seeks a contract that performs well regardless of the agent's preferences and technology.
This is a demanding criterion: fully-contingent contracts do not guarantee the principal a payoff that is larger than her payoff if the agent shirks, even if effort is costless for the agent. Conversely, contracts with transfers that do not vary when output is very small protect the principal from severe risk-aversion, and contracts with transfers that do not vary when output is very large protect the principal from severe risk-seeking. Thus, I identify virtues of these partially-contingent contracts, which are widely used in practice.
I study a contracting environment in which there are repeated interactions between a time-inconsistent 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 more accurately forecast his future behavior by inspecting his own choice history. 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. I conclude that, in many circumstances, learning does little to protect the agent or to promote efficiency. Furthermore, if the agent’s beliefs initially reflect some degree of pessimism, his ability to learn can actually diminish his long-run average payoff. I show that while competition between principals 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.
Valuing Science Policy: Dynamic Decision-Making With Generalized Bayesian Learning [with Ivan Rudik and Derek Lemoine]
Draft available upon request.