Hello, I'm an assistant professor of economics at Georgia Tech. I work primarily on prior-free approaches to contract theory and mechanism design, but also maintain interests in other areas of microeconomics.
You can reach me via e-mail at firstname.lastname@example.org. Below: projects.
This paper studies a general moral hazard problem with heterogeneous agent beliefs. The agent is risk-neutral, enjoys limited liability, and has a one-dimensional production technology. If the principal is also risk neutral, then contracts that efficiently implement uniform effort are linear. More broadly, if the principal is not risk neutral or if she is willing to accept heterogeneous effort, then efficient contracts are piecewise-linear bonus contracts with payments that vary linearly when output is sufficiently large. These efficiency results lead immediately to the optimality of the corresponding contracts in a variety of environments in which e.g. the agent is uncertain about the relationship between effort and output, or the principal is uncertain about the agent's beliefs. Because our general results do not rely on any particular specification of the principal's preferences, our optimal contracts retain a simple linear structure even in the presence of e.g. non-linear income taxes.
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.
This paper studies a moral hazard problem in which the principal is uncertain about the agent's risk preferences and his production technology. We show that fully-contingent contracts do not perform well in this environment, even if effort is costless for the agent. Conversely, contracts with transfers that do not vary when output is low protect the principal from severe risk-aversion, and contracts with transfers that do not vary when output is high protect the principal from severe risk seeking. In special cases of our model, binary contracts are optimal.
This paper studies the role of stochastic contracts 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 are stochastic. I exhibit a general contracting environment in which these contracts are worst-case optimal.
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 leave him worse off in the long run. 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.