Yu Fu Wong
Assistant Professor of Economics, University of Pittsburgh
Assistant Professor of Economics, University of Pittsburgh
We introduce flexible endogenous monitoring into dynamic moral hazard. Specifically, we explore a model in which a principal commits to acquiring signals informative about an agent’s effort and conditioning future actions on these signals. In deriving the optimal incentive scheme, we identify two novel incentive mechanisms that differ qualitatively from those in exogenous monitoring models. We show that optimal monitoring generates inconclusive Poisson bad news that results in the agent’s immediate termination, and that the Poisson bad news becomes more precise and less frequent over time. We also study an extension where the principal may let the agent shirk, and show that the optimal incentive scheme features the possibility of tenure, i.e., permanent shirking.
This paper studies how a forward-looking decision maker experiments on unknown alternatives of correlated utilities. The utilities are modeled by a Brownian motion such that similar alternatives yield similar utilities. Experimentation trades off between the continuation value of exploration and the opportunity cost of exploitation. The optimal strategy is to continuously explore unknown alternatives, and then exploit the best known alternative when the one being explored is found to be sufficiently worse than the best one. The decision maker explores unknown alternatives more quickly as they prove to be worse than the best known one. Applied to firm experimentation, my model predicts a conditional version of Gibrat’s law and a linear relation between firm size and profitability.
This paper provides a model of strategic exploration in which competing players independently explore a set of alternatives. The model features a multiple-player multiple-armed bandit problem and captures a strategic tradeoff between preemption—covert exploration of alternatives that the opponent will explore in the future—and prioritization—exploration of the most promising alternatives. Our results explain how the strategic tradeoff shapes equilibrium behaviors and outcomes, e.g., in technology races between superpowers and R&D competitions between firms. We show that players compete on the same set of alternatives, leading to duplicated search from start to finish, and they explore alternatives that are a priori less promising before more promising ones are exhausted. The model also predicts that competition induces players to implement unreliable technologies too early, even though they should wait for the technology to mature. Coordinated exploration is impossible even if the alternatives are equally promising, but it can emerge in equilibrium following a phase of preemptive competition if there is a short deadline. With asymmetric capacities of exploration, the weak player conducts extensive instead of intensive exploration—exploring as many alternatives as the strong player does but never fully exploring any.
PhD in Economics, Columbia University (2023)
MSc in Economics, Toulouse School of Economics (2017)
BSc in Physics and Mathematics. Hong Kong University of Science and Technology (2015)
PhD Dynamic Methods in Economics, Pittsburgh (Spring 2025)
PhD Mathematical Methods in Economics, Pittsburgh (Fall 2024)
MA Game Theory, Warwick (Spring 2024)
UG Mathematical Economics, Warwick (Fall 2023)