Publications

We study the optimal joint intervention of a planner who can influence both the standalone marginal utilities of agents in a network and the weights of the links connecting them. The welfare-maximizing intervention displays two key features. First, when the planner's budget is moderate (yielding interior solutions), the optimal change in link weight between any pair of agents is proportional to the product of their eigen-centralities. Second, when the budget is sufficiently large, the optimal network converges to a simple structure: a complete network under strategic complements, or a complete balanced bipartite network under strategic substitutes. We show that welfare effects are governed by the principal eigenvalue of the network, while distributional outcomes are driven by the dispersion of the corresponding eigen-centralities. Comparing joint interventions to single interventions targeting only standalone marginal utilities, we find that joint interventions consistently generate higher aggregate welfare, but may also increase inequality, revealing a potential trade-off between efficiency and equity.

We study a mechanism design problem where the allocation rule is randomized and transfers are contingent on outcomes. In this problem, an agent reports his private information, and an exogenous randomized allocation rule assigns an outcome based on the report. A planner designs an outcome-contingent transfer to incentivize the agent to report truthfully. We say that the allocation rule is implementable if such transfers exist. For this implementation problem, we derive two sufficient and necessary conditions. Each has a geometric interpretation. Moreover, when the allocation rule is implementable, we construct transfers that implement the allocation rule.

We study the optimal design of relational contracts that incentivize an expert to share specialized knowledge with a novice. While the expert fears that a more knowledgeable novice may later erode his future rents, a third-party principal is willing to allocate her resources to facilitate knowledge transfer. In the unique profit-maximizing contract between the principal and the expert, the expert is asked to train the novice as much as possible, for free, in the initial period; knowledge transfers then proceed gradually and perpetually, while the principal offers lump-sum compensations to the expert right after verifying each transfer; even in the long run, a complete knowledge transfer might not be attainable. Our analysis sheds light on the success of several prominent cross-border technology transfers that took place in China's auto industry and Korea's high-speed rail development.

We propose a novel mediated communication protocol where the Sender can both transmit messages and burn money. We assume that the Sender has state-independent preferences. Our main result proves that increasing the budget must strictly improve the Sender's payoff, unless the payoff collapses to the cheap talk value when the budget exceeds the range of the Sender's value function. By this result, we can further show that the money-burning tactic must strictly improve the Sender's payoff in almost all scenarios unless the commitment is valueless. We also characterize the Sender's maximum equilibrium payoff. This characterization uncovers a connection to two types of robust Bayesian persuasion. Furthermore, our communication protocol directly applies to Web 3.0 communities, clarifying the value of commitment in these contexts.

In light of prevailing data regulations, consumer mobility across diverse markets inherently endogenizes market segmentation. Considering such strategic interactions, we define a market segmentation as strategy-proof when no consumer (with positive measure) has an incentive to deviate to another market. We show that in every strategy-proof market segmentation, the producer surplus remains at the uniform monopoly level, and the consumer surplus is bounded between the buyer-optimal level and the uniform monopoly level. Remarkably, no consumer is worse off than in the case of a uniform monopoly. We also construct a family of strategy-proof segmentations to realize every possible welfare outcome.

We study when an allocation rule is implementable in Bayesian mechanisms. We provide a necessary and sufficient condition on this implementability problem with no restriction on the allocation rule, the joint type distribution, or the belief. Our proof is based on the fundamental duality theorem and network flow theory. Generically, for almost all joint type distribution, any allocation is implementable in Bayesian mechanisms.