Research Perspective: 

My current research centres on designing and analysing dynamic matching markets, where participants, their preferences and available resources evolve over time. I use game theory and mechanism design techniques to formalize how agents interact under incomplete or costly-to-verify information. A key goal is to construct mechanisms that remain fair and efficient over multiple rounds of interaction. Another important direction is examining the price of fairness in these dynamic settings—understanding the trade-offs between equitable outcomes and overall efficiency. Recently, I have been focusing on international kidney exchange programs, exploring how to balance the cooperative gains among countries while ensuring no group is disadvantaged by participating.


Completed Papers:

Abstract: We study a model in which a sender tries to persuade a decision-maker by committing to a signaling strategy (Kamenica and Gentzkow, 2011). The decision-maker observes this and, in turn, commits to an interim verification strategy that reveals the true state of the world with an endogenously determined probability. The possibility of verification induces a trade-off for the sender: providing more information decreases the verification probability but also decreases the ex-ante persuasion payoff. The decision-maker trades off learning the state with more precision against the cost of verification. In equilibrium, the sender provides more information, which decreases the sender’s welfare and increases that of the decision-maker. We provide the precise condition under which social welfare is higher compared to the case without verification.


Work In Progress:

Abstract: This paper develops a theoretical framework for International Kidney Paired Exchange (IKPE) to address efficiency and fairness concerns in multi-country kidney-paired exchange networks. Drawing on a discrete version of the Kalai–Smorodinsky (KS) bargaining solution, we propose a mechanism that first maximizes worst country’s relative gain, and then selects an allocation that achieves the largest overall transplant benefit. The model features a dynamic weighting scheme that adjusts over time to compensate countries whose participation yields lower gains in the past rounds compared to other countries, ensuring an equitable distribution of cooperative gains. We prove that the proposed mechanism guarantees Pareto optimality and individual rationality of the resulting allocation as no country is worse off by cooperating than by acting alone. 




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