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    1. 新聞中心NEWS


      講座:Mean field Principal-Agent Problem and Application in Epidemic Control

      發布者:人力資源辦公室    發布時間:2020-12-29

      題 目:Mean field Principal-Agent Problem and Application in Epidemic Control

      演講人:汪佩奇 博士 普林斯頓大學

      主持人:李成璋 助理教授 上海交通大學安泰經濟與管理學院

      時 間:2021年1月6日(周三)9:00-10:30



      In economics and management science, principal/agent model is an important tool to examine conflicts of interest arising from a contractual relationship and to inform a better design of the contract. Existing framework considers agent as a single entity maximizing its utility under the contract, while ignoring the non-cooperative interactions among the agents.

      Leveraging the recently developed theory of Mean Field Games, we propose a principal/agent model in which the principal faces a large population of agents interacting in a mean field manner and assumed to reach the Nash equilibrium. We first give a brief review of Mean Field Games as a powerful paradigm to depict and analyze the equilibrium of large population of rational agents. We then review recent results on the design of the optimal contract, and how a probabilistic characterization of mean field equilibrium can help to transform it into a tractable problem. We base our discussion on models with finite state space, which find their applications in the pressing issues of epidemic control faced by the government, and we discuss one such model to illustrate its potential to inform the regulatory decisions during the pandemic. 


      Dr. Peiqi Wang is currently a quantitative researcher of credit algorithmic trading at Bank of America Securities. He earned his Ph.D. from Department of Operations Research and Financial Engineering at Princeton University. Before coming to Princeton, he obtained Dipl?me d’Ingénieur from école Polytechnique in France. At Bank of America, he authored the algo pricing engine for US corporate bonds and is among one of the earliest effort to establish the bond market making algo in the firm. His research interests include mean field control and mean field games, stochastic analysis, numerical methods, machine learning and algorithmic trading.