PhD Dissertation Defense, Wei Zhao, Economics and Decision Sciences

Congratulations to Dr Wei Zhao, Economics and Decision Sciences specialization, who successfully defended his Doctoral Dissertation at HEC Paris, on June 21, 2022. Dr Zhao has accepted a position at Renmin University - School of Economics (China) as Assistant Professor, starting September, 2022.

Specialization: Economics and Decision Sciences

Topic: Essays in Information Design and network theory

Supervisor: Tristan Tomala, Professor, HEC Paris 

Jury Members:

• Bruno BIAIS, Professor, HEC Paris 
• Vasiliki SKRETA, Professor, University of Texas at Austin (USA)
• Eduardo PEREZ, Associate Professor, Sciences Po Paris (France) 
• Ludovic Renou, Professor, Queen Mary University (UK)
• Tristan TOMALA Professor, HEC Paris - supervisor

Abstract:
This thesis studies dynamic information design and network theory. The thesis is composed of four chapters. In the first chapter, I study informa tion design in a dynamic moral hazard environment. An agent and an expert face a common uncertainty regarding the effectiveness of a collective decision. The agent bears the cost of effort of information ac quisition and makes the final decision. The expert is the only observer of research outcomes and provides information over time to the agent. Both parties are equally affected by the decision. I show that one opti mal information policy consists in disclosing truthfully with delay. In the first periods of time, the delay is zero, then strictly increases and finally vanishes. By the time the delay decreases back to zero, the agent has taken the decision with probability one. In the second chapter, we study a dynamic principal-agent problem, where the sole instrument the principal has to incentivize the agent is the disclosure of informa tion. The principal aims at maximizing the (discoun ted) number of times the agent chooses the principal’s preferred action. We show that there exists an optimal contract, where the principal stops disclosing informa tion as soon as its most preferred action is a static best reply for the agent, or else continues disclosing information until the agent perfectly learns the princi pal’s private information. If the agent perfectly learns the state, he learns it in finite time with probability one ; the more patient the agent, the later he learns it. In the third chapter, two types of intervention are commonly implemented in networks : characteristic intervention which influences individuals’ intrinsic incentives, and structural intervention which targets at the social links among individuals. In this paper we provide a general framework to evaluate the distinct equilibrium effects of both types of interventions. We identify a hidden equivalence between a structural intervention and an endogenously determined characteristic intervention. Compared with existing approaches in the literature, the perspective from such an equivalence provides several advantages in the analysis of targeting inter ventions of the network structure. We present a wide range of applications of our theory, including deter mining whether a structural intervention is beneficial, identifying the most wanted criminal(s) in delinquent networks, and targeting the key bridge nodes for dis connected communities. In the last chapter, we study the problem of designing efficient network sequen tially. In each period, the planner connects two unlin ked agents in the network formed in previous period, then the agents play a game with local complemen tarity under the newly formed network. The planner benefits from the entire discounted stream of equili brium welfare. We show that, forming a nested split graph in each period is an optimal strategy for the planner for any specific values of discount factors. Mo reover, when the planner heavily discounts future wel fare, the optimal strategy induces a quasi-complete graph in each period regardless of the strength of complementary effect. Our paper therefore provides a micro-foundation for quasi-complete network since it is formed under greedy algorithm. We also discuss the robustness of these results under non-linear best response and heterogeneous agents.
 

Keywords : Dynamic Information Design ; Social Network Theory ; Structural Intervention