Robust Network Targeting with Multiple Nash Equilibria
Holder: Guanyi Wang(University College London)
Time:2024-12-19 15:10-17:00
Location:Room 217, Guanghua Building 2
Abstract:
Many policy problems involve designing individualized treatment allocation rules to maximize the equilibrium social welfare of interacting agents. Focusing on large-scale simultaneous decision games with strategic complementarities, we develop a method to estimate an optimal treatment allocation rule that is robust to the presence of multiple equilibria. Our approach remains agnostic about changes in the equilibrium selection mechanism under counterfactual policies, and we provide a closed-form expression for the boundary of the identified set of equilibrium outcomes. To address the incompleteness that arises when an equilibrium selection mechanism is not specified, we use a maximin welfare criterion to select a policy based on the “least favourable” equilibrium outcome, and implement this policy using a greedy algorithm. We establish performance guarantees for our method by deriving a welfare regret bound, which accounts for sampling uncertainty and the use of a greedy algorithm. We demonstrate our method with an application to the microfinance dataset of Banerjee et al. (2013).
About the Speaker:
I am an econometrician from University College London, and my research integrates econometrics and microeconomic theory to develop statistical treatment allocation policies that specifically consider spillover effects through strategic interactions among units. I am also interested in social networks, and statistical decision theory.

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