Xueheng Li. Designing Weighted and Directed Networks under Complementarities. Games and Economic Behavior (2023 July). https://doi.org/10.1016/j.geb.2023.04.010.
Xueheng Li. Resentment and the Evolution of Cooperative Norms (2022). Available at SSRN: https://ssrn.com/abstract=3512872.
Work in Progress
Xueheng Li. Stochastic Stability in Psychological Games. This project is funded by the National Natural Science Foundation of China for Young Scholars（国家自然科学基金青年项目）, Grant No. 72203105, 2023-2025.
- Psychological game theory is a general framework to study interactions between individuals with belief-dependent preferences. Psychological games, however, often admit multiple equilibria. This project applies and extends the concepts and techniques of stochastic dynamics and stability to examine the social dynamics of high-order beliefs in psychological games. The aim is to pin down the most robust equilibrium for each generic psychological game.
Yanlin Chen, Xueheng Li, and Tianle Song. The Pareto Principle: A Network Formation Model of Public Goods Provision.
- In the context of public goods provision, the Pareto principle — also called the 80-20 rule — states that most to all public goods are provided by a roungly fixed small but non-negligible proportion (say, 20%) of individuals, the “vital few”. We consider a network formation game in which public goods provision are endogenously determined. Every strict equilibrium network exhibits 1) a nested upward-linking network structure and 2) the Pareto principle.
Valeria Burdea and Xueheng Li. The Market for Lemons and Liars.
- It has been proposed that intrinsic preferences for truth-telling can mitigate adverse selection in the market. We argue that relying on intrinsic preferences for truth-telling alone is unlikely to mitigate adverse selection, because there is self-selection and a market for liars: people with low lying aversion self-select into cheap-talk markets with lax information disclosure rules, leading to inefficiency as if all individuals are material payoff maximizers.