Seminar Details
2025-05-20 (14h) : Model-Based and Data-Driven Interventions in Network Games
At Euler building (room A.002)
Speaker :
Nima Monshizadeh (University of Groningen)
Abstract :
In modern cyber-physical-human systems like power grids and traffic networks, self-interested decisions by users or firms often lead to inefficiencies such as congestion, blackouts, and systemic risks. These challenges underscore the need for effective intervention mechanisms to coordinate self-interested behavior. However, designing effective interventions with guaranteed performance is challenging, as planners typically lack detailed knowledge of users’ private preferences or behaviors. This informational gap and privacy considerations complicate the prediction of user responses and hinder the development of suitable control strategies. In this talk, we examine the problem of intervention design in network games, where agents’ cost or utility functions are interdependent; as exemplified by applications such as multi-commodity markets. The central question is how the type of information available to the planner — ranging from full to partial knowledge of utility functions, network structure, or desired target profiles — shapes the design of effective interventions. We also give special attention to scenarios in which the planner has no a priori knowledge of agent cost functions and must rely instead on historical observations of agent actions and past interventions. Given these diverse informational settings, we discuss a range of static, dynamic, adaptive, and data-driven intervention strategies aimed at steering the system toward socially desirable outcomes. The results illustrate how combining control-theoretic, game-theoretic, and data-driven insights enables the design of interventions that are effective under various informational constraints.