Nima Monshizadeh

Engineering and Technology Institute, University of Groningen, The Netherlands

Talk: Model-Based and Data-Driven Interventions under Informational Constraints

In modern cyber-physical-human systems (CPHS), from power grids to traffic networks, individual agents such as users, firms, or devices make decisions based on self-interest. While rational from a local perspective, the aggregate effect of these decisions often diverges from socially optimal outcomes, leading to inefficiencies such as congestion, blackouts, and systemic instabilities. These instances, and their potentially devastating societal consequences, underscore the need for effective intervention mechanisms to coordinate self-interested behavior. To address this, system planners often rely on interventions such as taxes and incentives. However, designing such 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 hinders 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 energy markets. A key focus 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 in CPHS.

Bio: Nima Monshizadeh is an Associate Professor in the Engineering and Technology Institute Groningen at the University of Groningen, Netherlands. He received his Ph.D. degree with honors (cum laude) from the Bernoulli Institute for Mathematics and Computer Science at the University of Groningen. Previously, he served as a Research Associate with the control group at the University of Cambridge (2017-2018) and held visiting scholar positions at ETH Zurich (2015) and UCLA in Los Angeles (2018). He has been an associate editor of Automatica since 2020 and was a member of the IEEE Conference Editorial Board (2020-2023). His primary research area focuses on the optimization and control of cyber-physical systems.