Complex Network Games: the Scenario Approach

We aim at developing a theory and ecient solution algorithms for strategic decision making problems over networks subject to stochastic uncertainty, through methods in the broad area of operations research, specifically in computational game theory and chance constrained programming.

Motivated by the large-scale optimization and control problems arising in modern power, communication and transportation networks, where multiple self-interested decision makers compete for common yet uncertain resources, we will utilize chance constraints to limit stochastic uncertainty, and will study the distributed computation of equilibria in chance constrained network games.

With this aim, we will develop the new framework of \chance constrained network games" and start bridging the gap between game theory and chance constrained optimization. Our developments will open new research opportunities across computational game theory and operations research, e.g. chance constrained programming for multi-objective optimization.

Specifically, we will:

  • extend the successful \scenario approach" optimization, currently limited to standalone chance constrained optimization problems, to chance constrained network games;
  •  develop distributed \dynamic control" strategies for agents interacting in network games via tools from xed point operator theory.

Our theory will impact the analysis, design and control of strategic decision making over networks subject to stochastic uncertainty, a topic of broad interest in operations research, systems and control theory.


Our algorithms will impact the management and control for large-scale complex systems of systems, e.g. power, communication and transportation systems.

Project team members

Dr. ing. S. (Sergio) Grammatico
PhD Mattia Bianchi


Scenario approach, Distributed control algorithms, Multi-agent systems, Computational game theory, Variational inequalities.

Sponsored by

NWO Top-grant