Modern socio-technical systems, such as social networks, energy and financial systems, teams of robots, or transportation systems, are highly complex, involving large numbers of strategic agents that interact and influence each other in heterogeneous ways. Modeling, analysis, and design of such system integrates a wide scope of ideas, from engineering aspects (for technology and communications) to human behavior and regulations (requiring concepts from sociology and economics). Understanding the way in which such distributed systems of people and devices interact and work together, and how to optimize those systems and interactions is a main focal area of the FIND group. To this end, the group leverages interdisciplinary techniques from optimization, control, game theory, network science and network economics.
Specific research areas: Algorithmic game theory, decentralized and distributed control of multi-agent and large scale systems, mechanism design, distributed estimation and detection, empirical and theoretical analysis of social systems, epidemiology, network optimization and regulation, online learning and data-driven analysis of complex networks.