Dynamics of Cooperation and Conflict in Multiagent Systems

Abstract

Meeting today’s major scientific and societal challenges requires understanding the dynamics of cooperation, coordination, and conflict in complex adaptive systems (CAS). Artificial Intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic CAS. My research lies at the interface between CAS and AI: I develop computational methods to understand cooperation and conflict in multiagent systems, and how these depend on systems’ design and incentives. I focus on mapping interaction rules and incentives onto emerging macroscopic patterns and long-term dynamics. Examples of this research agenda, that I will survey in this talk, include modelling (1) the connection between reputation systems and cooperation dynamics, (2) the role of agents with hard-coded strategies in stabilizing fair behaviors in a population, or (3) the impact of recommendation algorithms on potential sources of conflict (e.g., radicalization and polarization) in a system composed of adaptive agents influencing each other over time.