Recent years have witnessed significant interest in the area of multiagent or distributed architecture control, with applications ranging from autonomous vehicle teams to communication networks to smart grid. The general setup is a collection of multiple decision-making elements interacting locally, perhaps striving to achieve a common collective objective. In multiagent learning, agents dynamically adapt to the actions of other agents, thereby effectively making the environment non-stationary from the perspective of any single agent. The resulting dynamics can exhibit behaviors ranging from chaos to convergence. This talk focuses on the two concerns of stability and selection---i.e., do agents converge, and if so, to what configurations? We discuss "stability" of population games through new connections between passivity theory and evolutionary game theory. We discuss "selection" in evolutionary games using the notion of stochastic stability and demonstrate its broader applicability in various settings.
Group for Research in Decision Analysis