Information spread in networks: Control, games, and equilibria

We design intervention schemes to control information spread in multi-agent systems. We consider two information spread models: linear distributed averaging and virus spread dynamics. Using the framework of differential games, we design a dynamical optimization framework that produces strategies that are robust to adversarial intervention. For linear dynamics, we show that optimal strategies make connection to potential-theory. In the virus spread case, we show that optimal controllers exhibit multiple switches. Moreover, we establish a connection between game theory and dynamical descriptions of network epidemics, which provides insights into decision making in infected networks. Finally, we present initial building blocks for network controllability using a limited number of controls.