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We investigate a problem related to the controllability ofnetworked evolutionary games, first presenting an algorithmthat computes a near-minimal set of nodes to drive all nodesin a tree network to a desired strategy, and then briefly discussingan algorithm that works for arbitrary networks usinggraph partitioning.The study of evolutionary games (EGs) on networks is atopic that has recently become prominent in biology, sociology,economics, physics and a wide range of other disciplinesin which dynamic agents interact over networks. Althoughthe primary focus has been to understand the emergenceof cooperation and other behaviors for various typesof games, dynamics, and network structures, there are manysituations throughout the fields mentioned above in whichone might want to control the outcome of the game or causea particular behavior to emerge. Although we are startingto see more literature on evolutionary games from a feedbackcontrol perspective [1] [2], to our knowledge there hasnot yet been an algorithmic method propo |
