A dynamic model of social network formation

We consider a dynamic social network model in which agents play repeated ga mes in pairings determined by a stochastically evolving social network. Ind ividual agents begin to interact at random, with the interactions modeled a s games. The game payoffs determine which interactions are reinforced, and the network structure emerges as a consequence of the dynamics of the agent s' learning behavior. We study this in a variety of game-theoretic conditio ns and show that the behavior is complex and sometimes dissimilar to behavi or in the absence of structural dynamics. We argue that modeling network st ructure as dynamic increases realism without rendering the problem of analy sis intractable.