Co-evolution in social interactions

An interesting problem which has been widely investigated is under what circumstances will a society of rational agents realize some particular stable situations, and whether they satisfy the condition of social efficiency? This will crucially depend on how they interact and what information they have when they interact. For instance, when strategic interactions are modeled as coordination games, it is known that the evolutionary process selects the risk-dominant equilibrium which is not necessarily efficient. We consider the networks of agents, in which each agent faces several types of strategic decision problems. We investigate the dynamics of collective decisions when each agent adapts the strategy of interaction to its neighbors. We are interested in showing how society gropes its way towards an equilibrium situation. We show that society selects the most efficient equilibrium among multiple equilibria when the agents composing it, learn from each other as collective learning, and they co-evolve their strategies over time. We also investigate the mechanism that leads society to an equilibrium of social efficiency.