Competition and Cooperation Mechanisms for Collective Behavior in Large Multi-agent Systems

We consider a 2-dimensional discrete space modeled by Cellular Automata consisting of m x n cells that can be occupied by agents. There exist several types of agents which differ in their way of behavior related to their own strategy when they interact with neighbors. We assume that interaction between agents is governed by a spatial Prisoner's Dilemma game. Each agent participates in several games with his neighbors and his goal is to maximize his payoff using own strategy. Agents can change their strategies in time by replacing their own strategy with a more profitable one from its neighborhood. While agents act in such a way to maximize their incomes we study conditions of emerging collective behavior in such systems measured by the average total payoff of agents in the game or by an equivalent measure-the total number of cooperating players. These measures are the external criteria of the game, and players acting selfishly are not aware of them. We show experimentally that collective behavior in such systems can emerge if some conditions related to the game are fulfilled. We propose to introduce an income-sharing mechanism to the game, giving a possibility to share incomes locally by agents. We present the results of an experimental study showing that the sharing mechanism is a distributed optimization algorithm that significantly improves the capabilities of emerging collective behavior measured by the external criterion of the game.