Behavior optimization in large distributed systems modeled by cellular automata

Abstract

We consider a distributed system modeled by the second-order Cellular Automata (CA) and interpreted as a multi-agent system, where interactions between agents are defined by a spatial Prisoner’s Dilemma game. The idea of the second-order CA is based on the concept “adapt to the best neighbor. Each agent uses some strategy for the selection of actions used against opponents and can change it during the iterated game. An agent acts in such a way to maximize its income. We intend to 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 criterion 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 on a wide range of the game parameters.