Modeling Complexity: From Cellular Automata to Evolutionary Game Theory and Multiagent Systems with Coalitions

Abstract

Since its origins, Cellular Automata (CA), have been used to model many type of physical and computational phenomena. Interacting CAs in spatial lattices combined with evolutionary game theory have been very popular for modeling genetics or behavior in biological systems. Refining and extending the behavior of each automaton we can obtain a framework where multiple autonomous entities (agents) interact, i.e., a Multiagent System (MAS). Multiagent systems can be used to solve problems that are difficult to manage using monolithic approaches. The dynamic formation of coalitions is nowadays a well-known area of interest in MAS, as coalitions can help self-interested agents to successfully cooperate and coordinate in a mutually beneficial manner. In this presentation, we will explore the use of MAS, as refined cellular automata, combined with evolutionary game theory and coalitions. We shall consider several application scenarios (social sciences, economy, optimization, ...) and also different interaction topologies ranging from spatial to complex networks.