Measuring emergent behaviors in a mixed competitive-cooperative environment

A fundamental challenge in evolutionary theory is explaining the evolution of cooperative (or altruistic) tenden- cies despite local competition among agents can limit cooper- ative benefits. In this paper, an agent-based model is devel- oped that combines network evolution strategies with conflict- ing pressures to induce cooperation as an emergent behavior. Thoroughly, we define a model of two agents able to evolve co- operative actions in a mixed competitive-cooperative environ- ment. Specifically, two simulated E-puck robots are put inside an arena filled with diverse kinds of food items (i.e., individual, social). The goal is to survive as long as possible by eating food to contrast energy consumption. Robot controllers, which de- termine the agent’s interaction with other agents, are evolved by using a genetic algorithm. Simulation results suggest that by side with expected behaviors, a new strategy emerges without any external pressure. Outcomes allow conclusions about the feasible cooperation choices individuals should make when par- ticipating in complex mixed cooperative-competitive scenarios. In particular, we observe a natural emergence of opportunistic behaviors in agents when such strategies can lead to the team’s success.