Evolution of cooperation within a behavior-based perspective: Confronting nature and animats

Abstract

We study the evolution of social behaviors within a behavioral framework. To this end, we define a “minimal social situation" that is experimented with both humans and simulations based on reinforcement learning algorithms. We analyse the dynamics of behaviors in this situa- tion by way of operant conditioning.We show that the best reinforcement algorithm, based on Staddon-Zhang's equations, has a performance and a variety of behaviors that comes close to that of humans, and clearlyoutperforms the well-known Q-learning. Though we use here a rather simple, yet rich, situation, we argue that operant conditioning deserves much study in the realm of artificial life, being too often misunderstood, and confused with classical conditioning.