Emergence of cooperation through simulation of moral behavior

Abstract

Human behavior can be analysed through a moral perspective when considering strategies for cooperation in evolutionary games. Presuming a multiagent task performed by self-centered agents, artificial moral behavior could bring about the emergence of cooperation as a consequence of the computational model itself. Herein we present results from our MultiA computational architecture, derived from a biologically inspired model and projected to simulate moral behavior through an Empathy module. Our testbed is a multiagent game previously defined in the literature such that the lack of cooperation may cause a cascading failure effect ("bankruptcy") that impacts on the global network topology via local neighborhood interactions. Starting with sensorial information originated from the environment, MultiA transforms it into basic and social artificial emotions and feelings. Then its own emotions are employed to estimate the current state of other agents through an Empathy module. Finally, the artificial feelings of MultiA provide a measure (called well-being) of its performance in response to the environment. Through that measure and reinforcement learning techniques, MultiA learns a mapping from emotions to actions. Results indicate that strategies relied upon simulation of moral behavior may indeed help to decrease the internal reward from selfish selection of actions, thus favoring cooperation as an emergent property of multiagent systems.