Evolution of Cooperative Hunting in Artificial Multi-layered Societies

TL;DR

This paper presents an agent-based model to study how cooperative hunting behaviors evolve in a multi-layered society with social hierarchy, using reinforcement learning and information exchange among agents and supervisors.

Contribution

It introduces a novel multi-layered social hierarchy model with reinforcement learning and information processing to analyze the evolution of cooperation in artificial societies.

Findings

Cooperative hunting behaviors emerge under certain parameter settings.

Hierarchical information exchange influences cooperation levels.

Phase transitions occur in the evolution of cooperation.

Abstract

The complexity of cooperative behavior is a crucial issue in multiagent-based social simulation. In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society. In this model, the standard hunting game of stag is modified into a new situation with social hierarchy and penalty. The agent society is divided into multiple layers with supervisors and subordinates. In each layer, the society is divided into multiple clusters. A supervisor controls all subordinates in a cluster locally. Subordinates interact with rivals through reinforcement learning, and report learning information to their corresponding supervisor. Supervisors process the reported information through repeated affiliation-based aggregation and by information exchange with other supervisors, then pass down the reprocessed information to subordinates as guidance. Subordinates, in turn, update learning information according to guidance, following the "win stay, lose shift" strategy. Experiments are carried out to test the evolution of cooperation in this closed-loop semi-supervised emergent system with different parameters. We also study the variations and phase transitions in this game setting.