Emergence of agriculture in an artificial society of reinforcement learning agents

TL;DR

This paper demonstrates how agricultural practices can spontaneously emerge in an artificial society of reinforcement learning agents, revealing universal principles of complex behavior emergence.

Contribution

It introduces a novel artificial society model that uncovers key mechanisms behind the emergence of agriculture through reinforcement learning and social dynamics.

Findings

Agriculture emerges spontaneously without explicit instructions.

Social learning acts as a firewall against cheaters.

Emergence of agriculture leads to population growth and resource amplification.

Abstract

The origin of agriculture represents a major evolutionary transition and a paradigmatic example of how complex collective behaviors emerge from simple interactions. Here we introduce an artificial society of reinforcement learning agents embedded in a dynamic ecological environment to identify general principles underlying this transition. Within this system, agricultural practices emerge spontaneously - without explicit instruction - through the coupled dynamics of learning and environmental modification. We show that this transition is governed by four key ingredients: individual planning through the valuation of delayed rewards, social vulnerability to cheaters, stabilization via social learning, and an emergent lock-in effect that renders agriculture effectively irreversible once established. In particular, we demonstrate that social learning acts as a "firewall" that suppresses cheater invasion and enables the propagation of successful strategies, leading to sustained population growth and nonlinear amplification of domesticated resources. Together, these results reveal universal mechanisms linking individual decision-making, social interactions, and ecological feedbacks. More broadly, they highlight the potential of artificial societies as experimental platforms to study the emergence of cultural innovations and major evolutionary transitions.