Emergence of agriculture in an artificial society of reinforcement learning agents

2026-05-21

AI Summary

This study investigates how agriculture spontaneously emerges from individual interactions by developing a multi-agent reinforcement learning model embedded within a dynamic ecological environment. Agents, through coupled processes of learning and environmental modification, autonomously evolve agricultural behaviors. The research identifies four key mechanisms driving the origins of agriculture: delayed reward evaluation, social vulnerability to defectors, the stabilizing role of social learning, and path-dependent lock-in effects. Notably, social learning suppresses defection and facilitates the spread of effective strategies. The model successfully reproduces the spontaneous emergence and irreversible entrenchment of agriculture, yielding sustained population growth and nonlinear amplification of domesticated resources. These findings demonstrate a universal synergistic mechanism linking individual decision-making, social interaction, and ecological feedback in the transition to agricultural systems.

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.

Problem

• agricultural emergence
• collective behavior
• evolutionary transition
• cultural innovation
• artificial society

Innovation

• reinforcement learning agents
• artificial society
• emergence of agriculture
• social learning
• ecological feedback