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.

翻译：农业起源代表了一次重大的进化转变，也是复杂集体行为如何从简单互动中涌现出的典型范例。在此，我们引入了一个嵌入动态生态环境的强化学习智能体人工社会，以识别这一转变背后的通用原则。在该系统中，农业实践通过学习和环境改造的耦合动力学自发涌现——无需任何明确指令。我们表明，这一转变由四个关键要素驱动：通过延迟回报估值的个体规划、易受欺骗者影响的社会脆弱性、通过社会学习实现的稳定化，以及一旦建立便使农业实际上不可逆的涌现锁定效应。特别地，我们证明社会学习起到了“防火墙”的作用，抑制了欺骗者的入侵，并促成了成功策略的传播，从而带来持续的人口增长和驯化资源的非线性放大。这些结果共同揭示了连接个体决策、社会互动与生态反馈的通用机制。更广泛地，它们凸显了将人工社会作为研究文化创新涌现和重大进化转变的实验平台的潜力。