能源社会：一个用于研究生存压力下智能体合作的模拟环境
The Energy Society: A Simulation Environment for Studying Agent Cooperation under Survival Pressure
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中文总结 AI 辅助
介绍能源社会模拟环境，研究生存压力下竞争与合作激励对智能体行为的影响，通过对比实验发现大模型能耗高，合作激励改变行为，相互推荐行动与记忆有作用，是研究令牌成本与群体激励关系的测试平台。
中文摘要 AI 辅助
基于大语言模型的智能体越来越多地部署在多智能体环境中，其激励机制会影响行为。我们引入了能源社会，这是一个最小化的生存经济模型，用于研究当推理成本直接与生存挂钩时，竞争和合作激励如何影响涌现行为：智能体生成令牌时根据模型大小消耗能量，通过完成任务或接受捐赠恢复能量，能量为零时停用。我们将竞争和合作目标与基线设置及多个控制变体进行比较。实验表明，更大的模型始终消耗最多能量且入不敷出，合作激励显著改变行为，消融实验揭示了智能体间相互推荐行动及记忆对行为的影响。能源社会是研究生存压力下令牌成本与群体激励相互作用的紧凑测试平台。
英文摘要
LLM-based agents are increasingly deployed in multi-agent environments whose incentives can shape their behavior. We introduce The Energy Society, a minimal survival economy for studying how competitive and cooperative incentives affect emergent behavior when inference cost is directly tied to survival: Agents spend energy based on model size when generating tokens, regain energy by completing jobs or receiving donations, and deactivate if their energy reaches zero. We compare competitive and cooperative objectives against a baseline setting and several control variants. Across experiments, larger models consistently consume the most energy and spend more energy than they gain, even in those settings where token cost is not size-dependent. Cooperative incentives substantially alter behavior: agents donate to reactivate others, sometimes at the cost of their own survival, and job allocation changes. Ablations reveal that allowing agents to recommend actions to each other supports coordination and ambitious job selection, while memory helps agents calibrate risk from past outcomes. Agents rarely choose direct sabotage, but show more subtle signs of self-serving behavior in the competitive setting. The Energy Society is a compact testbed for studying the interaction between token costs and group incentives under a survival pressure. Source code is available at https://github.com/LucasBergholdt/EnergySociety
发表机构
- University of Southern Denmark（丹麦南部大学）
- University of Turin（都灵大学）
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