The Energy Society: A Simulation Environment for Studying Agent Cooperation under Survival Pressure

The success of deployed agents relies on their ability to handle open-ended user requests using their inherent capabilities, not only in solving requests directly but also in effectively leveraging inter-agent communication channels and feedback signals over time. This requires a multi-agent environment where agents can operate autonomously, strategically communicate, behave collaboratively and be driven by economic incentives, much like humans in society. Towards this vision, we propose $\mathtt{AgentSociety}$, a mechanism that enables decentralized agentic collaboration grounded in liquid democracy and information diffusion from social choice theory. We show that $\mathtt{AgentSociety}$ provides an environment for agents to make autonomous decisions utilizing their local context to maximize their utility while achieving collective outcomes through incentivized collaboration. Specifically, we prove that delegation to more competent neighbor agents is incentive compatible and naturally generates multi-agent routing path by consensus. Additionally, our mechanism incentivizes agents to selectively disclose information to their neighbor agents when doing so aligns with their self-interest, so as to garner influence. We characterize the Nash equilibrium showing that agent payoffs are reflective of their marginal contributions. We compare and benchmark strategy profiles adopted by open and proprietary state-of-the-art language models deployed in $\mathtt{AgentSociety}$ against best response. Finally, we evaluate collaborative performance from consensus-based routing among self-interested heterogeneous agents in $\mathtt{AgentSociety}$ on real-world datasets.

LLMs are increasingly deployed as agents that plan, use tools, and act over time. When they share persistent resources, such as compute pools or energy reserves, decisions by one agent affect the conditions faced by later agents. We study this coordination failure in a renewable energy commons. Four same-family GPT, Gemini, or Grok agents act in homogeneous self-play as electricity prosumers, instructed to maximize operational continuity. Holding aggregate residual demand and the decision protocol fixed, we vary the regeneration rate of a shared energy reserve from abundance to scarcity. All three families preserve the reserve when demand does not exceed peak renewable replacement, but over-appropriate it beyond that threshold (all nine exact scarcity contrasts survive Holm correction; largest adjusted p = 4.87e-5). The pattern is self-defeating: the same populations protect current service while undermining future service. At higher scarcity (rho = 1.2), early aggregate request pressure exceeds peak renewable replacement in every family and averages 1.21 times that level. Mean trajectories fall below the reserve level of maximum replenishment by rounds 5-7. Two offline benchmarks compare a social planner maximizing group-wide operational-service value with open access, where each prosumer maximizes its own value. At a discount factor of gamma = 0.95, both benchmarks sustain the reserve under the same dynamics. Realized depletion instead resembles outcomes under a more impatient open-access benchmark. The populations therefore behave like impatient optimizers at the level of the public trajectory. This system-level alignment failure would be missed by isolated-response evaluation.

How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interactions from the physical costs of behavior, while artificial life models traditionally study emergent self-replication without formalizing the dilemma between acquiring resources and preserving the shared energy needed to reproduce. In contrast, we introduce Autopoietic Game Theory, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving. We study these dynamics using a computational substrate of randomly initialized programs in Z80 machine code, showing empirically, and motivating with a simplified theoretical model, that embedding a social dilemma directly into the physics of computation can favor the emergence of self-replicating, cooperative strategies. When resources are scarce, our analysis shows that defection can become self-limiting even in well-mixed populations: parasitic stealing destroys shared energy, slows execution, and can prevent reliable replication. Empirically, evolved programs suppress stealing across several Z80 environments, while spatial assortment further supports structural complexity and task performance. We further show that the framework can incorporate exogenous pressures, such as math tasks structured as sequential social dilemmas, when rewards are tied to computation budgets. These results suggest that coupling an agent's capacity for computation to its available energy transforms cooperation into a dominant scaffolding for building sustainable, self-organizing systems.