Abstract
This paper presents an agent-based model (ABM) of a sustainability game in which each agent is powered by a Large Language Model (LLM). The simulation model explores how LLM-based agents manage the tension between short-term competitive advantage and long-term ecological sustainability. By embedding agents in a resource-constrained environment—featuring renewable and non-renewable assets, military conflict, and shared environmental limits—the paper investigates whether and under what conditions LLMs can adopt sustainable behaviors. Several experimental scenarios are evaluated with different strategies endowed to agents, also varying the number of agents, the connectivity of the relationship network and forecast length. Results show that LLM agents can more likely achieve sustainable collective outcomes when unguided or when provided with explicitly sustainable strategies. Also, explicit strategies significantly influence system dynamics—occasionally leading to ecological collapse or aggressive domination. Findings suggest that even shallow behavioral priors can steer LLM-based agents toward or away from sustainability, and that tests of this kind may serve as valuable tools for assessing alignment and coordination in multi-agent LLM systems. Moreover, the results provide insight to confirm that LLM-enhanced ABMs could be used in sustainability issues.