Designing institutions for social-ecological systems requires models that capture heterogeneity, uncertainty, and strategic interaction. Multiple modeling approaches have emerged to meet this challenge, including empirical game-theoretic analysis (EGTA), which merges ABM's scale and diversity with game-theoretic models' formal equilibrium analysis. The newly popular class of LLM-driven simulations provides yet another approach, and it is not clear how these approaches can be integrated with one another, nor whether the resulting simulations produce a plausible range of behaviours for real-world social-ecological governance. To address this gap, we compare four LLM-augmented frameworks: procedural ABMs, generative ABMs, LLM-EGTA, and expert guided LLM-EGTA, and evaluate them on a real-world case study of irrigation and fishing in the Amu Darya basin under centralized and decentralized governance. Our results show: first, procedural ABMs, generative ABMs, and LLM-augmented EGTA models produce strikingly different patterns of collective behaviour, highlighting the value of methodological diversity. Second, inducing behaviour through system prompts in LLMs is less effective than shaping behaviour through parameterized payoffs in an expert-guided EGTA-based model.

翻译：设计社会生态系统的制度需要能够捕捉异质性、不确定性和策略互动的模型。为应对这一挑战，已涌现出多种建模方法，包括经验博弈论分析（EGTA），该方法将基于主体的建模（ABM）的规模与多样性与博弈论模型的正式均衡分析相结合。新兴的LLM驱动模拟提供了另一种途径，但目前尚不清楚这些方法如何相互整合，以及由此产生的模拟是否能产生现实世界社会生态治理中合理的行为范围。为填补这一空白，我们比较了四种LLM增强框架：程序化ABM、生成式ABM、LLM-EGTA以及专家指导的LLM-EGTA，并以阿姆河流域在集中式和分散式治理下的灌溉与渔业为真实案例进行评估。研究结果表明：首先，程序化ABM、生成式ABM和LLM增强的EGTA模型产生了显著不同的集体行为模式，凸显了方法多样性的价值。其次，通过LLM系统提示诱导行为的效果，不如在专家指导的基于EGTA模型中通过参数化收益塑造行为有效。