There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Large Language Model (LLM) agents within an evolutionary game-theoretic framework, this paper investigates the complex interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Evolutionary game theory (EGT) is used to quantitatively model the dilemmas faced by each actor, and LLMs provide additional degrees of complexity and nuances and enable repeated games and incorporation of personality traits. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" (not trusting and defective) stances than pure game-theoretic agents. We observe that, in case of full trust by users, incentives are effective to promote effective regulation; however, conditional trust may deteriorate the "social pact". Establishing a virtuous feedback between users' trust and regulators' reputation thus appears to be key to nudge developers towards creating safe AI. However, the level at which this trust emerges may depend on the specific LLM used for testing. Our results thus provide guidance for AI regulation systems, and help predict the outcome of strategic LLM agents, should they be used to aid regulation itself.

本研究解决了在人工智能开发生态系统中促进信任与合作的需求。通过将大型语言模型（LLM）代理嵌入进演化博弈论框架，揭示了AI开发者、监管者与用户之间的复杂互动及其在不同监管情境下的战略选择。研究发现，LLM代理在信任不到位时倾向于采取更“悲观”的立场，这一发现为AI监管系统提供了重要的指导，预测了战略LLM代理在法规中的应用效果。

大型语言模型是否信任人工智能监管？博弈论大型语言模型代理的出现行为