Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents
Abstract
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.
Cite
@article{arxiv.2504.08640,
title = {Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents},
author = {Alessio Buscemi and Daniele Proverbio and Paolo Bova and Nataliya Balabanova and Adeela Bashir and Theodor Cimpeanu and Henrique Correia da Fonseca and Manh Hong Duong and Elias Fernandez Domingos and Antonio M. Fernandes and Marcus Krellner and Ndidi Bianca Ogbo and Simon T. Powers and Fernando P. Santos and Zia Ush Shamszaman and Zhao Song and Alessandro Di Stefano and The Anh Han},
journal= {arXiv preprint arXiv:2504.08640},
year = {2025}
}