Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Buscemi, Alessio, Proverbio, Daniele, Bova, Paolo, Balabanova, Nataliya, Bashir, Adeela, Cimpeanu, Theodor, da Fonseca, Henrique Correia, Duong, Manh Hong, Domingos, Elias Fernandez, Fernandes, Antonio M., Krellner, Marcus, Ogbo, Ndidi Bianca, Powers, Simon T., Santos, Fernando P., Shamszaman, Zia Ush, Song, Zhao, Di Stefano, Alessandro, Han, The Anh
Institutions: Luxembourg Institute of Science and Technology, University of Trento, Teesside University, University of Birmingham, University of St Andrews, Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, Université Libre de Bruxelles, Vrije Universiteit Brussel, University of Stirling, University of Amsterdam
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4414829248