Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents
Journal:
arXiv
Published Date:
Apr 11, 2025
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.
