Game-theory behaviour of large language models: The case of Keynesian beauty contests - Estee Lu, Siting (Economics and business review, 2025)
The growing adoption of large language models (LLMs) pre-sents potential for deeper understanding of human behav-iours within game theory frameworks. This paper examinesstrategic interactions among multiple types of LLM-basedagents in a classical beauty contest game. LLM-based agentsdemonstrate varying depth of reasoning that fall withina range of level-0 to 1, which are lower than experimentalresults conducted with human subjects in previous studies.However, they do display a similar convergence pattern to-wards Nash Equilibrium choice in repeated settings. Throughsimulations that vary the group composition of agent types,I found that environments with a lower strategic uncertaintyenhance convergence for LLM-based agents, and environ-ments with mixed strategic types accelerate convergencefor all. Results with simulated agents not only convey in-sights into potential human behaviours in competitive set-tings, but also prove valuable for understanding strategicinteractions among algorithms.