Game-theory behaviour of large language models: The case of Keynesian beauty contests

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 vary...

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Published in:Economics and business review Vol. 11; no. 2; pp. 119 - 148
Main Author: Estee Lu, Siting
Format: Journal Article
Language:English
Published: Poznan Poznań University of Economics and Business Press 01.06.2025
Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu
Sciendo
Poznan University of Economics
Subjects:
Algorithms
Beauty contests
Behavior
C63
C70
C90
Competition
Decision making
economic games
Experiments
Game theory
Human subjects
ICT Information and Communications Technologies
Keynesian theory
Large language models
Simulation
Social research
Social sciences
Socio-Economic Research
strategic interactions
strategic interactions
economic games
large language models
ISSN:2392-1641, 2450-0097
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Abstract 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.
AbstractList The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. This paper examines strategic interactions among multiple types of LLM-based agents in a classical beauty contest game. LLM-based agents demonstrate varying depth of reasoning that fall within a range of level-0 to 1, which are lower than experimental results conducted with human subjects in previous studies. However, they do display a similar convergence pattern towards Nash Equilibrium choice in repeated settings. Through simulations that vary the group composition of agent types, I found that environments with a lower strategic uncertainty enhance convergence for LLM-based agents, and environments with mixed strategic types accelerate convergence for all. Results with simulated agents not only convey insights into potential human behaviours in competitive settings, but also prove valuable for understanding strategic interactions among algorithms.
The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. This paper examines strategic interactions among multiple types of LLM-based agentsin a classical beauty contest game. LLM-based agents demonstrate varying depth of reasoning that fall within a range of level-0 to 1, which are lower than experimental results conducted with human subjects in previous studies. However, they do display a similar convergence pattern towards Nash Equilibrium choice in repeated settings. Through simulations that vary the group composition of agent types, I found that environments with a lower strategic uncertainty enhance convergence for LLM-based agents, and environments with mixed strategic types accelerate convergence for all. Results with simulated agents not only convey insights into potential human behaviours in competitive settings, but also prove valuable for understanding strategic interactions among algorithms.
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.
The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. This paper examines strategic interactions among multiple types of LLM-based agents in a classical beauty contest game. LLM-based agents demonstrate varying depth of reasoning that fall within a range of level-0 to 1, which are lower than experimental results conducted with human subjects in previous literature, but they display similar convergence pattern towards Nash Equilibrium choice in repeated setting. Through simulations that varies the group composition of agent types, I found that environment with lower strategic uncertainty enhances convergence for LLM-based agents, and environments with mixed strategic types accelerate convergence for all. Results with simulated agents not only convey insights on potential human behaviours in competitive setting, they also offer valuable understanding of strategic interactions among algorithms.
Author Estee Lu, Siting
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Issue 2
Keywords strategic interactions
economic games
large language models
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Snippet The growing adoption of large language models (LLMs) pre-sents potential for deeper understanding of human behav-iours within game theory frameworks. This...
The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. This paper...
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SubjectTerms Algorithms
Beauty contests
Behavior
C63
C70
C90
Competition
Decision making
economic games
Experiments
Game theory
Human subjects
ICT Information and Communications Technologies
Keynesian theory
Large language models
Simulation
Social research
Social sciences
Socio-Economic Research
strategic interactions
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