LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory

TL;DR

This paper evaluates large language models' strategic reasoning using behavioral game theory, revealing that model size alone doesn't determine performance and highlighting biases and the nuanced effects of prompting methods.

Contribution

Introduces a behavioral game theory framework for assessing LLM strategic reasoning, analyzing the effects of prompting and demographic biases on decision-making.

Findings

Certain models outperform others regardless of size

Chain-of-Thought prompting has limited universal benefits

Demographic features influence model decision patterns

Abstract

Strategic decision-making involves interactive reasoning where agents adapt their choices in response to others, yet existing evaluations of large language models (LLMs) often emphasize Nash Equilibrium (NE) approximation, overlooking the mechanisms driving their strategic choices. To bridge this gap, we introduce an evaluation framework grounded in behavioral game theory, disentangling reasoning capability from contextual effects. Testing 22 state-of-the-art LLMs, we find that GPT-o3-mini, GPT-o1, and DeepSeek-R1 dominate most games yet also demonstrate that the model scale alone does not determine performance. In terms of prompting enhancement, Chain-of-Thought (CoT) prompting is not universally effective, as it increases strategic reasoning only for models at certain levels while providing limited gains elsewhere. Additionally, we investigate the impact of encoded demographic features on the models, observing that certain assignments impact the decision-making pattern. For instance, GPT-4o shows stronger strategic reasoning with female traits than males, while Gemma assigns higher reasoning levels to heterosexual identities compared to other sexual orientations, indicating inherent biases. These findings underscore the need for ethical standards and contextual alignment to balance improved reasoning with fairness.