LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory

• URL: http://arxiv.org/abs/2502.20432v3
• Date: Sat, 01 Nov 2025 22:42:55 GMT
• Title: LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory
• Authors: Jingru Jia, Zehua Yuan, Junhao Pan, Paul E. McNamara, Deming Chen,
• Abstract summary: We introduce an evaluation framework grounded in behavioral game theory, disentangling reasoning capability from contextual effects.<n>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.<n>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.
• Score: 7.8900549152197215
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance [86.46794021499511]
  We show a previously underexplored gap between strategy usage and strategy executability.<n>We propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability.<n> SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance.
  arXiv  Detail & Related papers  (2026-02-26T03:34:23Z)
• How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns [51.02752099869218]
  Large Language Models (LLMs) display strikingly different generalization behaviors.<n>We introduce a novel benchmark that decomposes reasoning into atomic core skills.<n>We show that RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns.
  arXiv  Detail & Related papers  (2025-12-30T08:16:20Z)
• Observer, Not Player: Simulating Theory of Mind in LLMs through Game Observation [0.0]
  We present an interactive framework for evaluating whether large language models (LLMs) exhibit genuine "understanding"<n>We focus on Rock-Paper-Scissors (RPS), which, despite its apparent simplicity, requires sequential reasoning, adaptation, and strategy recognition.<n>Our framework captures not only predictive accuracy but also whether the model can stably identify the latent strategies in play.
  arXiv  Detail & Related papers  (2025-12-22T09:49:13Z)
• From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs [58.02809208460186]
  We revisit this paradox using high-quality traces from DeepSeek-R1 as demonstrations.<n>We find that adding more exemplars consistently degrades accuracy, even when demonstrations are optimal.<n>We introduce Insight-to-solve (I2S), a sequential test-time procedure that turns demonstrations into explicit, reusable insights.
  arXiv  Detail & Related papers  (2025-09-27T08:59:31Z)
• CHBench: A Cognitive Hierarchy Benchmark for Evaluating Strategic Reasoning Capability of LLMs [10.29314561183905]
  Game-playing ability serves as an indicator for evaluating the strategic reasoning capability of large language models.<n>We propose CHBench, a novel evaluation framework inspired by the cognitive hierarchy models from behavioral economics.
  arXiv  Detail & Related papers  (2025-08-16T07:10:26Z)
• NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks [65.70224757972068]
  We select reasoning traces from a strong teacher model based on a large pool of questions from NaturalReasoning.<n>We find that simply scaling up data size with random sampling is a strong baseline with steady performance gains.<n>We find that selecting difficult examples that require more diverse reasoning strategies is more sample-efficient to transfer the teacher model's reasoning skills.
  arXiv  Detail & Related papers  (2025-07-02T17:30:24Z)
• Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making [33.2843381902912]
  Large language models are increasingly used in strategic decision-making settings.<n>We compare LLMs and humans using experimental paradigms adapted from behavioral game-theory research.
  arXiv  Detail & Related papers  (2025-06-11T04:43:54Z)
• The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think [81.38614558541772]
  We introduce the CoT Encyclopedia, a framework for analyzing and steering model reasoning.<n>Our method automatically extracts diverse reasoning criteria from model-generated CoTs.<n>We show that this framework produces more interpretable and comprehensive analyses than existing methods.
  arXiv  Detail & Related papers  (2025-05-15T11:31:02Z)
• Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning [12.559028963968247]
  We investigate the crucial relationship between a model's reasoning ability and fairness. We find that larger models with stronger reasoning abilities exhibit substantially lower stereotypical bias. We introduce ReGiFT, a novel approach that extracts structured reasoning traces from advanced reasoning models and infuses them into models that lack such capabilities.
  arXiv  Detail & Related papers  (2025-04-08T03:21:51Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [58.130894823145205]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning. We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• The Double-Edged Sword of Behavioral Responses in Strategic Classification: Theory and User Studies [7.695481260089599]
  We propose a strategic classification model that considers behavioral biases in human responses to algorithms. We show how misperceptions of a classifier can lead to different types of discrepancies between biased and rational agents' responses. We show that strategic agents with behavioral biases can benefit or (perhaps, unexpectedly) harm the firm compared to fully rational strategic agents.
  arXiv  Detail & Related papers  (2024-10-23T17:42:54Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents [4.209869303518743]
  We introduce GameBench, a cross-domain benchmark for evaluating strategic reasoning abilities of large language models. Our evaluations use GPT-3 and GPT-4 in their base form along with two scaffolding frameworks designed to enhance strategic reasoning ability: Chain-of-Thought (CoT) prompting and Reasoning Via Planning (RAP) Our results show that none of the tested models match human performance, and at worst GPT-4 performs worse than random action.
  arXiv  Detail & Related papers  (2024-06-07T00:28:43Z)
• Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning [25.732397636695882]
  We show that large language models (LLMs) display reasoning patterns akin to those observed in humans. Our research demonstrates that the architecture and scale of the model significantly affect its preferred method of reasoning.
  arXiv  Detail & Related papers  (2024-02-20T12:58:14Z)
• Aligning Large Language Models by On-Policy Self-Judgment [49.31895979525054]
  Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. We present a novel alignment framework, SELF-JUDGE, that does on-policy learning and is parameter efficient. We show that the rejecting sampling by itself can improve performance further without an additional evaluator.
  arXiv  Detail & Related papers  (2024-02-17T11:25:26Z)
• K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning [76.3114831562989]
  It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. We propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)"
  arXiv  Detail & Related papers  (2024-02-02T16:07:05Z)
• MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
  We introduce a novel evaluation paradigm for Large Language Models (LLMs) This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
  arXiv  Detail & Related papers  (2023-12-28T15:49:43Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• HANS, are you clever? Clever Hans Effect Analysis of Neural Systems [1.6267479602370545]
  Large Language Models (It-LLMs) have been exhibiting outstanding abilities to reason around cognitive states, intentions, and reactions of all people involved, letting humans guide and comprehend day-to-day social interactions effectively. Several multiple-choice questions (MCQ) benchmarks have been proposed to construct solid assessments of the models' abilities. However, earlier works are demonstrating the presence of inherent "order bias" in It-LLMs, posing challenges to the appropriate evaluation.
  arXiv  Detail & Related papers  (2023-09-21T20:52:18Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.