# Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making

arXiv: https://arxiv.org/abs/2506.09390 · HTML: https://mlsanity.com/paper/2506.09390/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing](https://mlsanity.com/paper/2309.05898/) (2023)
- [Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method](https://mlsanity.com/paper/2402.15813/) (2024)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory](https://mlsanity.com/paper/2502.20432/) (2025)
- [Negotiating with LLMS: Prompt Hacks, Skill Gaps, and Reasoning Deficits](https://mlsanity.com/paper/2312.03720/) (2023)
- [Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game](https://mlsanity.com/paper/2501.14225/) (2025)
- [Large Language Models Playing Mixed Strategy Nash Equilibrium Games](https://mlsanity.com/paper/2406.10574/) (2024)
- [Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values](https://mlsanity.com/paper/2502.00313/) (2025)
- [Inferring Strategies from Observations in Long Iterated Prisoner's Dilemma Experiments](https://mlsanity.com/paper/2202.04171/) (2022)
- [Large Language Models are Biased Reinforcement Learners](https://mlsanity.com/paper/2405.11422/) (2024)
- [Social cycling and conditional responses in the Rock-Paper-Scissors game](https://mlsanity.com/paper/1404.5199/) (2014)

## Built on by (most cited first, 9 of 9)

- [The Emergence of Altruism in Large-Language-Model Agents Society](https://mlsanity.com/paper/2509.22537/) (2025)
- [GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare](https://mlsanity.com/paper/2510.08872/) (2025)
- [The Fragility of Strategic Thinking in Large Language Models](https://mlsanity.com/paper/2510.10813/) (2025)
- [Communication Enhances LLMs' Stability in Strategic Thinking](https://mlsanity.com/paper/2602.06081/) (2026)
- [Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation](https://mlsanity.com/paper/2604.11840/) (2026)
- [Evaluating Large Language Models in a Complex Hidden Role Game](https://mlsanity.com/paper/2605.22826/) (2026)
- [Language Model Agents Under Attack: A Cross Model-Benchmark of Profit-Seeking Behaviors in Customer Service](https://mlsanity.com/paper/2512.24415/) (2025)
- [When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems](https://mlsanity.com/paper/2601.10102/) (2026)
- [Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races](https://mlsanity.com/paper/2608.01193/) (2026)

## Abstract (arXiv)

Large language models are increasingly used in strategic decision-making settings, yet evidence shows that, like humans, they often deviate from full rationality. In this study, we compare LLMs and humans using experimental paradigms directly adapted from behavioral game-theory research. We focus on two well-studied strategic games, Rock-Paper-Scissors and the Prisoner's Dilemma, which are well known for revealing systematic departures from rational play in human subjects. By placing LLMs in identical experimental conditions, we evaluate whether their behaviors exhibit the bounded rationality characteristic of humans. Our findings show that LLMs reproduce familiar human heuristics, such as outcome-based strategy switching and increased cooperation when future interaction is possible, but they apply these rules more rigidly and demonstrate weaker sensitivity to the dynamic changes in the game environment. Model-level analyses reveal distinctive architectural signatures in strategic behavior, and even reasoning models sometimes struggle to find effective strategies in adaptive situations. These results indicate that current LLMs capture only a partial form of human-like bounded rationality and highlight the need for training methods that encourage flexible opponent modeling and stronger context awareness.