# Simulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs

arXiv: https://arxiv.org/abs/2402.08189 · HTML: https://mlsanity.com/paper/2402.08189/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation](https://mlsanity.com/paper/2007.04954/) (2020)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2304.09797/) (2023)
- [MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming](https://mlsanity.com/paper/2310.06500/) (2023)
- [Strategic Reasoning with Language Models](https://mlsanity.com/paper/2305.19165/) (2023)
- [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [Blind Judgement: Agent-Based Supreme Court Modelling With GPT](https://mlsanity.com/paper/2301.05327/) (2023)
- [Route Packing: Geospatially-Accurate Visualization of Route Networks](https://mlsanity.com/paper/1909.10173/) (2019)

## Built on by (most cited first, 21 of 21)

- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [Werewolf Arena: A Case Study in LLM Evaluation via Social Deduction](https://mlsanity.com/paper/2407.13943/) (2024)
- [MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks](https://mlsanity.com/paper/2505.12371/) (2025)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis](https://mlsanity.com/paper/2404.15532/) (2024)
- [Very Large-Scale Multi-Agent Simulation in AgentScope](https://mlsanity.com/paper/2407.17789/) (2024)
- [Explaining Large Language Models Decisions Using Shapley Values](https://mlsanity.com/paper/2404.01332/) (2024)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games](https://mlsanity.com/paper/2410.21359/) (2024)
- [BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems](https://mlsanity.com/paper/2501.06314/) (2025)
- [Re-Thinking Process Mining in the AI-Based Agents Era](https://mlsanity.com/paper/2408.07720/) (2024)
- [Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction](https://mlsanity.com/paper/2412.02301/) (2024)
- [Do Persona-Infused LLMs Affect Performance in a Strategic Reasoning Game?](https://mlsanity.com/paper/2512.06867/) (2025)
- [MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications](https://mlsanity.com/paper/2506.19502/) (2025)
- [Structured Debate Improves Corporate Credit Reasoning in Financial AI](https://mlsanity.com/paper/2510.17108/) (2025)
- [Declarative Integration and Management of Large Language Models through Finite Automata: Application to Automation, Communication, and Ethics](https://mlsanity.com/paper/2409.13693/) (2024)
- [A Dynamic and High-Precision Method for Scenario-Based HRA Synthetic Data Collection in Multi-Agent Collaborative Environments Driven by LLMs](https://mlsanity.com/paper/2502.00022/) (2025)
- [Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games](https://mlsanity.com/paper/2505.24255/) (2025)
- [Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis](https://mlsanity.com/paper/2510.05335/) (2025)
- [Persona-based Multi-Agent Collaboration for Brainstorming](https://mlsanity.com/paper/2512.04488/) (2025)
- [Mapping the Emerging Social Science of Large Language Models](https://mlsanity.com/paper/2609.07598/) (2026)

## Abstract (arXiv)

When creating policies, plans, or designs for people, it is challenging for designers to foresee all of the ways in which people may reason and behave. Recently, Large Language Models (LLMs) have been shown to be able to simulate human reasoning. We extend this work by measuring LLMs ability to simulate strategic reasoning in the ultimatum game, a classic economics bargaining experiment. Experimental evidence shows human strategic reasoning is complex; people will often choose to punish other players to enforce social norms even at personal expense. We test if LLMs can replicate this behavior in simulation, comparing two structures: single LLMs and multi-agent systems. We compare their abilities to (1) simulate human-like reasoning in the ultimatum game, (2) simulate two player personalities, greedy and fair, and (3) create robust strategies that are logically complete and consistent with personality. Our evaluation shows that multi-agent systems are more accurate than single LLMs (88 percent vs. 50 percent) in simulating human reasoning and actions for personality pairs. Thus, there is potential to use LLMs to simulate human strategic reasoning to help decision and policy-makers perform preliminary explorations of how people behave in systems.