# Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining

arXiv: https://arxiv.org/abs/2509.09071 · HTML: https://mlsanity.com/paper/2509.09071/

## Key facts

- Cited by 11 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 21 of its 90 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## 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, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Who Should Predict? Exact Algorithms For Learning to Defer to Humans](https://mlsanity.com/paper/2301.06197/) (2023)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method](https://mlsanity.com/paper/2402.15813/) (2024)
- [Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models](https://mlsanity.com/paper/2405.10431/) (2024)
- [The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2311.00168/) (2023)
- [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019)
- [STEER: Assessing the Economic Rationality of Large Language Models](https://mlsanity.com/paper/2402.09552/) (2024)
- [Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function](https://mlsanity.com/paper/2406.01382/) (2024)
- [Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration](https://mlsanity.com/paper/2402.18498/) (2024)
- [The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making](https://mlsanity.com/paper/2410.07304/) (2024)
- [Optimizing Large Language Model Hyperparameters for Code Generation](https://mlsanity.com/paper/2408.10577/) (2024)
- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [Prompt Adaptation as a Dynamic Complement in Generative AI Systems](https://mlsanity.com/paper/2407.14333/) (2024)
- [Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot](https://mlsanity.com/paper/2202.04013/) (2022)

## Built on by (most cited first, 11 of 11)

- [To Mask or to Mirror: Human-AI Alignment in Collective Reasoning](https://mlsanity.com/paper/2510.01924/) (2025)
- [Discovering Differences in Strategic Behavior Between Humans and LLMs](https://mlsanity.com/paper/2602.10324/) (2026)
- [Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs](https://mlsanity.com/paper/2603.10476/) (2026)
- [Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation](https://mlsanity.com/paper/2602.12089/) (2026)
- [Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation](https://mlsanity.com/paper/2605.01750/) (2026)
- [Commitment To Cooperation With Self-Negotiated Contracts](https://mlsanity.com/paper/2607.22750/) (2026)
- [Personality engineering with AI agents: A new methodology for negotiation research](https://mlsanity.com/paper/2605.20554/) (2026)
- [Deal Me Maybe: The Role of Emotions in Multi-Agent Negotiation](https://mlsanity.com/paper/2608.06922/) (2026)
- [Evaluating Rational Contracting in Natural Language](https://mlsanity.com/paper/2608.10475/) (2026)
- [Modeling Human Behavior with Type Vectors Using AI](https://mlsanity.com/paper/2608.18265/) (2026)
- [Fair Like Us? Auditing LLM Alignment in Resource Allocation](https://mlsanity.com/paper/2609.29692/) (2026)

## Abstract (arXiv)

Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents behave relative to their human and task-specific statistical predecessors. In this work, we present results from an empirical study comparing humans (N=216), multiple frontier LLMs, and customized Bayesian agents in dynamic multi-player bargaining games under identical conditions. Bayesian agents extract the highest surplus with aggressive trade proposals that are frequently rejected. Humans and LLMs achieve comparable aggregate surplus within their groups, but exhibit different trading strategies. LLMs favor conservative, concessionary proposals that are usually accepted by other LLMs, while humans propose trades that are consistent with fairness norms but are more likely to be rejected. These findings highlight that performance parity -- a common benchmark in agent evaluation -- can mask substantive procedural differences in how LLMs behave in complex multi-agent interactions.