# LLMs with Personalities in Multi-issue Negotiation Games

arXiv: https://arxiv.org/abs/2405.05248 · HTML: https://mlsanity.com/paper/2405.05248/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 90 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations](https://mlsanity.com/paper/2402.12348/) (2024)
- [AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction](https://mlsanity.com/paper/2305.09620/) (2023)
- [Evaluating Language Model Agency through Negotiations](https://mlsanity.com/paper/2401.04536/) (2024)
- [A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?](https://mlsanity.com/paper/2312.00798/) (2023)
- [NPCs as People, Too: The Extreme AI Personality Engine](https://mlsanity.com/paper/1609.04879/) (2016)

## Built on by (most cited first, 22 of 22)

- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy](https://mlsanity.com/paper/2407.06813/) (2024)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [GLEE: A Unified Framework and Benchmark for Language-based Economic Environments](https://mlsanity.com/paper/2410.05254/) (2024)
- [MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions](https://mlsanity.com/paper/2502.16796/) (2025)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications](https://mlsanity.com/paper/2505.00049/) (2025)
- [Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?](https://mlsanity.com/paper/2412.16772/) (2024)
- [Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans](https://mlsanity.com/paper/2509.16394/) (2025)
- [We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism](https://mlsanity.com/paper/2509.11118/) (2025)
- [Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents](https://mlsanity.com/paper/2602.01063/) (2026)
- [Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas](https://mlsanity.com/paper/2410.03181/) (2024)
- [Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy](https://mlsanity.com/paper/2512.18292/) (2025)
- [A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies](https://mlsanity.com/paper/2409.18335/) (2024)
- [Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering](https://mlsanity.com/paper/2511.04499/) (2025)
- [Measuring Social Influence with Networked Synthetic Control](https://mlsanity.com/paper/2505.13334/) (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)
- [How Far Can LLMs Emulate Human Behavior?: A Strategic Analysis via the Buy-and-Sell Negotiation Game](https://mlsanity.com/paper/2511.17990/) (2025)
- [Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems](https://mlsanity.com/paper/2604.15078/) (2026)
- [Personality Anchoring for Social Simulation: Linking Personality, Social Behavior, and Interaction Success with LLM Agents](https://mlsanity.com/paper/2606.06936/) (2026)
- [Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation](https://mlsanity.com/paper/2608.13712/) (2026)
- [Belief Cascades Drive Persuasion in LLM Agent Networks](https://mlsanity.com/paper/2608.25152/) (2026)

## Abstract (arXiv)

Powered by large language models (LLMs), AI agents have become capable of many human tasks. Using the most canonical definitions of the Big Five personality, we measure the ability of LLMs to negotiate within a game-theoretical framework, as well as methodological challenges to measuring notions of fairness and risk. Simulations (n=1,500) for both single-issue and multi-issue negotiation reveal increase in domain complexity with asymmetric issue valuations improve agreement rates but decrease surplus from aggressive negotiation. Through gradient-boosted regression and Shapley explainers, we find high openness, conscientiousness, and neuroticism are associated with fair tendencies; low agreeableness and low openness are associated with rational tendencies. Low conscientiousness is associated with high toxicity. These results indicate that LLMs may have built-in guardrails that default to fair behavior, but can be "jail broken" to exploit agreeable opponents. We also offer pragmatic insight in how negotiation bots can be designed, and a framework of assessing negotiation behavior based on game theory and computational social science.