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Personality engineering with AI agents: A new methodology for negotiation research

arXiv 2605.20554 ↗First posted 19 May 2026cs.AI cs.HC cs.SIcited by 0

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Abstract (arXiv)

According to canonical negotiation theory, people's success in a negotiation depends on how well they balance competing demands--empathizing and asserting, demonstrating concern for other and concern for self, being soft on the people and hard on the problem. Yet people struggle to manage these tensions, so researchers have lacked the ability to rigorously test the field's prescriptions under controlled conditions. AI agents do not face the same limitations, and their precision, repertoire, consistency, and scalability enable a new class of experiments to contribute to negotiation theory. In this article, we introduce personality engineering--a methodology that precisely parameterizes the personas of AI agents based on established personality frameworks--and propose how this method can be used to study the traits and behaviors of human negotiators. Given its relevance to negotiation theory, we propose the interpersonal circumplex--and its two core dimensions of warmth and dominance--as a foundational coordinate system for the field. This approach offers both a rigorous methodology for testing classic negotiation theories and a practical guide for designing the personas of AI negotiation agents.

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References · 7

  1. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
  2. 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 122
  3. 2025LLM Social Simulations Are a Promising Research Methodcited by 97
  4. 2026This human study did not involve human subjects: Validating LLM simulations as behavioral evidencecited by 26
  5. 2025Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargainingcited by 11
  6. 2025Advancing AI Negotiations: A Large-Scale Autonomous Negotiation Competitioncited by 6
  7. 2025SAC: A Framework for Measuring and Inducing Personality Traits in LLMs with Dynamic Intensity Controlcited by 2

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