Panel Leader: Jonathan Gratch (USC)
Jonathan Gratch, University of Southern California
A Brief History of AI Negotiators
With the recent rise of language models like GPT, there has been an explosion of interest in AI as a negotiator. Yet negotiation has been a major focus of AI research since the early 1990s. The field has developed sophisticated techniques to address key challenges in deploying AI negotiators in real-world settings, including aligning AI with user goals, developing provably efficient solutions, incorporating human factors such as emotion and trust, and highlighting ethical concerns when organizations delegate negotiations to AI. These challenges and solutions are particularly relevant for this new generation of language-based approaches. I will briefly review these key challenges and solutions and thereby set the stage for the other panel speakers.
Michelle Vaccaro, Massachusetts Institute of Technology
Smooth-Talking Bots: AI Negotiators Make Better Impressions
This study examines how large language model (LLM) chatbots perform in negotiations compared to humans and explores whether manipulating chatbot “personality” (warmth and dominance) affects negotiation outcomes. Building on theories of algorithmic aversion and algorithmic appreciation, we conducted an integrative experiment in which human participants negotiated with human, chatbot, or Wizard-of-Oz (human-controlled) counterparts. Our design spanned four “spaces”: (1) actor (human or chatbot), (2) personality (systematically varied warmth-dominance combinations), (3) task (distributive or integrative bargaining), and (4) role (e.g., buyer vs. seller). Preliminary results suggest that unprompted chatbots outperform human negotiators in subjective value (i.e., counterpart satisfaction) but underperform on objective outcomes in both distributive and integrative contexts. Additionally, prompting chatbots to exhibit warmth increases subjective value at the expense of objective outcomes, whereas prompting them to display dominance strengthens objective outcomes but reduces subjective value. However, when warmth and dominance are combined, chatbots achieve both higher subjective and objective outcomes than unprompted chatbots. By demonstrating that AI negotiators can improve user experience without compromising deal terms, this research offers practical implications for organizations considering the use of LLM-enabled negotiation systems. Future work will involve larger samples to deepen our understanding of how chatbot personality interacts with individual human differences—such as trust in AI and cultural norms—to shape negotiation dynamics and outcomes.
Mike Lewis, Meta AI Research
What We Learned from Teaching AI to Negotiate
Negotiation has long been an interesting problem to AI researchers, as it relies on building agents that can strategically use language to achieve their own goals, and research on the problem has motivated the development of new techniques. I will describe the first AI system trained end-to-end to negotiate, featuring early uses of planning and end-to-end reinforcement learning to optimize dialogue systems. I will also discuss how we designed an agent game of Diplomacy, in which players must establish trust and coordinate in a competitive environment. Our Cicero bot uses an interface between a large language model and a symbolic planner to perform at the level of strong human players.
Min Kyung Lee, University of Texas at Austin
Opportunities and Challenges in AI Mediators
As AI systems become increasingly embedded in decision-making and social processes, they are taking on the role of mediators—moderating group interactions, facilitating knowledge exchange, and representing individuals in collective decision-making. In this talk, I will explore the opportunities and challenges of AI mediators, drawing from past and ongoing case studies in resource allocation, knowledge sharing, and deliberation.