Automated Mediator for Human Negotiation: Pre-Mediation via a Structured LLM Pipeline

2026-06-09

AI Summary

This study addresses the frequent omission of pre-mediation—a critical yet resource-intensive phase often hindered by high costs, lengthy durations, and a scarcity of skilled mediators—which undermines the effectiveness of multiparty negotiations in reaching mutually beneficial agreements. To overcome these challenges, the authors propose a modular large language model (LLM) pipeline architecture that decomposes pre-mediation into specialized agents for dialogue understanding, preference prediction, response critique, and structured summarization. These agents operate sequentially to handle unilateral preparation tasks, explicitly separating reasoning, generation, and evaluation functions to circumvent the limitations of end-to-end prompting. Experimental results demonstrate that the system matches human mediators in self-reported trust and confidence in agreements, reduces preference inference error by 36%, and—after prompt optimization—lowers over-affirmative behavior from 36.6% to 16.8%, aligning with human baseline performance.

Abstract

Pre-mediation, the preparatory phase preceding direct human negotiation, plays a critical role in achieving mutually beneficial agreements, yet is often omitted due to cost, time, and limited access to trained mediators. We introduce an automated mediator for human negotiation, implemented as a structured pipeline of LLM modules, that supports pre-mediation in integrative negotiation settings. The pipeline decomposes preparation into specialized modules for dialogue, preference prediction, response-level critique, and structured summarization, separating inference, generation, and evaluation to address limitations of monolithic single-prompt approaches. We use the term "agent" for each module following common LLM-systems terminology, but the components are not autonomous and do not interact peer-to-peer; outputs are passed forward in a fixed sequence. We evaluate the system in two controlled human-subject experiments comparing AI-based pre-mediation with professional human mediators in a multi-issue negotiation scenario. On short-term self-reported measures, the automated mediator achieves preparation outcomes broadly comparable to human mediators, including trust in the mediator and confidence in reaching mutually beneficial agreements, while achieving substantially lower error on the preference-inference task under our scenario and prompts (36% lower RMSE). A second study shows that targeted prompt refinements reduce excessive affirmation patterns from 36.6% to 16.8%, matching human mediator baselines. Our findings suggest that structured LLM pipelines can provide scalable, low-effort pre-mediation support broadly comparable to human mediators on short-term self-reported preparation outcomes. The pipeline's single-party design mirrors how human mediators run pre-mediation today and enables parallel deployment across all parties to a dispute, supporting scalability.

Problem

• pre-mediation
• human negotiation
• automated mediator
• integrative negotiation
• LLM pipeline

Innovation

• structured LLM pipeline
• pre-mediation
• preference inference
• integrative negotiation
• automated mediation