Econometric Foundations for Multi-Agent Negotiation Systems: A Theoretical Framework for LLM-Driven Strategic Advisor Evaluation

Abstract

Purpose: This study develops a comprehensive econometric framework for evaluating LLM-based multi-agent negotiation systems. The framework targets the StrategicAdvisorAgent—an LLM-powered component implementing six specialised methods: context analysis, strategy generation, proposal evaluation, concession recommendation, tactic detection, and recommendation synthesis. Design/Methodology/Approach: The theoretical framework extends classical econometric theory to accommodate AI agent characteristics and proposes 24 performance metrics across six methods. A hybrid data methodology combines real European procurement data (2,715 TED notices from 18 countries) with synthetic supplier responses and expert validation. Causal inference approaches (RCT, IV, RDD, DiD) are adapted for AI evaluation with appropriate statistical inference procedures. Findings: Traditional AI metrics inadequately capture strategic value creation in negotiation contexts. The proposed Extended Econometric Model, Y_it = f(X_it, A_it, H_{i,t-1}, S_t, θ_t) + α_i + λ_t + ε_it, accommodates dynamic learning behaviours, temporal dependencies, and emergent capabilities, enabling multidimensional performance assessment across technical accuracy, strategic effectiveness, and business impact. Research Implications: The framework provides production-ready econometric specifications for organisations deploying AI negotiation systems. The hybrid validation methodology addresses academic research constraints while maintaining ecological validity by grounding the study in authentic procurement data. Originality/Value: A comprehensive econometric framework specifically designed for LLM-based negotiation agents, bridging artificial intelligence, econometrics, and negotiation theory. The 24-metric specification, with method-specific performance measures, adapted causal identification strategies, and a hybrid validation methodology, establishes methodological foundations for rigorous evaluation of AI agents in strategic business contexts.