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

Ehsan Hatamian ()
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Ehsan Hatamian: University of Economics-Varna, Bulgaria

Business & Management Compass, 2026, issue 1, 22-40

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, proposing 24performance 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 multi-dimensional performance assessment across technical accuracy, strategic effectiveness, and business impact dimensions.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 through authentic procurement data grounding. Originality/Value:Acomprehensive 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 hybrid validation methodology establishes methodological foundations for rigorous AI agent evaluation in strategic business contexts.

Keywords: Multi-agent systems; Large language models; Econometric evaluation; Negotiation intelligence; Strategic advisor agents; Causal inference; Performance measurement; Agentic AI systems (search for similar items in EconPapers)
JEL-codes: C38 C45 C52 C63 M11 (search for similar items in EconPapers)
Date: 2026
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