Trust Dynamics in Strategic Coopetition: Computational Foundations for Requirements Engineering in Multi-Agent Systems

2025-10-28

AI Summary

Existing conceptual models (e.g., i*) lack behaviorally grounded computational mechanisms for modeling trust dynamics in multi-stakeholder requirement engineering, while agent-based trust models are decoupled from requirements engineering contexts. Method: We propose a two-layer computable trust model integrating game theory and multi-agent systems, distinguishing immediate trust from reputation, and incorporating asymmetric updating, lag effects, and trust ceilings. It formally captures negativity bias amplification and relational recovery constraints. We further develop a structured transformation framework from i* dependency networks to executable models. Results: The model demonstrates robustness across 78,125 parameter configurations. Applied to the Renault-Nissan alliance (1999–2025), it successfully reproduces its five-stage evolution, achieving 49 out of 60 validation points (81.7%). This significantly enhances the analyzability and predictability of trust evolution in requirements negotiation.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Requirements engineering increasingly occurs in multi-stakeholder environments where organizations simultaneously cooperate and compete, creating coopetitive relationships in which trust evolves dynamically based on observed behavior over repeated interactions. While conceptual modeling languages like i* represent trust relationships qualitatively, they lack computational mechanisms for analyzing how trust changes with behavioral evidence. Conversely, computational trust models from multi-agent systems provide algorithmic updating but lack grounding in requirements engineering contexts and conceptual models. This technical report bridges this gap by developing a computational trust model that extends game-theoretic foundations for strategic coopetition with dynamic trust evolution. We introduce trust as a two-layer system with immediate trust responding to current behavior and reputation tracking violation history. Trust evolves through asymmetric updating where cooperation builds trust gradually while violations erode it sharply, creating hysteresis effects and trust ceilings that constrain relationship recovery. We develop a structured translation framework enabling requirements engineers to instantiate computational trust models from i* dependency networks and organizational contexts. Comprehensive experimental validation across 78,125 parameter configurations establishes robust emergence of negativity bias, hysteresis effects, and cumulative damage amplification. Empirical validation using the Renault-Nissan Alliance case study (1999-2025) achieves 49 out of 60 validation points (81.7%), successfully reproducing documented trust evolution across five distinct relationship phases including crisis and recovery periods. This technical report builds upon its foundational companion work in arXiv:2510.18802.

Problem

• Modeling dynamic trust evolution in strategic coopetition environments
• Bridging conceptual trust models with computational trust mechanisms
• Translating requirements engineering models into computational trust frameworks

Innovation

• Dynamic trust model with two-layer system
• Asymmetric updating for cooperation and violations
• Translation framework from i* to computational models