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

• URL: http://arxiv.org/abs/2510.24909v1
• Date: Tue, 28 Oct 2025 19:26:14 GMT
• Title: Trust Dynamics in Strategic Coopetition: Computational Foundations for Requirements Engineering in Multi-Agent Systems
• Abstract summary: This report develops a computational trust model that extends game-theoretic foundations for strategic coopetition with dynamic trust evolution.<n>We develop a structured translation framework enabling engineers to instantiate computational trust models from i* dependency networks and organizational contexts.
• Score: 0.33985917934283577
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Accelerating Trust Convergence in IIoT: A ML Approach for Dynamic Network Conditions [5.9556977806856155]
  We propose a dynamic trust management solution, known as the Trust Convergence Acceleration (TCA) approach.<n>Our model predicts the number of time units needed for trust convergence based on key network metrics.<n>We demonstrate the effectiveness of the TCA-based approach, achieving up to a 28.6% reduction in trust convergence time under challenging conditions.
  arXiv  Detail & Related papers  (2026-06-18T13:29:06Z)
• Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks [3.7802766761560442]
  Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices.<n> Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence of faulty or adversarial updates.<n>This paper presents a lightweight agentic trust coordination approach for FL in sustainable and resilient industrial networks.
  arXiv  Detail & Related papers  (2026-03-26T11:21:22Z)
• ONTrust: A Reference Ontology of Trust [1.068745732421904]
  This paper is the culmination of a long-term research program of providing a solid ontological foundation on trust.<n>A Reference Ontology of Trust (ONTrust) was developed, grounded on the Unified Foundational Ontology and specified in OntoUML.<n>ONTrust formally characterizes the concept of trust and its different types, describes the different factors that can influence trust, as well as explains how risk emerges from trust relations.
  arXiv  Detail & Related papers  (2026-02-07T18:47:34Z)
• Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems [94.9141394384021]
  Individual agents in multi-agent systems often lack robustness, tending to blindly conform to misleading peers.<n>We show this weakness stems from both sycophancy and inadequate ability to evaluate peer reliability.<n>We first formalize the learning problem of history-aware reference, introducing the historical interactions of peers as additional input.<n>We then develop Epistemic Context Learning (ECL), a reasoning framework that conditions predictions on explicitly-built peer profiles from history.
  arXiv  Detail & Related papers  (2026-01-29T13:59:32Z)
• PaTAS: A Parallel System for Trust Propagation in Neural Networks Using Subjective Logic [1.8485970721272895]
  This paper introduces the Parallel Trust Assessment System (PaTAS), a framework for modeling and propagating trust in neural networks.<n>Experiments on real-world and adversarial datasets demonstrate that PaTAS produces interpretable, symmetric, and convergent trust estimates.
  arXiv  Detail & Related papers  (2025-11-25T18:15:36Z)
• Computational Foundations for Strategic Coopetition: Formalizing Interdependence and Complementarity [0.33985917934283577]
  This report develops computational foundations that formalize two critical dimensions of coopetition: interdependence and complementarity.<n>We ground interdependence in i* structural dependency analysis, translating depender-dependee-dependum relationships into quantitative interdependence coefficients through a structured translation framework.<n>We formalize complementarity following Brandenburger and Nalebuff's Added Value concept, modeling synergistic value creation with validated parameterization.<n>We integrate structural dependencies with bargaining power in value appropriation and introduce a game-theoretic formulation where Nash Equilibrium incorporates structural interdependence.
  arXiv  Detail & Related papers  (2025-10-21T16:57:40Z)
• The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability [7.452202188661883]
  We introduce and empirically validate the Trust-Vulnerability Paradox (TVP)<n>TVP: increasing inter-agent trust to enhance coordination simultaneously expands risks of over-exposure and over-authorization.<n>This study formalizes TVP, establishes reproducible baselines with unified metrics, and demonstrates that trust must be modeled and scheduled as a first-class security variable in multi-agent system design.
  arXiv  Detail & Related papers  (2025-10-21T12:18:39Z)
• A biologically Inspired Trust Model for Open Multi-Agent Systems that is Resilient to Rapid Performance Fluctuations [0.0]
  Existing trust models face challenges related to agent mobility, changing behaviors, and the cold start problem.<n>We introduce a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally.<n>This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy.
  arXiv  Detail & Related papers  (2025-04-17T08:21:54Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [377.2483044466149]
  Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
  arXiv  Detail & Related papers  (2025-02-20T06:20:36Z)
• Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms [0.0]
  In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor. We show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.
  arXiv  Detail & Related papers  (2024-04-28T19:44:56Z)
• A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes [0.0]
  The study is based on CA, a proposed decentralized computational trust model inspired by synaptic plasticity and the formation of assemblies in the human brain. We ran a series of simulations to compare CA model to FIRE, a well-established, decentralized trust and reputation model for open MAS. The main finding is that FIRE is superior to changes in the trustee population, whereas CA is resilient to the trustor population changes.
  arXiv  Detail & Related papers  (2024-04-13T10:56:32Z)
• TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support [59.41529066449414]
  We propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity. TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Experiments show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot.
  arXiv  Detail & Related papers  (2023-06-23T07:39:12Z)
• KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks [63.531790269009704]
  Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things) Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
  arXiv  Detail & Related papers  (2023-02-22T14:24:45Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.