A reliability-aware fuzzy multi-agent framework for strategic decision support under uncertainty

In modern business environments, strategic planning decisions are often made under uncertainty, incomplete information, and subjective expert evaluations. Traditional decision support systems mainly focus on the performance indicators of alternatives and generally do not explicitly incorporate the reliability of expert assessments as a dynamic component of the decision process. As a result, the stability, robustness, and explainability of strategic decisions may decrease in uncertain environments. This paper proposes a reliability-aware fuzzy multi-agent framework for strategic decision support under uncertainty. In the proposed architecture, local agents representing risk, finance, resource availability, sustainability, and strategic compliance are modeled as independent fuzzy inference systems. Their outputs are aggregated by a coordinator agent through a reliability-weighted utility function. The proposed framework is formalized within a three-dimensional decision space (strategy–performance–reliability) and incorporates an adaptive feedback-based reliability updating mechanism that enables the system to learn from observed outcomes over time. In addition, the proposed approach integrates fuzzy inference, distributed multi-agent evaluation, and online reliability adaptation within a unified decision-support architecture. The functionality of the model is demonstrated through a numerical simulation scenario reflecting a strategic planning problem under uncertainty. The obtained results indicate that the proposed framework provides more adaptive, stable, and explainable decision behavior compared to traditional static decision-making approaches.