Grey Systems Theory: A Global Reappraisal and Interdisciplinary Applications for Decision-Making in an Uncertain World – A Scientometric Approach

Background: Grey Systems Theory (GST), introduced by Deng in 1982, is a foundational approach to modeling uncertainty and decision-making with incomplete data. GST views reality as a spectrum—neither fully known nor unknown—enabling its application across engineering, economics, environmental science, medicine, management, and public policy. Despite wide adoption, prior reviews remain fragmented, lacking comprehensive analysis of GST’s intellectual progress and current frontiers. This study addresses these gaps by providing an integrated, systematic scientometric analysis that clarifies GST’s development, structure, and emerging trends.

Purpose: This study delivers a dynamic, holistic mapping of GST’s intellectual development from 1982 to 2025. It traces GST’s evolving trajectory, reveals global collaboration networks, pinpoints key thematic clusters, and spotlights emerging domains of application.

Methodology: Driven by clear objectives, we adopted a comprehensive scientometric approach. We retrieved data from the Scopus database, analyzing it through the R package Bibliometrix to spotlight citation, co-authorship, and co-word networks. To ensure depth and reliability, our search query spanned diverse keywords: “grey systems,” “grey model,” “grey prediction,” and “grey theory.” Only peer-reviewed, finalized research articles qualified, guaranteeing academic rigor. With this methodology, we constructed thematic maps, identified leading authors and journals, and mapped international research collaborations.

Findings: The analysis reveals three distinct phases in the evolution of GST:

1. Conceptual Foundation (1980s–1990s): This phase was marked by the development of core constructs such as grey numbers and grey matrices.

2. Building on these foundations, the next phase—Algorithmic Expansion (2000–2015)—saw the proliferation of hybrid models and the integration of GST with fuzzy logic, evolutionary algorithms, and machine learning.

3. In recent years, this trajectory has given way to Global Convergence (2016–present), marked by growing interdisciplinary applications in public policy, population health, sustainable energy, digital logistics, and socio-technical systems analysis.

Complementing this evolutionary timeline, co-word analysis indicates two dominant research clusters: a technical cluster centered on forecasting, optimization, and multi-criteria decision-making, and a human-oriented cluster focusing on applications in the social sciences and public health. Citation analysis reconfirms the seminal role of Deng’s 1982 article as the intellectual cornerstone of the field. Geographically, China remains the epicenter of scientific production, with institutions such as Nanjing, Wuhan, and Chongqing universities playing central roles, while countries like the United States, the United Kingdom, and India contribute as complementary partners through international collaborations.

Conclusion: As global challenges grow more complex, the integration of GST with social sciences and advanced technologies—such as artificial intelligence, big data, and digital twins—emerges as crucial. GST, once seen primarily as a computational tool, is now shifting to an interdisciplinary framework that empowers researchers to address twenty-first century problems. Through this evolution, researchers, policymakers, and managers gain a common language, which enables informed decision-making even in uncertain environments. Advancing hybrid grey–AI models, fostering international scientific collaborations, and localizing models to address specific challenges—including resource management, public health, supply chain resilience, and crisis response—are key implications. Moreover, novel applications in climate change and data governance further expand opportunities for decision-making under uncertainty. In sum, this study presents the first systematic and comprehensive scientometric review of over four decades of GST research, clarifying both the current status of the field and future trajectories.