Large-Scale Group Decision-Making Frameworks

Large-scale group decision-making frameworks address the challenge of coordinating the views and expertise of hundreds or thousands of participants in contexts ranging from emergency planning and healthcare to environmental policy and renewable energy investment. Central to these frameworks are strategies for managing heterogeneity in preferences, ensuring scalability in computation, and guiding consensus through iterative feedback. Clustering techniques are widely employed to partition decision makers into subgroups with similar opinions, thereby reducing complexity and preserving the integrity of original information. Consensus models then operate within and between these clusters, often guided by influence networks or feedback mechanisms, to converge on collective judgments. In parallel, big data paradigms such as MapReduce have been harnessed to aggregate and analyse vast volumes of preference data, enabling real‐time or near-real‐time decision support. Across disciplines, the emphasis has shifted towards hybrid approaches that integrate linguistic representations, probabilistic assessments and network analysis to reflect the nuanced nature of human judgement. The global significance of these frameworks lies in their capacity to deliver robust, transparent and scalable decisions in high-stakes environments, with practical applications that span healthcare resource allocation, emergency response planning and large-scale public policy deliberations.

Research from Nature Portfolio

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Research from all publishers

Recent work has provided a comprehensive state-of-the-art survey of large-scale group decision-making in the era of Big Data, outlining key challenges such as information overload, preference heterogeneity and consensus stability. This survey synthesises existing methods across disciplines and identifies research gaps in the management of social big data for decision support.

Another significant contribution employs fuzzy cluster analysis to tackle emergency response plan selection. In this approach, decision makers are grouped via fuzzy clustering, and a feedback-driven consensus procedure is introduced to adjust subgroup matrices that fail to reach agreement. Heterogeneous information is preserved through similarity measures, and the TOPSIS technique is used to select the best alternative under crisis conditions.

In the healthcare domain, a MapReduce-based model has been developed for Industry 4.0 environments. Decision makers are first clustered using a biogeography-based optimisation combined with fuzzy C-means. Subgroup preferences are represented by two-tuple fuzzy linguistic expressions, and classification is achieved through a deep learning-inspired feature extractor and extreme learning machine. The MapReduce framework enables efficient handling of massive clinical and operational datasets to support multicriteria group decisions.

Large-Scale Group Decision-Making Frameworks publication trend

The graph below shows the total number of articles in large-scale group decision-making frameworks across all publications each year (not limited to Nature Index journals).

Technical terms

Large-Scale Group Decision Making (LSGDM): Decision processes involving hundreds or thousands of stakeholders, requiring specialised methods to manage scale, heterogeneity and consensus.

Fuzzy Cluster Analysis: A soft-clustering technique that assigns decision makers to subgroups with varying degrees of membership based on similarity in their expressed preferences.

Consensus Reaching Process: An iterative mechanism by which subgroups or individuals adjust their opinions through feedback loops to achieve a predefined level of agreement.

MapReduce: A distributed data-processing model that splits tasks into mapping and reducing phases, facilitating the analysis of large datasets in decision-making frameworks.

References

1. From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega (2021).
2. Heterogeneous Large-Scale Group Decision Making Using Fuzzy Cluster Analysis and Its Application to Emergency Response Plan Selection. IEEE Transactions on Systems Man and Cybernetics Systems (2021).
3. LSGDM with Biogeography‐Based Optimization (BBO) Model for Healthcare Applications. Journal of Healthcare Engineering (2022).