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Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models

Author

Listed:
  • Juan Carlos González-Quesada

    (Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • José Ramón Trillo

    (Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • Carlos Porcel

    (Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • Ignacio Javier Pérez

    (Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
    These authors contributed equally to this work.)

  • Francisco Javier Cabrerizo

    (Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
    These authors contributed equally to this work.)

Abstract

The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a significant challenge. This paper presents a novel framework that leverages the semantic and affective capabilities of large language models to support large-scale group decision-making tasks by extracting and quantifying experts’ communicative traits—specifically clarity and trust—from natural language input. Based on these traits, participants are clustered into behavioural groups, each of which is assigned a representative preference structure and a weight reflecting its internal cohesion and communicative quality. A sentiment-informed consensus mechanism then aggregates these group-level matrices to form a collective decision outcome. The method enhances scalability and interpretability while preserving the richness of human expression. The results suggest that incorporating behavioural dimensions into large-scale group decision-making via large language models fosters fairer, more balanced, and semantically grounded decisions, offering a promising avenue for next-generation decision-support systems.

Suggested Citation

  • Juan Carlos González-Quesada & José Ramón Trillo & Carlos Porcel & Ignacio Javier Pérez & Francisco Javier Cabrerizo, 2025. "Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models," Future Internet, MDPI, vol. 17(9), pages 1-24, August.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:381-:d:1731999
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    References listed on IDEAS

    as
    1. Chao, Xiangrui & Kou, Gang & Li, Tie & Peng, Yi, 2018. "Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information," European Journal of Operational Research, Elsevier, vol. 265(1), pages 239-247.
    2. José Ramón Trillo & Francisco Javier Cabrerizo & Francisco Chiclana & María Ángeles Martínez & Francisco Mata & Enrique Herrera-Viedma, 2022. "Theorem Verification of the Quantifier-Guided Dominance Degree with the Mean Operator for Additive Preference Relations," Mathematics, MDPI, vol. 10(12), pages 1-10, June.
    3. Liu, Bingsheng & Zhou, Qi & Ding, Ru-Xi & Palomares, Iván & Herrera, Francisco, 2019. "Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination," European Journal of Operational Research, Elsevier, vol. 275(2), pages 737-754.
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