Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective

Ru Xi Ding, Iván Palomares*, Xueqing Wang, Guo Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, Francisco Herrera

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

31 Citations (Scopus)

Abstract

The last decade witnessed tremendous developments in social media and e-democracy technologies. A fundamental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM research was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior management, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided.

Original languageEnglish
Pages (from-to)84-102
Number of pages19
JournalInformation Fusion
Volume59
Issue numberJuly 2020
Early online date23 Jan 2020
DOIs
Publication statusE-pub ahead of print - 23 Jan 2020

Keywords

  • Artificial Intelligence
  • Behaviour management
  • Consensus reaching processes
  • Group decision making
  • Large-scale decision making
  • Preference modelling
  • Subgroup clustering

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