The research area of Large Group Decision Making (LGDM) has acquired growing importance in the last 5 to 6 six years, with many researchers around the globe developing models and solutions to support collective decisions at large scale.
A variant of this LGDM framework, called Large-Scale Decision Making is the subject of this article published in Information Fusion (Impact Factor: 10.716, ranked 3/134 by ISI-JCR in Computer Science: Artificial Intelligence), in which the DSRS Lab collaborated with scientists from University of Granada (Spain), Tianjin University (China) and Sichuan University (China).
The last decade witnessed tremendous developments in social media and e-democracy technologies. A funda- mental 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 re- search 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 man- agement, 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.
The article establishes a taxonomy around which existing LSDM literature is overviewed, based on the key elements/processes undertaken in LSDM models (e.g. consensus measurement and building, subgroup clustering, behavior management…) and the types of approaches adopted by different scholars for each of these key elements.
Furthermore, the article provides a comprehensive discussion on the challenges and future research directions in LSDM, with special emphasis on (i) the potential role of AI and Data Fusion/Data Science technologies to improve these decision support models, and (ii) Innovative Areas of Application of LSDM.
Ru-Xi Ding, Iván Palomares, Xueqing Wang, Guo-Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion. Volume 59, July 2020, Pages 84-102.
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