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Large Group Decision Making

Creating Decision Support Approaches at Scale

  • Iván  Palomares Carrascosa

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Iván Palomares Carrascosa
    Pages 1-4
  3. Iván Palomares Carrascosa
    Pages 5-36
  4. Iván Palomares Carrascosa
    Pages 37-45
  5. Iván Palomares Carrascosa
    Pages 47-93
  6. Iván Palomares Carrascosa
    Pages 95-103
  7. Iván Palomares Carrascosa
    Pages 105-108
  8. Iván Palomares Carrascosa
    Pages E1-E1
  9. Back Matter
    Pages 109-118

About this book

Introduction

This SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted.

Keywords

Group Decision Making Multi-criteria Decision Making Consensus Reaching Processes Decision Support Systems Preference Modeling Aggregation Multi-Attribute Decision Making Consensus Information Fusion Information Systems Social Network Analysis Data Science Artificial Intelligence

Authors and affiliations

  • Iván  Palomares Carrascosa
    • 1
  1. 1.School of Computer Science (SCEEM)University of BristolBristolUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-01027-0
  • Copyright Information The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-01026-3
  • Online ISBN 978-3-030-01027-0
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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