rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Large Group Decision Making
    sub title: Creating Decision Support Approaches at Scale
  name:
    name part: Palomares Carrascosa, Iván.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 118 p. 43 illus., 33 illus. in color. online resource.
  abstract: 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.
  table of contents: 1 Introduction -- 2 Group Decision Making and Consensual Processes -- 3 Scaling things up: Large Group Decision Making (LGDM) -- 4 LGDM Approaches and Models: a Literature Review -- 5 Implementations and Real-World Applications of LGDM Research -- 6 Conclusions and Future Directions of Research.
  note: by Iván Palomares Carrascosa.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Management information systems
  subject:
    topic: Computer science
  subject:
    topic: Big data
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Management of Computing and Information Systems
  subject:
    topic: Big Data
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9783030010270
  identifier: https://doi.org/10.1007/978-3-030-01027-0
  record info:
    record creation date: 181031
    record change date: 20191220130100.0
    record identifier: 978-3-030-01027-0