A Bibliometric Review of Decision Models in Uncertainty Between 1990 and 2018

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The increase in uncertainty due to frequent changes affects decision making, increasing the complexity of choosing the best decisions. For this reason, the decision-maker is looking for alternatives to reduce risks and meet these new challenges. In this context, the document aims to carry out a bibliometric review of decision models under uncertainty between 1990 and 2018 and to learn about the scientific progress of seventeen peer-validated models. It analyses 21,535 articles in nine bibliometric indicators extracted from the Web of Science database. As main results, a positive trend of publications in the analysed models observed, being the Analytical Hierarchical Process (AHP) the most used one. Also, China presented as a country with more scientific collaborations. There is excellent potential for future lines of research on the subject.

Original languageEnglish
Title of host publicationComputational and Decision Methods in Economics and Business - Proceedings of the International Workshop “Innovation, Complexity and Uncertainty in Economics and Business”
EditorsAnna Maria Gil-Lafuente, Josefa Boria, Agustín Torres, José M. Merigó, Janusz Kacprzyk
PublisherSpringer Science and Business Media Deutschland GmbH
Pages217-225
Number of pages9
ISBN (Print)9783030937867
DOIs
StatePublished - 2022
EventInternational Workshop on Innovation, Complexity and Uncertainty in Economics and Business, REDCID 2019 - Barcelona, Spain
Duration: 14 Nov 201915 Nov 2019

Publication series

NameLecture Notes in Networks and Systems
Volume388 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Workshop on Innovation, Complexity and Uncertainty in Economics and Business, REDCID 2019
Country/TerritorySpain
CityBarcelona
Period14/11/1915/11/19

Keywords

  • AHP
  • Bibliometric
  • Decision-Making
  • Fuzzy Logic
  • Uncertainty

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