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Individual And Social Strategies To Deal With Ignorance Situations In Multi-Person Decision Making

Author

Listed:
  • S. ALONSO

    (Department of Software Engineering, University of Granada, Granada 18071, Spain)

  • E. HERRERA-VIEDMA

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

  • F. CHICLANA

    (Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK)

  • F. HERRERA

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

Abstract

Multi-person decision making problems involve the preferences of some experts about a set of alternatives in order to find the best one. However, sometimes experts might not possess a precise or sufficient level of knowledge of part of the problem and as a consequence that expert might not give all the information that is required. Indeed, this may be the case when the number of alternatives is high and experts are using fuzzy preference relations to represent their preferences. In the literature, incomplete information situations have been studied, and as a result, procedures that are able to compute the missing information of a preference relation have been designed. However, these approaches usually need at least a piece of information about every alternative in the problem in order to be successful in estimating all the missing preference values.In this paper, we address situations in which an expert does not provide any information about a particular alternative, which we call situations oftotal ignorance. We analyze several strategies to deal with these situations. We classify these strategies into: (i)individualstrategies that can be applied to each individual preference relation without taking into account any information from the rest of experts and (ii)socialstrategies, that is, strategies that make use of the information available from the group of experts. Both individual and social strategies use extra assumptions or knowledge, which could not be directly instantiated in the experts preference relations. We also provide an analysis of the advantages and disadvantages of each one of the strategies presented, and the situations where some of them may be more adequate to be applied than the others.

Suggested Citation

  • S. Alonso & E. Herrera-Viedma & F. Chiclana & F. Herrera, 2009. "Individual And Social Strategies To Deal With Ignorance Situations In Multi-Person Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 313-333.
  • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:02:n:s0219622009003417
    DOI: 10.1142/S0219622009003417
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    Citations

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    Cited by:

    1. Büyüközkan, Gülçin & Karabulut, Yağmur & Mukul, Esin, 2018. "A novel renewable energy selection model for United Nations' sustainable development goals," Energy, Elsevier, vol. 165(PA), pages 290-302.
    2. Ming Tang & Huchang Liao & Zongmin Li & Zeshui Xu, 2018. "Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations," IJERPH, MDPI, vol. 15(4), pages 1-21, April.
    3. Liu, Fang & Zhang, Wei-Guo & Wang, Zhong-Xing, 2012. "A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making," European Journal of Operational Research, Elsevier, vol. 218(3), pages 747-754.
    4. Sergio Alonso & Rosana Montes & Daniel Molina & Iván Palomares & Eugenio Martínez-Cámara & Manuel Chiachio & Juan Chiachio & Francisco J. Melero & Pablo García-Moral & Bárbara Fernández & Cristina Mor, 2021. "Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys," Sustainability, MDPI, vol. 13(11), pages 1-27, May.
    5. Kylie Clay & Lauren Cooper, 2022. "Safeguarding against Harm in a Climate-Smart Forest Economy: Definitions, Challenges, and Solutions," Sustainability, MDPI, vol. 14(7), pages 1-13, April.
    6. Büyüközkan, Gülçin & Güleryüz, Sezin, 2016. "An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey," International Journal of Production Economics, Elsevier, vol. 182(C), pages 435-448.
    7. Carmen De Maio & Aurelio Tommasetti & Orlando Troisi & Massimiliano Vesci & Giuseppe Fenza & Vincenzo Loia, 2016. "Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 923-948, September.

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