A decision-making framework based on prospect theory with probabilistic linguistic term sets
Jing Gu,
Ying Zheng,
Xiaoli Tian and
Zeshui Xu
Journal of the Operational Research Society, 2021, vol. 72, issue 4, 879-888
Abstract:
In real-world decisions, we often encounter situations when decision-makers’ (DMs’) preferences can only be expressed as uncertain linguistic terms instead of crisp values. Similarly, when decisions involving several risky prospects with linguistic outcome information, it is a challenge to properly calculate the corresponding prospect values. To address this issue, this paper proposes a decision-making framework based on prospect theory where the outcomes are characterized by probabilistic linguistic term sets (PLTSs). The key contributions of this research are twofold: Firstly, it allows DMs to express their assessment of outcomes in terms of linguistic terms with interval probabilities. Secondly, it furnishes a paradigm to extend prospect theory to accommodate other forms of fuzzy and linguistic input. To begin with, this paper first presents different types of PLTSs. Then, gains and losses are calculated based on the positive and negative reference points and the operation rules of PLTSs. In accordance with the value and probability weight functions, the weighted prospect values are determined. Finally, we apply the decision-making framework to a practical case to illustrate its feasibility under linguistic environment.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1701957 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:72:y:2021:i:4:p:879-888
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2019.1701957
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().