Information representation of blockchain technology: Risk evaluation of investment by personalized quantifier with cubic spline interpolation

Abstract

With the applications of blockchain technology in various fields, the research on blockchain has attracted much attention. Different from the researches focusing on specific applications of blockchain technology in a certain field, this study devotes to capturing the attitudes of investors regarding different risk criteria in blockchain technology investment decision making. We use personalized quantifiers to extract investors’ preferences on each risk evaluation criterion. At present, the personalized quantifier that can reflect individual attitudes and behavior intentions have been fitted by various functions, but there are still limitations. In this regard, this paper introduces a cubic spline interpolation function to fit the personalized quantifier, and addresses the consistency of the personalized quantifier in the ordered weighted averaging aggregation. Moreover, we employ a qualitative information representation model called probabilistic linguistic term sets to express decision-makers' evaluations on each criterion. We give a case study to illustrate the usability of the proposed personalized quantifier in blockchain risk evaluation. The comparative analysis with other four types of personalized quantifiers shows that our proposed personalized quantifier with cubic spline interpolation has ideal geometric characteristics in terms of smooth curve and high fitting accuracy, thus having strong applicability. Further, we show that this method is relatively easy to operate.

Publication DOI: https://doi.org/10.1016/j.ipm.2021.102571
Divisions: Aston Business School > Operations & Information Management
Aston Business School
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: The work was supported by the National Natural Science Foundation of China under Grant 71771156, 71971145 and the Chengdu Planning Project of Social Science under Grant YY2320191038
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Related URLs: https://www.sci ... 306457321000728 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-07
Published Online Date: 2021-03-15
Accepted Date: 2021-03-01
Authors: Wen, Zhi
Liao, Huchang
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 15 September 2022.

License: Creative Commons Attribution Non-commercial No Derivatives


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