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Investment decision analysis of international megaprojects based on cognitive linguistic cloud models

    Xiaomei Mi   Affiliation
    ; Huchang Liao   Affiliation
    ; Xiao-Jun Zeng   Affiliation

Abstract

The investment decision analysis of international megaprojects is a major area of interest. The choice of interna
tional megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model.

Keyword : megaproject investment, decision analysis, cognitive linguistic cloud model, multi-granular information, personalized semantics

How to Cite
Mi, X., Liao, H., & Zeng, X.-J. (2020). Investment decision analysis of international megaprojects based on cognitive linguistic cloud models. International Journal of Strategic Property Management, 24(6), 414-427. https://doi.org/10.3846/ijspm.2020.13669
Published in Issue
Oct 26, 2020
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