Multi-attribute strategic weight manipulation with minimum adjustment trust relationship in social network group decision making. (February 2023)

• Record Type: Journal Article
• Authors: Liu, Yating; Liang, Haiming; Dong, Yucheng; Cao, Yongfeng
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: Social network group decision making (SNGDM) -- Strategic weight manipulation -- Trust relationship -- Mixed 0-1 linear programming models (MLPMs); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105672
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In social network group decision making (SNGDM), multi-attribute strategic weight manipulation refers to adjusting expert trust relationships to determine a strategic attribute weight for getting a coordinator's desired ranking results. We suggest a model of strategic weight manipulation with a minimum adjustment trust relationship to achieve the strategic attribute weight, motivated by the desire to reduce the adjustments. Then, in order to find the best solution for the suggested model, a method based on the mixed 0–1 linear programming models (MLPMs) was employed. Additionally, one desired property is provided in order to achieve a strategic attribute weight depending on the ranking range in social network situations. Finally, the efficiency of our suggested models is confirmed using a numerical example, and two simulation experiments are created to provide to compare weighted averaging (WA) and ordered weighted averaging (OWA). Because the OWA has a larger value of minimum adjustment when manipulating a strategic attribute weight, we argue that: (1) the OWA has a better performance in defending against strategic weight manipulation than the WA; (2) as the number of trust relationships and experts increases, the performance gap between the two approaches gets smaller. Highlights: We study the strategic weight manipulation problem in SNGDM. We construct the strategic weight manipulation model. We provide one property on solution existence of proposed model. We design two simulation experiments to provide the comparative analysis.