Multi-attribute strategic weight manipulation with minimum adjustment trust relationship in social network group decision making

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.