A multi‐agent affective interactive MAGDM approach and its applications

Abstract

AbstractTraditional multi‐attribute group decision‐making (MAGDM) methods focus on weights calculation of sub‐attributes and experts' preferences, but lack the discussion on the decision‐makers' affective interaction, and its influence on the decision preference and group consistency. To address this problem, the present study proposed a new multilayer affective computing model based on “personality–mood–emotion” pattern, under the multi‐agent decision system framework. In addition, we introduced the group trending index and affection‐preference incentive mechanism, which can help simulate MAGDM process and learn group experts' decision preferences. Further, we proposed a new multi‐agent affective interactive MAGDM (MAAI‐MAGDM) method, where we defined a novel group convergence index and an alternative decision entropy to explain the convergence process of decision and group consistency. Compared to the traditional MAGDM approaches, the proposed MAAI‐MAGDM method fully considered the affective features of each expert, reduced the dependence on aggregation operators and weight analysis, alleviated the workload of group experts, and effectively reduced the complexity of decision‐making calculation process. Finally, we verified that the proposed method can effectively assist the decision‐making processes by employing two numerical cases.