Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making

Abstract

As the scale of multi-stage large group decision-making (LGDM) continues to expand, the possibility of low-contribution individuals exhibiting herd behavior also increases, potentially leading to the phenomenon of “fishing in troubled waters.” This may obstruct the speed of consensus reaching while generating no valuable opinions, which is a topic worthy of exploration. Considering that humans are easily influenced by interests, the employment of machine intelligence to objectively identify herd behavior is more appropriate. In this context, a herd behavior identification method based on behavioral characteristics clustering from the perspective of human–machine collaboration is herein proposed. First, from the human side, an opinion–social network coevolution model is constructed to simulate the consensus reaching process (CRP) of the expert group. Then, the group is clustered into three subgroups in consideration of behavior that encompasses both opinion changes and trust relationship changes. Based on this, the low-contribution cluster with a herd behavior pattern can be optimized from the machine side. Through simulation experiments, it is verified that herd behavior management significantly accelerates the consensus-reaching speed under the premise of having minimal impact on the decision-making results. In general terms, this study is the first to propose the concept of herd behavior and provides a solution to manage it from a new perspective, which is suitable for application in multi-stage LGDM scenarios.