Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models.

Automated summary

This study provides a systematic review of classical dynamic consensus and opinion dynamics models. It identifies the recent trends and methodologies of both models, discusses challenges and open problems, and suggests future research directions. The study finds that recent literature focuses on large-scale group decision-making models and behavioral management of decision-makers under social network analysis in the design of consensus and opinion dynamics models.