Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Abstract

Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

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

“…Agents opinions are also supposed to be binary and to be selected from {−1, +1}. This framework is extended in [188] to the case, in which agents can take more than two actions.…”

Hierarchical Heuristic Learning towards Effcient Norm Emergence

“…More recently, Yu et al [36] proposed an adaptive learning framework for efficient norm emergence. Later, Yu et al [37] proposed a novel adaptive learning to facilitate consensus formation among agents, in order to efficiently establish a consistent social norm in agent societies. However, all the aforementioned works usually focus on relatively small-size games, and do not address the issue of efficient norm emergence in large action space problems.…”

“…Until now, significant efforts have been devoted to investigating norm emergence problem from the bottom-up approaches [3,11,38,4,17,12,39,13,19,23,24,25,28,29,30,35,36,37]. Sen and Airiau [24] investigated the norm emergence problem in a population of agents within randomly connected networks where each agent is equipped with certain multiagent learning algorithms.…”

Research on the Community Number Evolution Model of Public Opinion Based on Stochastic Competitive Learning

“…In 2014, Tsang A proposed a model of opinion dynamics [15], which achieved the dynamics opinion model by building the trust function among members and based on the nature of homophilic network. In 2016, ChaoYu proposed a consensus formation model [16], which elaborated the process of consensus formation by constructing performance-driven and behavior-driven methods. The Gossiper-Media model [17] studies the interactive game between the public and the media in opinion propagation, confirming that competition among media will promote the formation of public opinion consensus.…”