Opinion dynamics: models, extensions and external effects

Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. [...]Comment: 42 pages, 6 figure

Modelling and Analysis of Social Contagion Processes with Dynamic Networks

Abstract. In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analysed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model – the ability to form clusters – is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified, and the convergence speed of networks is analysed.