Opinion Dynamics

Abstract

The computational social science is a challenging interdisciplinary field of study, aimed at studying social phenomena by merging the traditional research of social science with the use of a computational and data-driven approach. In the conceptual framework of the complex systems science the environment where these phenomena live and act can be seen as a social system, composed of many elements, the agents, which form a social complex network: thus, the agents can be represented by the nodes of a network, whose reciprocal positions determine the resulting network structure. Therefore, the complex systems science perspective proves particularly useful in order to study the interactions occurring in a social environment: as a matter of fact, these mechanisms give rise to emergent collective behaviors at the system level. Hence, the aim of the computational social science is to study the different interaction mechanisms so as to understand their role in the rise of the collective social phenomena. In order to perform effective and robust studies, plenty of models have been proposed, ranging from general social dynamics and social spreading to crowd behavior and opinion, cultural or language dynamics, or hierarchy or segregation formation. In this context, opinion dynamics is definitively a fundamental social behavior to study. By everyday experience, one of the most important social phenomenon is agreement: especially when it comes to a discussion involving different opinions, the process of reaching at least a partial agreement proved to be a basic catalyst of the evolution of the human thinking during time. From the dynamics of the single individuals’ interactions to the opinion dynamics in large populations, there are many situations in the social and political life in which a community needs to reach a consensus. Through a process of progressive agreements on different themes, some opinions spread, some others disappear and some others evolve through merging and non-linear memetic processes: in this way, the dominant macro-culture is formed and evolves in a society. Therefore, opinion dynamics models can be seen as a sub-set of the so-called consensus models. In general, a consensus model allows to understand if and how a set of interacting agents can reach a consensus when choosing among several options: political vote, opinions, cultural features are well-known examples of them. The agent-based modelling methodology proves particularly useful to represent the individuals’ behavior and analyze the population dynamics. In the literature, plenty of models are aimed at studying opinion dynamics: each model proposed can often be modified with various variants. In this chapter, we will present the overall scenario of opinion dynamics, by showing the main challenges and approaches to study and analyze consensus formation in heterogeneous settings and conditions.

Managers are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. I call such situations messes. Managers do not solve problems, they manage messes.

Russell L. Ackoff.