Dynamics of Opinions and Social Structures (original) (raw)

Social structure and opinion formation

Arxiv preprint cond-mat/0407252, 2004

We present a dynamical theory of opinion formation that takes explicitly into account the structure of the social network in which individuals are embedded. This is the case that arises in situations as varied as the adoption of new technologies and political views. The theory predicts the evolution of a set of opinions through a social network and establishes the existence of a martingale property, i.e. that the expected weighted fraction of the population that holds a given opinion is constant in time. The distribution of the fraction stabilizes only after a long time that diverges with the system size. This coexistence of opinions within a social network is in agreement with the often observed locality effect, in which an opinion or a fad is localized to given groups without infecting the whole society. We verified these predictions, as well as those concerning the fragility of opinions and the importance of highly connected individuals in opinion formation, by performing computer experiments on a number of social networks.

Social influences in opinion dynamics: The role of conformity

Physica A: Statistical Mechanics and its Applications, 2014

We study the effects of social influences in opinion dynamics. In particular, we define a simple model, based on the majority rule voting, in order to consider the role of conformity. Conformity is a central issue in social psychology as it represents one of peoples behaviors that emerges as a result of their interactions. The proposed model represents agents, arranged in a network and provided with an individual behavior, that change opinion in function of those of their neighbors. In particular, agents can behave as conformists or as nonconformists. In the former case, agents change opinion in accordance with the majority of their social circle (i.e., their neighbors); in the latter case, they do the opposite, i.e., they take the minority opinion. Moreover, we investigate the nonconformity both on a global and on a local perspective, i.e., in relation to the whole population and to the social circle of each nonconformist agent, respectively. We perform a computational study of the proposed model, with the aim to observe if and how the conformity affects the related outcomes. Moreover, we want to investigate whether it is possible to achieve some kind of equilibrium, or of order, during the evolution of the system. Results highlight that the amount of nonconformist agents in the population plays a central role in these dynamics. In particular, conformist agents play the role of stabilizers in fully-connected networks, whereas the opposite happens in complex networks. Furthermore, by analyzing complex topologies of the agent network, we found that in the presence of radical nonconformist agents the topology of the system has a prominent role; oth-erwise it does not matter since we observed that a conformist behavior is almost always more convenient. Finally, we analyze the results of the model by considering that agents can change also their behavior over time, i.e., conformists can become nonconformists and vice versa.

Persistent instability in polarized opinion formation and collective decision-making

In this article we present a simple model of opinion formation and collective decision making, which identifies the conditions for unstable, non-linear outcomes of these processes. We propose a simple modification of the commonly used model of French from 1957: social actors change their influence in order to avoid a future outcome they do not like. The analysis of a simple, 3-member group shows that the social structure of the influence process has an important impact on the likelihood of persistent instability: one condition is polarization, a second is the presence of a bouncing subgroup or actor. The third condition is the intolerance for a discrepancy between the future outcome and the current opinion. In a complex 6-actor group computer simulations reveal very complex oscillations for intermediate levels of intolerance. The likelihood of these oscillations is discussed, as well as its consequences for the predictability of opinion formation and collective decisionmaking conditions.

How opinion dynamics generate group hierarchies

2010

We recently proposed a model coupling the evolution of the opinions of the individual with the local network topology. The opinion dynamics is based on the Bounded Confidence model. The social networks is based on a group concept where each individual is totally connected to the members of its group and is linked to the individuals of the other groups with a given probability. During a time step, the individual has to decide between discussing with a member of its own network and applying the opinion dynamics, or moving groups because it has an opinion far from the average opinion of its own group. One of the main results we obtained is that the group sizes, starting from an homogenous situation can be strongly heterogeneous at the equilibrium state. This kind of heterogeneity can be identified in many real networks. In this paper we present the complete set of behaviours that this complex model can exhibit, at group level. In particular we will focus on the mechanisms that lead to the stability of the groups and the appearance of heterogeneity in sizes.

Interplay between media and social influence in the collective behavior of opinion dynamics

2015

Messages conveyed by media act as a major drive in shaping attitudes and inducing opinion shift. On the other hand, individuals are strongly affected by peer pressure while forming their own judgment. We solve a general model of opinion dynamics where individuals either hold one of two alternative opinions or are undecided and interact pairwise while exposed to an external influence. As media pressure increases, the system moves from pluralism to global consensus; four distinct classes of collective behavior emerge, crucially depending on the outcome of direct interactions among individuals holding opposite opinions. Observed nontrivial behaviors include hysteretic phenomena and resilience of minority opinions. Notably, consensus could be unachievable even when media and microscopic interactions are biased in favor of the same opinion: The unfavored opinion might even gain the support of the majority.

Reconciling long-term cultural diversity and short-term collective social behavior

Proceedings of the National Academy of Sciences, 2012

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and longterm cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are intensively studied, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that interopinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space. When empirical data are used as inputs in models, ultrametricity has strong and counterintuitive effects. On short timescales, it facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long timescales, it suppresses cultural convergence by restricting it within disjoint groups. Moreover, ultrametricity implies that these results are surprisingly robust to modifications of the dynamical rules considered. Thus the empirical distribution of individuals in cultural space appears to systematically optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence in a diverse range of online and offline settings.

Opinion Dynamics

Computational social sciences, 2023

A series of authored and edited monographs that utilize quantitative and computational methods to model, analyze and interpret large-scale social phenomena. Titles within the series contain methods and practices that test and develop theories of complex social processes through bottom-up modeling of social interactions. Of particular interest is the study of the co-evolution of modern communication technology and social behavior and norms, in connection with emerging issues such as trust, risk, security and privacy in novel socio-technical environments. Computational Social Sciences is explicitly transdisciplinary: quantitative methods from fields such as dynamical systems, artificial intelligence, network theory, agent-based modeling, and statistical mechanics are invoked and combined with state-of-the-art mining and analysis of large data sets to help us understand social agents, their interactions on and offline, and the effect of these interactions at the macro level. Topics include, but are not limited to social networks and media, dynamics of opinions, cultures and conflicts, socio-technical co-evolution and social psychology. Computational Social Sciences will also publish monographs and selected edited contributions from specialized conferences and workshops specifically aimed at communicating new findings to a large transdisciplinary audience. A fundamental goal of the series is to provide a single forum within which commonalities and differences in the workings of this field may be discerned, hence leading to deeper insight and understanding.

Opinion Dynamics: Models, Extensions and External Effects

Understanding Complex Systems, 2016

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

Opinion group formation and dynamics: Structures that last from nonlasting entities

Physical Review E, 2012

We extend simple opinion models to obtain stable but continuously evolving communities. Our scope is to meet a challenge raised by sociologists of generating "structures that last from non lasting entities". We achieve this by introducing two kinds of noise on a standard opinion model. First, agents may interact with other agents even if their opinion difference is large. Second, agents randomly change their opinion at a constant rate. We show that for a large range of control parameters, our model yields stable and fluctuating polarized states, where the composition and mean opinion of the emerging groups is fluctuating over time.

Polarization and Non-Positive Social Influence

International Journal of Knowledge and Systems Science, 2012

The authors study patterns about group opinions in a group-based society by considering social influence. They classify three types of social influence: positive, neutral, and negative from the perspective of social identity, and investigate to what extent the non-positive social influence leads to group opinion polarization based on the Hopfield network model. Numerical simulations show that opinion in a group-based society would self-organize into bi-polarization pattern under the condition of no imposing external intervention, which is entirely different from the result of drift to an extreme polarization dominant state with single homogenous influence. These results are explained in the study and the authors show that opinions polarization in a group is coexisted with local structure balance.