215,485 research outputs found
The Dynamics of Public Opinion in Complex Networks
This paper studies the problem of public opinion formation and concentrates on the interplays among three factors: individual attributes, environmental influences and information flow. We present a simple model to analyze the dynamics of four types of networks. Our simulations suggest that regular communities establish not only local consensus, but also global diversity in public opinions. However, when small world networks, random networks, or scale-free networks model social relationships, the results are sensitive to the elasticity coefficient of environmental influences and the average connectivity of the type of network. For example, a community with a higher average connectivity has a higher probability of consensus. Yet, it is misleading to predict results merely based on the characteristic path length of networks. In the process of changing environmental influences and average connectivity, sensitive areas are discovered in the system. By sensitive areas we mean that interior randomness emerges and we cannot predict unequivocally how many opinions will remain upon reaching equilibrium. We also investigate the role of authoritative individuals in information control. While enhancing average connectivity facilitates the diffusion of the authoritative opinion, it makes individuals subject to disturbance from non-authorities as well. Thus, a moderate average connectivity may be preferable because then the public will most likely form an opinion that is parallel with the authoritative one. In a community with a scale-free structure, the influence of authoritative individuals keeps constant with the change of the average connectivity. Provided that the influence of individuals is proportional to the number of their acquaintances, the smallest percentage of authorities is required for a controlled consensus in a scale free network. This study shows that the dynamics of public opinion varies from community to community due to the different degree of impressionability of people and the distinct social network structure of the community.Public Opinion, Complex Network, Consensus, Agent-Based Model
Compelled to do the right thing
We use a model of opinion formation to study the consequences of some
mechanisms attempting to enforce the right behaviour in a society. We start
from a model where the possible choices are not equivalent (such is the case
when the agents decide to comply or not with a law) and where an imitation
mechanism allow the agents to change their behaviour based on the influence of
a group of partners. In addition, we consider the existence of two social
constraints: a) an external authority, called monitor, that imposes the correct
behaviour with infinite persuasion and b) an educated group of agents that act
upon their fellows but never change their own opinion, i.e., they exhibit
infinite adamancy. We determine the minimum number of monitors to induce an
effective change in the behaviour of the social group, and the size of the
educated group that produces the same effect. Also, we compare the results for
the cases of random social interactions and agents placed on a network. We have
verified that a small number of monitors are enough to change the behaviour of
the society. This also happens with a relatively small educated group in the
case of random interactions.Comment: 8 pages, 9 figures, submitted to EPJ
Opinion Polarization by Learning from Social Feedback
We explore a new mechanism to explain polarization phenomena in opinion
dynamics in which agents evaluate alternative views on the basis of the social
feedback obtained on expressing them. High support of the favored opinion in
the social environment, is treated as a positive feedback which reinforces the
value associated to this opinion. In connected networks of sufficiently high
modularity, different groups of agents can form strong convictions of competing
opinions. Linking the social feedback process to standard equilibrium concepts
we analytically characterize sufficient conditions for the stability of
bi-polarization. While previous models have emphasized the polarization effects
of deliberative argument-based communication, our model highlights an affective
experience-based route to polarization, without assumptions about negative
influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017
Extremism propagation in social networks with hubs
One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission
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
Non-consensus opinion model on directed networks
Dynamic social opinion models have been widely studied on undirected
networks, and most of them are based on spin interaction models that produce a
consensus. In reality, however, many networks such as Twitter and the World
Wide Web are directed and are composed of both unidirectional and bidirectional
links. Moreover, from choosing a coffee brand to deciding who to vote for in an
election, two or more competing opinions often coexist. In response to this
ubiquity of directed networks and the coexistence of two or more opinions in
decision-making situations, we study a non-consensus opinion model introduced
by Shao et al. \cite{shao2009dynamic} on directed networks. We define
directionality as the percentage of unidirectional links in a network,
and we use the linear correlation coefficient between the indegree and
outdegree of a node to quantify the relation between the indegree and
outdegree. We introduce two degree-preserving rewiring approaches which allow
us to construct directed networks that can have a broad range of possible
combinations of directionality and linear correlation coefficient
and to study how and impact opinion competitions. We find that, as
the directionality or the indegree and outdegree correlation
increases, the majority opinion becomes more dominant and the minority
opinion's ability to survive is lowered
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