134,840 research outputs found
Can extremism guarantee pluralism?
Many models have been proposed to explain opinion formation in groups of
individuals; most of these models study opinion propagation as the interaction
between nodes/agents in a social network. Opinion formation is a complex
process and a realistic model should also take into account the important
feedbacks that the opinions of the agents have on the structure of the social
networks and on the characteristics of the opinion dynamics. In this paper we
will show that associating to different agents different kinds of
interconnections and different interacting behaviours can lead to interesting
scenarios, like the coexistence of several opinion clusters, namely pluralism.
In our model agents have opinions uniformly and continuously distributed
between two extremes. The social network is formed through a social aggregation
mechanism including the segregation process of the extremists that results in
many real communities. We show how this process affects the opinion dynamics in
the whole society. In the opinion evolution we consider the different
predisposition of single individuals to interact and to exchange opinion with
each other; we associate to each individual a different tolerance threshold,
depending on its own opinion: extremists are less willing to interact with
individuals with strongly different opinions and to change significantly their
ideas. A general result is obtained: when there is no interaction restriction,
the opinion always converges to uniformity, but the same is happening whenever
a strong segregation process of the extremists occurs. Only when extremists are
forming clusters but these clusters keep interacting with the rest of the
society, the survival of a wide opinion range is guaranteed.Comment: 20 pages, 10 figure
Can Extremism Guarantee Pluralism?
Many models have been proposed to explain the opinion formation in a group of individuals; most of these models study the opinion propagation as the interaction between nodes/agents in a social network. Opinion formation is a very complex process and a realistic model should also take into account the important feedbacks that the opinions of the agents have on the structure of the social networks and on the characteristics of the opinion dynamics. In this paper we will show that associating to different agents different kind of interconnections and different interacting behaviour can lead to interesting scenarios, like the co-existence of several opinion clusters, namely pluralism. In our model agents have opinions uniformly and continuously distributed between two extremes. The social network is formed through a social aggregation mechanism including the segregation process of the extremists that results in many real communities. We show how this process affects opinion dynamics in the whole society. In the opinion evolution we consider the different predisposition of single individuals to interact and to to modify each other's opinions; we associate to each individual a different tolerance threshold, depending on its own opinion: extremists are less willing to interact with individuals with strongly different opinions and to change significantly their ideas. A general result is obtained: when there is no interaction restriction, the opinion always converges to uniformity, but the same is happening whenever a strong segregation process of the extremists occurs. Only when extremists are forming clusters but these clusters keep interacting with the rest of the society, the survival of a wide opinion range is guaranteed.Extremists, Segregation, Opinion Dynamics
Opinion and community formation in coevolving networks
In human societies opinion formation is mediated by social interactions,
consequently taking place on a network of relationships and at the same time
influencing the structure of the network and its evolution. To investigate this
coevolution of opinions and social interaction structure we develop a dynamic
agent-based network model, by taking into account short range interactions like
discussions between individuals, long range interactions like a sense for
overall mood modulated by the attitudes of individuals, and external field
corresponding to outside influence. Moreover, individual biases can be
naturally taken into account. In addition the model includes the opinion
dependent link-rewiring scheme to describe network topology coevolution with a
slower time scale than that of the opinion formation. With this model
comprehensive numerical simulations and mean field calculations have been
carried out and they show the importance of the separation between fast and
slow time scales resulting in the network to organize as well-connected small
communities of agents with the same opinion.Comment: 10 pages, 5 figures. New inset for Fig. 1 and references added.
Submitted to Physical Review
Surviving opinions in Sznajd models on complex networks
The Sznajd model has been largely applied to simulate many sociophysical
phenomena. In this paper we applied the Sznajd model with more than two
opinions on three different network topologies and observed the evolution of
surviving opinions after many interactions among the nodes. As result, we
obtained a scaling law which depends of the network size and the number of
possible opinions. We also observed that this scaling law is not the same for
all network topologies, being quite similar between scale-free networks and
Sznajd networks but different for random networks.Comment: 9 pages, 3 figure
Scale-free networks in complex systems
In the past few years, several studies have explored the topology of
interactions in different complex systems. Areas of investigation span from
biology to engineering, physics and the social sciences. Although having
different microscopic dynamics, the results demonstrate that most systems under
consideration tend to self-organize into structures that share common features.
In particular, the networks of interaction are characterized by a power law
distribution, , in the number of connections per node,
, over several orders of magnitude. Networks that fulfill this propriety of
scale-invariance are referred to as ``scale-free''. In the present work we
explore the implication of scale-free topologies in the antiferromagnetic (AF)
Ising model and in a stochastic model of opinion formation. In the first case
we show that the implicit disorder and frustration lead to a spin-glass phase
transition not observed for the AF Ising model on standard lattices. We further
illustrate that the opinion formation model produces a coherent, turbulent-like
dynamics for a certain range of parameters. The influence, of random or
targeted exclusion of nodes is studied.Comment: 9 pages, 4 figures. Proceeding to "SPIE International Symposium
Microelectronics, MEMS, and Nanotechnology", 11-15 December 2005, Brisbane,
Australi
On the Steady State of Continuous Time Stochastic Opinion Dynamics with Power Law Confidence
This paper introduces a class of non-linear and continuous-time opinion
dynamics model with additive noise and state dependent interaction rates
between agents. The model features interaction rates which are proportional to
a negative power of opinion distances. We establish a non-local partial
differential equation for the distribution of opinion distances and use Mellin
transforms to provide an explicit formula for the stationary solution of the
latter, when it exists. Our approach leads to new qualitative and quantitative
results on this type of dynamics. To the best of our knowledge these Mellin
transform results are the first quantitative results on the equilibria of
opinion dynamics with distance-dependent interaction rates. The closed form
expressions for this class of dynamics are obtained for the two agent case.
However the results can be used in mean-field models featuring several agents
whose interaction rates depend on the empirical average of their opinions. The
technique also applies to linear dynamics, namely with a constant interaction
rate, on an interaction graph
Modelling opinion formation driven communities in social networks
In a previous paper we proposed a model to study the dynamics of opinion
formation in human societies by a co-evolution process involving two distinct
time scales of fast transaction and slower network evolution dynamics. In the
transaction dynamics we take into account short range interactions as
discussions between individuals and long range interactions to describe the
attitude to the overall mood of society. The latter is handled by a uniformly
distributed parameter , assigned randomly to each individual, as
quenched personal bias. The network evolution dynamics is realized by rewiring
the societal network due to state variable changes as a result of transaction
dynamics. The main consequence of this complex dynamics is that communities
emerge in the social network for a range of values in the ratio between time
scales. In this paper we focus our attention on the attitude parameter
and its influence on the conformation of opinion and the size of the resulting
communities. We present numerical studies and extract interesting features of
the model that can be interpreted in terms of social behaviour.Comment: 5 pages, 2 figures. Added references. To appear in Special Issue 2010
of Computer Physics Communication
Are Opinions Based on Science: Modelling Social Response to Scientific Facts
As scientists we like to think that modern societies and their members base
their views, opinions and behaviour on scientific facts. This is not
necessarily the case, even though we are all (over-) exposed to information
flow through various channels of media, i.e. newspapers, television, radio,
internet, and web. It is thought that this is mainly due to the conflicting
information on the mass media and to the individual attitude (formed by
cultural, educational and environmental factors), that is, one external factor
and another personal factor. In this paper we will investigate the dynamical
development of opinion in a small population of agents by means of a
computational model of opinion formation in a co-evolving network of socially
linked agents. The personal and external factors are taken into account by
assigning an individual attitude parameter to each agent, and by subjecting all
to an external but homogeneous field to simulate the effect of the media. We
then adjust the field strength in the model by using actual data on scientific
perception surveys carried out in two different populations, which allow us to
compare two different societies. We interpret the model findings with the aid
of simple mean field calculations. Our results suggest that scientifically
sound concepts are more difficult to acquire than concepts not validated by
science, since opposing individuals organize themselves in close communities
that prevent opinion consensus.Comment: 21 pages, 5 figures. Submitted to PLoS ON
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