1,832 research outputs found
Comparing Extremism Propagation Patterns in Continuous Opinion Models
We compare patterns of extremism propagation yielded by 4 continuous opinion models, when the main parameters vary, on different types of networks (total connection, random network, lattice). In two models the individuals take into account the uncertainty of their interlocutor, and they show similar patterns, with a higher probability of double extreme convergence than in the other couple of models (in which the interlocutor\'s uncertainty is not taken into account). The addition of noise does not change significantly the results, except that it favours the single extreme convergence in some models. The lattice topology of interactions provides results which are significantly different from the ones obtained with a random network of similar connection density. We identify 3 typical behaviours with a single initial extremist, which help to explain the different results. In particular, we observe that the single extreme convergence is favoured by small shortest paths between all pairs of nodes in the network.Continuous Opinion, Extremism, Convergence Pattern
The role of network topology on extremism propagation with the Relative Agreement opinion dynamics
In (Deffuant et al., 2002), we proposed a simple model of opinion dynamics,
which we used to simulate the influence of extremists in a population.
Simulations were run without any specific interaction structure and varying the
simulation parameters, we observed different attractors such as predominance of
centrism or of extremism. We even observed in certain conditions, that the
whole population drifts to one extreme of the opinion, even if initially there
are an equal number of extremists at each extreme of the opinion axis. In the
present paper, we study the influence of the social networks on the presence of
such a dynamical behavior. In particular, we use small-world networks with
variable connectivity and randomness of the connections. We find that the drift
to a single extreme appears only beyond a critical level of connectivity, which
decreases when the randomness increases.Comment: 15 pages, 9 figure
Bayesian Updating Rules in Continuous Opinion Dynamics Models
In this article, I investigate the use of Bayesian updating rules applied to
modeling social agents in the case of continuos opinions models. Given another
agent statement about the continuous value of a variable , we will see that
interesting dynamics emerge when an agent assigns a likelihood to that value
that is a mixture of a Gaussian and a Uniform distribution. This represents the
idea the other agent might have no idea about what he is talking about. The
effect of updating only the first moments of the distribution will be studied.
and we will see that this generates results similar to those of the Bounded
Confidence models. By also updating the second moment, several different
opinions always survive in the long run. However, depending on the probability
of error and initial uncertainty, those opinions might be clustered around a
central value.Comment: 14 pages, 5 figures, presented at SigmaPhi200
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
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