238,100 research outputs found
Generalized Opinion Dynamics from Local Optimization Rules
We study generalizations of the Hegselmann-Krause (HK) model for opinion
dynamics, incorporating features and parameters that are natural components of
observed social systems. The first generalization is one where the strength of
influence depends on the distance of the agents' opinions. Under this setup, we
identify conditions under which the opinions converge in finite time, and
provide a qualitative characterization of the equilibrium. We interpret the HK
model opinion update rule as a quadratic cost-minimization rule. This enables a
second generalization: a family of update rules which possess different
equilibrium properties. Subsequently, we investigate models in which a external
force can behave strategically to modulate/influence user updates. We consider
cases where this external force can introduce additional agents and cases where
they can modify the cost structures for other agents. We describe and analyze
some strategies through which such modulation may be possible in an
order-optimal manner. Our simulations demonstrate that generalized dynamics
differ qualitatively and quantitatively from traditional HK dynamics.Comment: 20 pages, under revie
Consensus time in a voter model with concealed and publicly expressed opinions
The voter model is a simple agent-based model to mimic opinion dynamics in
social networks: a randomly chosen agent adopts the opinion of a randomly
chosen neighbour. This process is repeated until a consensus emerges. Although
the basic voter model is theoretically intriguing, it misses an important
feature of real opinion dynamics: it does not distinguish between an agent's
publicly expressed opinion and her inner conviction. A person may not feel
comfortable declaring her conviction if her social circle appears to hold an
opposing view. Here we introduce the Concealed Voter Model where we add a
second, concealed layer of opinions to the public layer. If an agent's public
and concealed opinions disagree, she can reconcile them by either publicly
disclosing her previously secret point of view or by accepting her public
opinion as inner conviction. We study a complete graph of agents who can choose
from two opinions. We define a martingale that determines the probability
of all agents eventually agreeing on a particular opinion. By analyzing the
evolution of in the limit of a large number of agents, we derive the
leading-order terms for the mean and standard deviation of the consensus time
(i.e. the time needed until all opinions are identical). We thereby give a
precise prediction by how much concealed opinions slow down a consensus.Comment: 21 pages, 6 figures, to appear in J. Stat. Mech. Theory Ex
Novel Multidimensional Models of Opinion Dynamics in Social Networks
Unlike many complex networks studied in the literature, social networks
rarely exhibit unanimous behavior, or consensus. This requires a development of
mathematical models that are sufficiently simple to be examined and capture, at
the same time, the complex behavior of real social groups, where opinions and
actions related to them may form clusters of different size. One such model,
proposed by Friedkin and Johnsen, extends the idea of conventional consensus
algorithm (also referred to as the iterative opinion pooling) to take into
account the actors' prejudices, caused by some exogenous factors and leading to
disagreement in the final opinions.
In this paper, we offer a novel multidimensional extension, describing the
evolution of the agents' opinions on several topics. Unlike the existing
models, these topics are interdependent, and hence the opinions being formed on
these topics are also mutually dependent. We rigorous examine stability
properties of the proposed model, in particular, convergence of the agents'
opinions. Although our model assumes synchronous communication among the
agents, we show that the same final opinions may be reached "on average" via
asynchronous gossip-based protocols.Comment: Accepted by IEEE Transaction on Automatic Control (to be published in
May 2017
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
Mass media and repulsive interactions in continuous-opinion dynamics
This letter focus on the effect of repulsive interactions on the adoption of
an external message in an opinion model. With a simple change in the rules, we
modify the Deffuant \emph{et al.} model to incorporate the presence of
repulsive interactions. We will show that information receptiveness is optimal
for an intermediate fraction of repulsive links. Using the master equation as
well as Monte Carlo simulations of the message-free model, we identify the
point where the system becomes optimally permeable to external influence with
an order-disorder transition
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