57,047 research outputs found
COHESION, CONSENSUS AND EXTREME INFORMATION IN OPINION DYNAMICS
Opinion formation is an important element of social dynamics. It has been widely studied in the last years with tools from physics, mathematics and computer science. Here, a continuous model of opinion dynamics for multiple possible choices is analysed. Its main features are the inclusion of disagreement and possibility of modulating external information/media effects, both from one and multiple sources. The interest is in identifying the effect of the initial cohesion of the population, the interplay between cohesion and media extremism, and the effect of using multiple external sources of information that can influence the system. Final consensus, especially with the external message, depends highly on these factors, as numerical simulations show. When no external input is present, consensus or segregation is determined by the initial cohesion of the population. Interestingly, when only one external source of information is present, consensus can be obtained, in general, only when this is extremely neutral, i.e., there is not a single opinion strongly promoted, or in the special case of a large initial cohesion and low exposure to the external message. On the contrary, when multiple external sources are allowed, consensus can emerge with one of them even when this is not extremely neutral, i.e., it carries a strong message, for a large range of initial conditions
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
Response Functions to Critical Shocks in Social Sciences: An Empirical and Numerical Study
We show that, provided one focuses on properly selected episodes, one can
apply to the social sciences the same observational strategy that has proved
successful in natural sciences such as astrophysics or geodynamics. For
instance, in order to probe the cohesion of a policy, one can, in different
countries, study the reactions to some huge and sudden exogenous shocks, which
we call Dirac shocks. This approach naturally leads to the notion of structural
(as opposed or complementary to temporal) forecast. Although structural
predictions are by far the most common way to test theories in the natural
sciences, they have been much less used in the social sciences. The Dirac shock
approach opens the way to testing structural predictions in the social
sciences. The examples reported here suggest that critical events are able to
reveal pre-existing ``cracks'' because they probe the social cohesion which is
an indicator and predictor of future evolution of the system, and in some cases
foreshadows a bifurcation. We complement our empirical work with numerical
simulations of the response function (``damage spreading'') to Dirac shocks in
the Sznajd model of consensus build-up. We quantify the slow relaxation of the
difference between perturbed and unperturbed systems, the conditions under
which the consensus is modified by the shock and the large variability from one
realization to another
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
Opinion dynamics with disagreement and modulated information
Opinion dynamics concerns social processes through which populations or
groups of individuals agree or disagree on specific issues. As such, modelling
opinion dynamics represents an important research area that has been
progressively acquiring relevance in many different domains. Existing
approaches have mostly represented opinions through discrete binary or
continuous variables by exploring a whole panoply of cases: e.g. independence,
noise, external effects, multiple issues. In most of these cases the crucial
ingredient is an attractive dynamics through which similar or similar enough
agents get closer. Only rarely the possibility of explicit disagreement has
been taken into account (i.e., the possibility for a repulsive interaction
among individuals' opinions), and mostly for discrete or 1-dimensional
opinions, through the introduction of additional model parameters. Here we
introduce a new model of opinion formation, which focuses on the interplay
between the possibility of explicit disagreement, modulated in a
self-consistent way by the existing opinions' overlaps between the interacting
individuals, and the effect of external information on the system. Opinions are
modelled as a vector of continuous variables related to multiple possible
choices for an issue. Information can be modulated to account for promoting
multiple possible choices. Numerical results show that extreme information
results in segregation and has a limited effect on the population, while milder
messages have better success and a cohesion effect. Additionally, the initial
condition plays an important role, with the population forming one or multiple
clusters based on the initial average similarity between individuals, with a
transition point depending on the number of opinion choices
How does leadership support the activity of communities of practice ?
the purpose of this paper is to present leadership as an important mechanism underlying the coordination and the cohesion of communities of practice. More precisely, it will be shown that an important factor conditioning the coordination and the cohesion of a community rests on the leaders’ capacity to influence individual behaviors. This capacity of influence is grounded on the high degrees of reputation and trust they enjoy within the community. However, coordination of individual behaviors is not ensured by the mere existence of leadership. A simulation model points out the conditions under which leadership forms an efficient coordinating device.communities of practice, leadership, reputation, exit, loyalty, coordination, social simulation.
Individualization as driving force of clustering phenomena in humans
One of the most intriguing dynamics in biological systems is the emergence of
clustering, the self-organization into separated agglomerations of individuals.
Several theories have been developed to explain clustering in, for instance,
multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of
fish, and animal herds. A persistent puzzle, however, is clustering of opinions
in human populations. The puzzle is particularly pressing if opinions vary
continuously, such as the degree to which citizens are in favor of or against a
vaccination program. Existing opinion formation models suggest that
"monoculture" is unavoidable in the long run, unless subsets of the population
are perfectly separated from each other. Yet, social diversity is a robust
empirical phenomenon, although perfect separation is hardly possible in an
increasingly connected world. Considering randomness did not overcome the
theoretical shortcomings so far. Small perturbations of individual opinions
trigger social influence cascades that inevitably lead to monoculture, while
larger noise disrupts opinion clusters and results in rampant individualism
without any social structure. Our solution of the puzzle builds on recent
empirical research, combining the integrative tendencies of social influence
with the disintegrative effects of individualization. A key element of the new
computational model is an adaptive kind of noise. We conduct simulation
experiments to demonstrate that with this kind of noise, a third phase besides
individualism and monoculture becomes possible, characterized by the formation
of metastable clusters with diversity between and consensus within clusters.
When clusters are small, individualization tendencies are too weak to prohibit
a fusion of clusters. When clusters grow too large, however, individualization
increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure
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