8,241 research outputs found
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
For whom will the Bayesian agents vote?
Within an agent-based model where moral classifications are socially learned,
we ask if a population of agents behaves in a way that may be compared with
conservative or liberal positions in the real political spectrum. We assume
that agents first experience a formative period, in which they adjust their
learning style acting as supervised Bayesian adaptive learners. The formative
phase is followed by a period of social influence by reinforcement learning. By
comparing data generated by the agents with data from a sample of 15000 Moral
Foundation questionnaires we found the following. 1. The number of information
exchanges in the formative phase correlates positively with statistics
identifying liberals in the social influence phase. This is consistent with
recent evidence that connects the dopamine receptor D4-7R gene, political
orientation and early age social clique size. 2. The learning algorithms that
result from the formative phase vary in the way they treat novelty and
corroborative information with more conservative-like agents treating it more
equally than liberal-like agents. This is consistent with the correlation
between political affiliation and the Openness personality trait reported in
the literature. 3. Under the increase of a model parameter interpreted as an
external pressure, the statistics of liberal agents resemble more those of
conservative agents, consistent with reports on the consequences of external
threats on measures of conservatism. We also show that in the social influence
phase liberal-like agents readapt much faster than conservative-like agents
when subjected to changes on the relevant set of moral issues. This suggests a
verifiable dynamical criterium for attaching liberal or conservative labels to
groups.Comment: 31 pages, 5 figure
Signatures of the neurocognitive basis of culture wars found in moral psychology data\ud
Moral Foundation Theory (MFT) states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations on a subset of issues. With the introduction of functional imaging techniques, a wealth of new data on neurocognitive processes has rapidly mounted and it has\ud
become increasingly more evident that this type of data should provide an adequate basis for modeling social systems. In particular, it has been shown that there is a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information.\ud
Furthermore this spectrum is correlated to political affiliation. Here we use methods of statistical mechanics to characterize the collective behavior of an agent-based model society whose interindividual interactions due to information exchange in the form of opinions, are in qualitative agreement with neurocognitive and psychological data. The main conclusion derived from the model is\ud
that the existence of diversity in the cognitive strategies yields different statistics for the sets of moral foundations and that these arise from the cognitive interactions of the agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data about moral dynamics, presents statistical signatures\ud
consistent with those that characterize opinions of conservatives and liberals. The higher the difference in the treatment of novel and corroborating information the more agents correlate to liberals.\u
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
Agent-based Social Psychology: from Neurocognitive Processes to Social Data
Moral Foundation Theory states that groups of different observers may rely on
partially dissimilar sets of moral foundations, thereby reaching different
moral valuations. The use of functional imaging techniques has revealed a
spectrum of cognitive styles with respect to the differential handling of novel
or corroborating information that is correlated to political affiliation. Here
we characterize the collective behavior of an agent-based model whose inter
individual interactions due to information exchange in the form of opinions are
in qualitative agreement with experimental neuroscience data. The main
conclusion derived connects the existence of diversity in the cognitive
strategies and statistics of the sets of moral foundations and suggests that
this connection arises from interactions between agents. Thus a simple
interacting agent model, whose interactions are in accord with empirical data
on conformity and learning processes, presents statistical signatures
consistent with moral judgment patterns of conservatives and liberals as
obtained by survey studies of social psychology.Comment: 11 pages, 4 figures, 2 C codes, to appear in Advances in Complex
System
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
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
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
Higher-Order Uncertainty
You have higher-order uncertainty iff you are uncertain of what opinions you should have. I defend three claims about it. First, the higher-order evidence debate can be helpfully reframed in terms of higher-order uncertainty. The central question becomes how your first- and higher-order opinions should relate—a precise question that can be embedded within a general, tractable framework. Second, this question is nontrivial. Rational higher-order uncertainty is pervasive, and lies at the foundations of the epistemology of disagreement. Third, the answer is not obvious. The Enkratic Intuition---that your first-order opinions must “line up” with your higher-order opinions---is incorrect; epistemic akrasia can be rational. If all this is right, then it leaves us without answers---but with a clear picture of the question, and a fruitful strategy for pursuing it
Rise of the centrist: from binary to continuous opinion dynamics
We propose a model that extends the binary ``united we stand, divided we
fall'' opinion dynamics of Sznajd-Weron to handle continuous and multi-state
discrete opinions. Disagreement dynamics are often ignored in continuous
extensions of the binary rules, so we make the most symmetric continuum
extension of the binary model that can treat the consequences of agreement
(debate) and disagreement (confrontation) within a population of agents. We use
the continuum extension as an opportunity to develop rules for persistence of
opinion (memory). Rules governing the propagation of centrist views are also
examined. Monte Carlo simulations are carried out. We find that both memory
effects and the type of centrist significantly modify the variance of average
opinions in the large timescale limits of the models. Finally, we describe the
limit of applicability for Sznajd-Weron's model of binary opinions as the
continuum limit is approached. By comparing Monte Carlo results and long
time-step limits, we find that the opinion dynamics of binary models are
significantly different to those where agents are permitted more than 3
opinions
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