10,991 research outputs found
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
Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion
Social identities are among the key factors driving behavior in complex
societies. Signals of social identity are known to influence individual
behaviors in the adoption of innovations. Yet the population-level consequences
of identity signaling on the diffusion of innovations are largely unknown. Here
we use both analytical and agent-based modeling to consider the spread of a
beneficial innovation in a structured population in which there exist two
groups who are averse to being mistaken for each other. We investigate the
dynamics of adoption and consider the role of structural factors such as
demographic skew and communication scale on population-level outcomes. We find
that outgroup aversion can lead to adoption being delayed or suppressed in one
group, and that population-wide underadoption is common. Comparing the two
models, we find that differential adoption can arise due to structural
constraints on information flow even in the absence of intrinsic between-group
differences in adoption rates. Further, we find that patterns of polarization
in adoption at both local and global scales depend on the details of
demographic organization and the scale of communication. This research has
particular relevance to widely beneficial but identity-relevant products and
behaviors, such as green technologies, where overall levels of adoption
determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure
Predictability of catastrophic events: material rupture, earthquakes, turbulence, financial crashes and human birth
We propose that catastrophic events are "outliers" with statistically
different properties than the rest of the population and result from mechanisms
involving amplifying critical cascades. Applications and the potential for
prediction are discussed in relation to the rupture of composite materials,
great earthquakes, turbulence and abrupt changes of weather regimes, financial
crashes and human parturition (birth).Comment: Latex document of 22 pages including 6 ps figures, in press in PNA
Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes
Under the sociological theory of homophily, people who are similar to one
another are more likely to interact with one another. Marketers often have
access to data on interactions among customers from which, with homophily as a
guiding principle, inferences could be made about the underlying similarities.
However, larger networks face a quadratic explosion in the number of potential
interactions that need to be modeled. This scalability problem renders
probability models of social interactions computationally infeasible for all
but the smallest networks. In this paper we develop a probabilistic framework
for modeling customer interactions that is both grounded in the theory of
homophily, and is flexible enough to account for random variation in who
interacts with whom. In particular, we present a novel Bayesian nonparametric
approach, using Dirichlet processes, to moderate the scalability problems that
marketing researchers encounter when working with networked data. We find that
this framework is a powerful way to draw insights into latent similarities of
customers, and we discuss how marketers can apply these insights to
segmentation and targeting activities
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