12,289 research outputs found
Social Diffusion and Global Drift on Networks
We study a mathematical model of social diffusion on a symmetric weighted
network where individual nodes' states gradually assimilate to local social
norms made by their neighbors' average states. Unlike physical diffusion, this
process is not state conservational and thus the global state of the network
(i.e., sum of node states) will drift. The asymptotic average node state will
be the average of initial node states weighted by their strengths. Here we show
that, while the global state is not conserved in this process, the inner
product of strength and state vectors is conserved instead, and perfect
positive correlation between node states and local averages of their
self/neighbor strength ratios always results in upward (or at least neutral)
global drift. We also show that the strength assortativity negatively affects
the speed of homogenization. Based on these findings, we propose an adaptive
link weight adjustment method to achieve the highest upward global drift by
increasing the strength-state correlation. The effectiveness of the method was
confirmed through numerical simulations and implications for real-world social
applications are discussed.Comment: 7 pages, 3 figures; to appear in Phys. Rev.
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
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes
A method is provided for designing and training noise-driven recurrent neural
networks as models of stochastic processes. The method unifies and generalizes
two known separate modeling approaches, Echo State Networks (ESN) and Linear
Inverse Modeling (LIM), under the common principle of relative entropy
minimization. The power of the new method is demonstrated on a stochastic
approximation of the El Nino phenomenon studied in climate research
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
The Web as an Adaptive Network: Coevolution of Web Behavior and Web Structure
Much is known about the complex network structure of the Web, and about behavioral dynamics on the Web. A number of studies address how behaviors on the Web are affected by different network topologies, whilst others address how the behavior of users on the Web alters network topology. These represent complementary directions of influence, but they are generally not combined within any one study. In network science, the study of the coupled interaction between topology and behavior, or state-topology coevolution, is known as 'adaptive networks', and is a rapidly developing area of research. In this paper, we review the case for considering the Web as an adaptive network and several examples of state-topology coevolution on the Web. We also review some abstract results from recent literature in adaptive networks and discuss their implications for Web Science. We conclude that adaptive networks provide a formal framework for characterizing processes acting 'on' and 'of' the Web, and offers potential for identifying general organizing principles that seem otherwise illusive in Web Scienc
Foraging as an evidence accumulation process
A canonical foraging task is the patch-leaving problem, in which a forager
must decide to leave a current resource in search for another. Theoretical work
has derived optimal strategies for when to leave a patch, and experiments have
tested for conditions where animals do or do not follow an optimal strategy.
Nevertheless, models of patch-leaving decisions do not consider the imperfect
and noisy sampling process through which an animal gathers information, and how
this process is constrained by neurobiological mechanisms. In this theoretical
study, we formulate an evidence accumulation model of patch-leaving decisions
where the animal averages over noisy measurements to estimate the state of the
current patch and the overall environment. Evidence accumulation models belong
to the class of drift diffusion processes and have been used to model decision
making in different contexts. We solve the model for conditions where foraging
decisions are optimal and equivalent to the marginal value theorem, and perform
simulations to analyze deviations from optimal when these conditions are not
met. By adjusting the drift rate and decision threshold, the model can
represent different strategies, for example an increment-decrement or counting
strategy. These strategies yield identical decisions in the limiting case but
differ in how patch residence times adapt when the foraging environment is
uncertain. To account for sub-optimal decisions, we introduce an
energy-dependent utility function that predicts longer than optimal patch
residence times when food is plentiful. Our model provides a quantitative
connection between ecological models of foraging behavior and evidence
accumulation models of decision making. Moreover, it provides a theoretical
framework for potential experiments which seek to identify neural circuits
underlying patch leaving decisions
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