15,934 research outputs found
Reducing complexity of multiagent systems with symmetry breaking: an application to opinion dynamics with polls
In this paper we investigate the possibility of reducing the complexity of a
system composed of a large number of interacting agents, whose dynamics feature
a symmetry breaking. We consider first order stochastic differential equations
describing the behavior of the system at the particle (i.e., Lagrangian) level
and we get its continuous (i.e., Eulerian) counterpart via a kinetic
description. However, the resulting continuous model alone fails to describe
adequately the evolution of the system, due to the loss of granularity which
prevents it from reproducing the symmetry breaking of the particle system. By
suitably coupling the two models we are able to reduce considerably the
necessary number of particles while still keeping the symmetry breaking and
some of its large-scale statistical properties. We describe such a multiscale
technique in the context of opinion dynamics, where the symmetry breaking is
induced by the results of some opinion polls reported by the media
Equation-Free Multiscale Computational Analysis of Individual-Based Epidemic Dynamics on Networks
The surveillance, analysis and ultimately the efficient long-term prediction
and control of epidemic dynamics appear to be one of the major challenges
nowadays. Detailed atomistic mathematical models play an important role towards
this aim. In this work it is shown how one can exploit the Equation Free
approach and optimization methods such as Simulated Annealing to bridge
detailed individual-based epidemic simulation with coarse-grained,
systems-level, analysis. The methodology provides a systematic approach for
analyzing the parametric behavior of complex/ multi-scale epidemic simulators
much more efficiently than simply simulating forward in time. It is shown how
steady state and (if required) time-dependent computations, stability
computations, as well as continuation and numerical bifurcation analysis can be
performed in a straightforward manner. The approach is illustrated through a
simple individual-based epidemic model deploying on a random regular connected
graph. Using the individual-based microscopic simulator as a black box
coarse-grained timestepper and with the aid of Simulated Annealing I compute
the coarse-grained equilibrium bifurcation diagram and analyze the stability of
the stationary states sidestepping the necessity of obtaining explicit closures
at the macroscopic level under a pairwise representation perspective
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Multiscale modeling of granular flows with application to crowd dynamics
In this paper a new multiscale modeling technique is proposed. It relies on a
recently introduced measure-theoretic approach, which allows to manage the
microscopic and the macroscopic scale under a unique framework. In the
resulting coupled model the two scales coexist and share information. This
allows to perform numerical simulations in which the trajectories and the
density of the particles affect each other. Crowd dynamics is the motivating
application throughout the paper.Comment: 30 pages, 9 figure
Multiscale modeling of granular flows with application to crowd dynamics
In this paper a new multiscale modeling technique is proposed. It relies on a
recently introduced measure-theoretic approach, which allows to manage the
microscopic and the macroscopic scale under a unique framework. In the
resulting coupled model the two scales coexist and share information. This
allows to perform numerical simulations in which the trajectories and the
density of the particles affect each other. Crowd dynamics is the motivating
application throughout the paper.Comment: 30 pages, 9 figure
Multiscale Analysis of Spreading in a Large Communication Network
In temporal networks, both the topology of the underlying network and the
timings of interaction events can be crucial in determining how some dynamic
process mediated by the network unfolds. We have explored the limiting case of
the speed of spreading in the SI model, set up such that an event between an
infectious and susceptible individual always transmits the infection. The speed
of this process sets an upper bound for the speed of any dynamic process that
is mediated through the interaction events of the network. With the help of
temporal networks derived from large scale time-stamped data on mobile phone
calls, we extend earlier results that point out the slowing-down effects of
burstiness and temporal inhomogeneities. In such networks, links are not
permanently active, but dynamic processes are mediated by recurrent events
taking place on the links at specific points in time. We perform a multi-scale
analysis and pinpoint the importance of the timings of event sequences on
individual links, their correlations with neighboring sequences, and the
temporal pathways taken by the network-scale spreading process. This is
achieved by studying empirically and analytically different characteristic
relay times of links, relevant to the respective scales, and a set of temporal
reference models that allow for removing selected time-domain correlations one
by one
Generating realistic scaled complex networks
Research on generative models is a central project in the emerging field of
network science, and it studies how statistical patterns found in real networks
could be generated by formal rules. Output from these generative models is then
the basis for designing and evaluating computational methods on networks, and
for verification and simulation studies. During the last two decades, a variety
of models has been proposed with an ultimate goal of achieving comprehensive
realism for the generated networks. In this study, we (a) introduce a new
generator, termed ReCoN; (b) explore how ReCoN and some existing models can be
fitted to an original network to produce a structurally similar replica, (c)
use ReCoN to produce networks much larger than the original exemplar, and
finally (d) discuss open problems and promising research directions. In a
comparative experimental study, we find that ReCoN is often superior to many
other state-of-the-art network generation methods. We argue that ReCoN is a
scalable and effective tool for modeling a given network while preserving
important properties at both micro- and macroscopic scales, and for scaling the
exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the
paper was presented at the 5th International Workshop on Complex Networks and
their Application
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