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Learn about the latest and greatest related to complex systems
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Steven Strogatz Talks Science and Math on the Joy of x Podcast
The noted mathematician and author Steven Strogatz explains why he
wanted to share intimate conversations with leading researchers
from diverse fields in his new Quanta Magazine podcast.
Complex economic activities concentrate in large cities
Pierre-Alexandre Balland, Cristian Jara-Figueroa, Sergio G.
Petralia, Mathieu P. A. Steijn, David L. Rigby & César A.
Nature Human Behaviour (2020)
Human activities, such as research, innovation and industry,
concentrate disproportionately in large cities. The ten most
innovative cities in the United States account for 23% of the
national population, but for 48% of its patents and 33% of its
gross domestic product. But why has human activity become
increasingly concentrated? Here we use data on scientific papers,
patents, employment and gross domestic product, for 353
metropolitan areas in the United States, to show that the spatial
concentration of productive activities increases with their
complexity. Complex economic activities, such as biotechnology,
neurobiology and semiconductors, concentrate disproportionately in
a few large cities compared to less--complex activities, such as
apparel or paper manufacturing. We use multiple proxies to measure
the complexity of activities, finding that complexity explains
from 40% to 80% of the variance in urban concentration of
occupations, industries, scientific fields and technologies. Using
historical patent data, we show that the spatial concentration of
cutting-edge technologies has increased since 1850, suggesting a
reinforcing cycle between the increase in the complexity of
activities and urbanization. These findings suggest that the
growth of spatial inequality may be connected to the increasing
complexity of the economy.
Analysis and control of epidemics in temporal networks with
self-excitement and behavioral changes
Lorenzo Zino, Alessandro . Rizzo, Maurizio Porfiri
European Journal of Control
The complexity of interaction patterns among individuals in social
systems plays a fundamental role on the inception and spreading of
epidemic outbreaks. Empirical evidence has shown that the network
of social interactions may co-evolve with the spread of the
disease at comparable time-scales. Time-varying features have also
been documented in the study of the propensity of individuals
toward social activity, leading to the emergence of burstiness and
temporal clustering. These temporal network dynamics are not
independent of the disease evolution, whereby infected individuals
could experience changes in their tendency to form connections,
spontaneously or due to exogenous control policies. Neglecting
these phenomena in modeling epidemics could lead to dangerous
mispredictions of an outbreak and ineffective control
interventions. In this paper, we propose a mathematically
tractable modeling framework that relies on a limited number of
parameters and encapsulates all these instances of
complex phenomena through the lens of activity driven networks.
Hawkes processes, Markov chains, and stability theory are
leveraged to assist in the analysis of the framework and the
formulation of theory-based control interventions. Our
mathematical findings confirm the intuition that bursty activity
patterns, typical of humans, facilitate epidemic spreading, while
behavioral changes aiming at individual isolation could accelerate
the eradication of epidemics. The proposed tools are demonstrated
on a real-world case of influenza spreading in Italy. Overall,
this work contributes new insight into the theory of temporal
networks, laying the foundations for the analysis and control of
spreading processes over networks with complex interaction
Neural Dendrites Reveal Their Computational Power
The dendritic arms of some human neurons can perform logic
operations that once seemed to require whole neural networks.
Mediterranean School of Complex Networks 2020
Date: 5 Sep - 12 Sep 2020
Location: Salina, Sicily
In the last decade, network theory has been revealed to be a
perfect instrument to model the structure of complex systems and
the dynamical process they are involved into. The wide variety of
applications to social sciences, technological networks, biology,
transportation and economic, to cite just only some of them,
showed that network theory is suitable to provide new insights
into many problems.
Given the success of the Sixth Edition in 2019 of the
Mediterranean School of Complex Networks, we call for applications
to the Seventh Edition in 2020.
Network experiment demonstrates converse symmetry breaking
F. Molnar, T. Nishikawa, and A.E. Motter,
Nature Physics (2020), doi:10.1038/s41567-019-0742-y.
Symmetry breaking—the phenomenon in which the symmetry of a system
is not inherited by its stable states—underlies pattern formation,
superconductivity and numerous other effects. Recent theoretical
work has established the possibility of converse symmetry
breaking, a phenomenon in which the stable states are symmetric
only when the system itself is not. This includes scenarios in
which interacting entities are required to be non-identical in
order to exhibit identical behaviour, such as in reaching
consensus. Here we present an experimental demonstration of this
phenomenon. Using a network of alternating-current
electromechanical oscillators, we show that their ability to
achieve identical frequency synchronization is enhanced when the
oscillators are tuned to be suitably non-identical and that
converse symmetry breaking persists for a range of noise levels.
These results have implications for the optimization and control
of network dynamics in a broad class of systems whose function
benefits from harnessing uniform behaviour.
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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