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what really complexity fury is focusing
on is to identify what are the basic
laws that are common across many systems
that are complex we started to get and
develop the tools to handle them in a
quantitative manner so complexity is
slowly moving from bank in metaphor
towards bank a science
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in a complex system you don't always
know what are the outcomes of the
interventions a classical example
obviously is the butterfly effect these
systems are highly nonlinear they are
not predictable using traditional tools
of science
studying complex systems has taught us
that many surprising pitfalls on the way
from micro to macro phenomenon these
systems are critically fragile to
external perturbations and small change
of parameters can trigger large systemic
effects
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fractals are much more than just the
pictures of mathematics they're the
pictures of nature they describe how
nature works how nature builds itself
how a simple process repeating over and
over and over again creates the
complexity that we see all around us
production again and again creates
patterns that repeat at different scales
very complex and very simple to me
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they say that well we have always live
in a very complex world rapidly
escalating complexity there's a level of
interconnectedness the level of
interdependency is unprecedented
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therefore it makes the world more
volatile more complex more uncertain and
increased connectivity has created
strong and often unknown
interdependencies the fundamental
question from network science
perspective is if you are given one of
these massive networks with millions and
hundreds of millions of nodes and truly
billions of links how do you start
analyzing it how do you understand it
how do you extract its properties and
what are those properties that you
really care about
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human networks can develop into a
complex system from very simple
interaction from it's part it's
decentralized its self organized the
power is distributed we are all agents
in different networks but they are not
made explicit and a critical idea here
is that there is a coevolution between
agents and structures agents are
creating the structures the markets the
institutions the technologies to which
they're responding to and then in turn
responding to the new structures and the
agents evolve themselves over time
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what big data is changing is that we
less and less rely on models and we more
and more rely on real-time dance
behavior what is going on in their
systems so modeling has changed to
taking this big flow data that coming
towards me and try to understand the
relationship between them and try to
have a real-time spatially distributed a
picture of what is going under your
system
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user data must come together with our
mathematical abilities
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but beyond these tools what I believe is
very important is that we now have to
come up with new tools new ideas and
concepts to face the problems we have
today
you
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