Two changes have ushered in a new era of analyzing the complex and
interdependent world surrounding us. One is related to the increased
influx of data, furnishing the raw material for this revolution that is
now starting to impact economic thinking. The second change is due
to a subtler reason: a paradigm shift in the analysis of complex systems.
The buzzword “big data” is slowly being replaced by what is becoming
established as “data science.” While the cost of computer storage is
continually falling, storage capacity is increasing at an exponential
rate. In effect, seemingly endless streams of data, originating from
countless human endeavors, are continually flowing along global information superhighways and being stored not only in server farms and
the cloud, but — importantly — also in the researcher’s local databases.
However, collecting and storing raw data is futile if there is no way to
extract meaningful information from it. Here, the budding science of
complex systems is helping distill meaning from this data deluge.
Traditional problem-solving has been strongly shaped by the success
of the reductionist approach taken in science. Put in the simplest
terms, the focus has traditionally been on things in isolation — on the
tangible, the tractable, the malleable. But not so long ago, this focus
shifted to a subtler dimension of our reality, where the isolation is
overcome. Indeed, seemingly single and independent entities are always
components of larger units of organization and hence influence each other. Our world, while still being comprised of many of the same “things” as in the past, has become highly networked and interdependent — and, therefore, much more complex. From the interaction of independent entities, the notion of a system has emerged.
Understanding the structure of a system’s components does not
bring insights into how the system will behave as a whole. Indeed, the
very concept of emergence fundamentally challenges our knowledge
of complex systems, as self-organization allows for novel properties — features not previously observed in the system or its components — to
unfold. The whole is literally more than the sum of its parts.
This shift away from analyzing the structure of “things” to analyzing
their patterns of interaction represents a true paradigm shift, and
one that has impacted computer science, biology, physics and sociology.
The need to bring about such a shift in economics, too, can be
heard in the words of Andy Haldane, chief economist at the Bank of
England (Haldane 2011):
“Economics has always been desperate to burnish its scientific credentials
and this meant grounding it in the decisions of individual people. By
itself, that was not the mistake. The mistake came in thinking the behavior
of the system was just an aggregated version of the behavior of the
individual. Almost by definition, complex systems do not behave like this.
[…] Interactions between agents are what matters. And the key to that
is to explore the underlying architecture of the network, not the behavior
of any one node.”
In a nutshell, the key to the success of complexity science lies in ignoring
the complexity of the components while quantifying the structure
of interactions. An ideal abstract representation of a complex system is
given by a graph — a complex network. This field has been emerging
in a modern form since about the turn of the millennium (Watts and
Strogatz 1998; Barabási and Albert 1999; Albert and Barabási 2002;
Underpinning economics with insights from complex systems
requires a major culture change in how economics is conducted. Specialized knowledge needs to be augmented with a diversity of expertise. Or, in the words of Jean-Claude Trichet, former president of the European Central Bank (Trichet 2010):
“I would very much welcome inspiration from other disciplines: physics,
engineering, psychology, biology. Bringing experts from these fields together
with economists and central bankers is potentially very creative
and valuable. Scientists have developed sophisticated tools for analyzing
complex dynamic systems in a rigorous way.”
What’s more, scientists themselves have acknowledged this call for
action (see, e.g., Schweitzer et al. 2009; Farmer et al. 2012).
In what follows, I will present two case studies that provide an initial
glimpse of the potential of applying such a data-driven and network-
inspired type of research to economic systems. By uncovering
patterns of organization otherwise hidden in the data, these studies
caught the attention not only of scholars and the general public, but
also of policymakers.
In order to read the whole article go to: http://j-node.blogspot.ch/2016/02/decoding-financial-networkshidden.html
This was a chapter contribution to “To the Man with a Hammer:
Augmenting the Policymaker’s Toolbox for a Complex World”, Bertelsmann Stiftung, 2016:
This article collection helps point the way forward. Gathering a distinguished panel of complexity experts and policy innovators, it provides concrete examples of promising insights and tools, drawing from complexity science, the digital revolution and interdisciplinary approaches.