Complexity, interconnection, and prediction
From almost every perspective, global interconnectivity is on the rise. Satellite networks
and communication systems allow essentially instantaneous access to data and images
worldwide, fluctuations in Tokyo's financial markets are felt in New York and London,
and overpopulation, pollution, deforestation, and global warming are widely recognized
as everybody's problem. Social, economic, technological, and environmental systems are
simultaneously becoming increasingly entangled and interconnected with each other.
This globalization offers potential benefits including a higher standard of living,
increased access to information, more sophisticated health care, and the possibility of
providing economic incentives to protect environmentally sensitive areas.
Simultaneously, it carries potentially catastrophic risks associated with cascading failures
which may propagate rapidly when systems are strongly interdependent and densely
connected. Examples include cascading delays in transportation and communications
systems, as well as errant policies or technological blunders that initiate a whirlwind of
economic, political, and environmental consequences.
Of course, engineers and policymakers work extremely hard to design systems that will
not break down, despite a great deal of uncertainty in the environment in which they
operate and the components from which they are built. Few of us would get on an
airplane if this were not the case. Simultaneously, we accept the fact that there are no
guarantees. Similarly, biological evolution favors organisms which are tolerant to
variations in weather and nutrients which are common during their lifetime, while
selective pressure does little to protect organisms against rare disturbances. In advanced
systems, the robustness architectures that stabilize systems and minimize propagation of
damage are so dominant and pervasive that we often take them for granted. At their best,
advanced technologies and organisms combine complicated internal networks and
feedback loops with sloppy parts to create systems so robust as to create the illusion of
very simple, reliable, and consistent behavior, apparently unperturbed by the
environment. Nonetheless, failures occur. With increased connectivity there is the
potential for increased social, economic, and environmental cost. Can anything be done
to predict these events and mitigate their damage?
Fragilities arise when the very architectures that lead to robustness under one set of
circumstances backfire leading to extreme sensitivity in other cases. One example is the
automobile airbag, which protects passengers in high speed head on collisions, but poses
a danger to small children riding in the front seat, even under low speed or stationary
deployment. Another is the immune system which protects people from common colds
and viruses, but occasionally backfires attacking an individual's own cells in an auto-
immune disease. Such failure modes represent hypersensitivities which are intrinsically
coupled to the robustness mechanism itself. While overall, higher performance is
achieved when the robustness architecture is included, under rare circumstances the
outcome is worse than it would be if the architecture were not there at all. In many cases,
this eventually leads to additional layers of complexity, as in the case of increasingly
sophisticated airbag deployment circuitry, involving sensors which estimate the
passenger's size. Iteration of this process culminates in a complexity spiral, in which new
features are developed to mitigate sensitivities associated with previous layers. Since
robustness is created by very specific internal structures, when any of these systems is
disassembled there is very little latitude to reassembly if a working system is expected.
Even the rare cascading failure that is the fragile side of HOT complexity reveals only a
limited glimpse of a system's internal architecture. Nonetheless, studying a system's
robustness mechanisms provides a potential pathway to predicting fragilities.
HOT was developed initially using the models of statistical physics, modified to include a
primitive form of robust design. Imagine your goal is
to design a toy forest on a checkerboard landscape, where occupied sites correspond to
trees, and vacancies correspond to firebreaks. Occasionally, a spark hits the forest due to
lightning or some other source. If it hits a firebreak, nothing happens. However, if it hits
a tree, the fire burns it and propagates through the connected cluster of nearest neighbor
occupied sites. Furthermore, sparks are more common in some parts of the forest than
they are in others. If your goal is to maximize the yield of trees, given the possibility of
fire, what is the optimal strategy? While this is not a realistic model of forest
management, it serves to illustrate the basic tradeoff between maximizing functionality
under ideal circumstances (represented by high densities), and the need to devote
resources to the development of robustness architectures to protect the system against a
spectrum of disturbances. HOT configurations consist of compact, high density, cellular
patterns of contiguous trees, separated by efficient, linear firebreaks. Optimal patterns
are much more robust to fires than random configurations at similar densities, but are also
extremely sensitive to changes in the spatial distribution of sparks, or flaws in the barrier
patterns. In many cases, the "robust, yet fragile" nature of HOT systems leads to heavy
tails or power law statistics in the distribution of failure events. Models based on the
HOT mechanism have been very successful in quantitatively describing the statistical
distributions of forest fires, world wide web traffic, and electrical power outages. Heavy
tails reflect tradeoffs in systems characterized by high densities and high throughputs,
where many internal variables are tuned to favor small losses in common events, at the
expense of large losses when subject to rare or unexpected perturbations, even if the
perturbations themselves are infinitesimal.
HOT is motivated by biology and engineering, and builds on the mathematics of
Developing models of varying resolution, ranging from the tractable models on which the
basic HOT framework is built, to complex, domain specific application models for
technological, economic, ecological, and biological systems will connect our new tools
with real world applications, and inspire new questions for theoretical consideration.
The real test of a general framework for understanding complex systems is the extent to
which is can provide new insights which impact future technologies, policies, medicines,
etc.. This work has begun on several fronts and involves a spectrum of interdisciplinary
collaborations. For the case of forest fires, with collaborators in geography I have
developed a sophisticated new fire regime simulation environment to investigate
fundamental properties of fire regime dynamics, and the long term effects of evolution
and suppression on terrestrial landscapes. This work provides a fundamentally new
perspective on the dynamics of disturbance-prone ecosystems and how humans interact
with them. Findings will be of significance to science and management, as human
disruption of natural disturbance regimes is widespread and increasing.