Complexity science: 'the new' in
thing' in government
Emphasis has to shift from
controlling the system, to adjusting parameters to influence how
the system unfolds
By ADRIAN
W.J. KUAH
FOR THE STRAITS TIMES
IN HIS speech at the Yale-NUS College ground-breaking ceremony
on July 6, Prime Minister Lee Hsien Loong stressed the importance
of an "appreciation of complexity" by graduates of the college.
This cryptic remark underscores the fact that complexity science is
now the rage in government and public policy.
The realisation that social, political and economic
systems - that is to say, human systems - are best understood as
complex systems that are dynamic, adaptable, emergent,
self-organised and non-linear, has resulted in new tools and
techniques being available to policymakers.
For example, in 2010, Danish forces operating in Helmand,
Afghanistan, experimented successfully with self-synchronised
social networks to manage their operations and intelligence
activities, in contrast to the more traditional hierarchical,
top-down command structures used in battlespace management.
Globally, city planners are adopting techniques used in
the analysis of complex fluid flows in the hopes of better
understanding, anticipating and managing traffic flows. Agent-based
models based on complexity science have augmented conventional
epidemiologic models that (wrongly) assume that the probability of
infection is normally distributed in populations that are
homogenous.
And in the national security enterprise, network-analysis
methods are being used to identify associations of terrorists and
to map the networks of terrorist cells.
Given that government officials are increasingly
encountering a daunting class of problems that involve complex
systems, the emergence of complexity science has been timely and
fortuitous. But the full benefits cannot be reaped unless there are
accompanying shifts in the mindsets and expectations of
policymakers.
From clockwork to
network
GIVEN that all complex systems
have features in common, the main attraction of complexity science
to policymakers is that the full suite of policy tools derived from
it can be universally applied to a wide range of policy problems
spanning pandemics, traffic jams, terrorism and so on.
While the versatility and effectiveness of complexity
approaches remain to be seen, there is no denying the growing
prevalence of complexity thinking in public policy. Yet complexity
science is based on a world view that is at odds with how
policymakers typically see the world.
The world view of the policymaker is that of the
Newtonian "world machine", to use physicist-writer Fritjof Capra's
term. The world is seen as a machine comprising basic parts
interacting in predictable albeit complicated ways, where the
machine is driven by the twin imperatives of rationality and
efficiency. Furthermore, an understanding of how the machine works
can be gleaned from an understanding of how the individual
components work.
Finally, the machine tends towards equilibrium, and any
deviation from equilibrium can be rectified by the turning of a
dial or the pushing of a lever. In such a world view, the
policymaker's role is to turn the dials and push the levers.
The world view that underpins complexity science and
complex systems could not be more different: the universe is no
longer seen as a (more or less) stable and predictable machine, the
understanding of which can be derived from the study of its parts.
Instead, it is an integrated and indivisible whole, a complex
system of dynamic relationships that also enmesh the policymaker,
and where micro-changes in its environment can be amplified in the
system's output. Furthermore, it is a system that is dynamic and
may not settle on an equilibrium state, if even such a state
exists.
The unresolved contradiction arising from holding a
mechanistic, deterministic world view while applying
complexity-based policy tools will result in a superficial,
problematic and ultimately failed adoption of complexity thinking
in public policy.
Whither the
policymaker?
CLEARLY, complexity science has provided new processes for tackling
problems. However, the recommendations made by complexity
researchers to policymakers can best be described as unpalatable.
For example, given that complex systems are adaptable and
self-organising, it may be that sometimes the best thing for
policymakers to do is nothing, to just let the system "sort itself
out".
This runs counter to the abiding image of the policymaker
as a hero taking aggressive action to solve economic problems or
societal woes. Policymakers have an in-built "action bias" and the
people they serve expect them to "do something, anything" in a
crisis.
And yet a major implication of complexity science can
best be summed up in a line from Winnie The Pooh: Don't
underestimate the value of doing nothing.
There is also the problem of attribution and evaluating
how policymakers perform. In complex systems, where small changes
can have big effects, big changes small effects, and where effects
have unanticipated causes, it is difficult to disentangle what the
policymaker does from the system's inherent dynamics. This
characteristic of complex systems can result in policymakers
claiming undeserved credit on the one
hand, as well as being wrongfully blamed on the other.
Finally, complexity science necessitates a shift in
mindset away from the deterministic to the probabilistic. It
focuses on identifying and analysing trends and probabilities,
rather than predicting specific events. Applied to public policy,
this changes the name of the policy game from algorithms to
heuristics. When it comes to complex systems, the emphasis must
therefore shift from doing something to control the system, to
shifting the system's parameters to influence how the system
unfolds. If policymakers wish to operate effectively under
conditions of complexity, then it is necessary for them to move
beyond determinism and abandon the reassuring myth of control.
The continued uncritical application of policy tools
based on complexity science, absent an appreciation of its
underlying assumptions, will likely result in unrealised
opportunities at best, and a misapplication of tools at worst.
The writer is a research fellow at
the S.Rajaratmam School of International Studies, Nanyang
Technological University.
Part D, The Straits Times, Saturday, July 14 2012, Pg D9