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 D9Edited by M the name 18 Jul `12, 6:11PM