Simple guide to complexity

PUBLISHED : Sunday, 25 September, 2011, 12:00am
UPDATED : Sunday, 25 September, 2011, 12:00am


'Life is so complicated!' How often have you heard yourself or someone else say this while tackling the daily challenges of job, family, technology, bureaucracy, death, taxes and booking that holiday in the Maldives? And of course, there is the debt crisis in Europe destroying your portfolio.

Complexity reigns in the real world, and increasingly in the world of science, too. Just last week, Cern, the European Organisation for Nuclear Research, reported that a neutrino beam fired from a particle accelerator near Geneva travelled 60 nanoseconds faster than the speed of light. This cast doubt on one of the very pillars of physics and Einstein's theory of relativity - that nothing travels faster than light.

In physics, mathematics and other scientific disciplines, complexity theory is gaining sway and changing the way we analyse the universe.

It was not always this way. Until the second half of the 20th century, scientists used a reductionist approach to explain the laws of nature, breaking things down into elementary forms. The dominant idea was that laws govern the behaviour of all animate and inanimate matter in the universe.

By understanding how fundamental particles work, we understand how our universe came into being. By understanding cells and molecules, we will understand the origin of life.

That is why today, Big Science builds larger and costlier equipment to break down smaller particles. Witness the Euro7.5 billion (HK$80 billion) Large Hadron Collider being built in Geneva, for instance. And increasingly powerful computers are deployed to decode the mystery of how cells are formed.

But this approach of reducing matter falls short when faced with the twin difficulties of scale and complexity, as Nobel laureate for physics Philip Warren Anderson observed in 1972. He noticed that simple phenomena generated complex outcomes that could not be predicted by just looking at their components.

Consider the V-shaped formation of Canada geese in flight or schools of fish swirling like undersea tornadoes. Research has shown that to make these complex patterns, all it takes is for each goose or fish to follow a simple rule: maintain an average distance from its neighbours and move in a similar direction. Simple rules generate complexity. The branching of trees, of veins on leaves and blood vessels are all based on a simple pattern.

Recognising the limitations of reductionism, more scientists are turning to the new discipline of complexity theory. An alternative to linear, reductionist thinking, which has dominated science since Isaac Newton's time, now seems possible.

Stephen Hawking said that the science of the 21st century will be the science of complexity. Many think tanks are conducting research on complexity theory. At the Santa Fe Institute in New Mexico, scientists from a range of fields - physics, biology, psychology, mathematics, immunology, computer science - have been working on the behaviour of complex systems.

The approaches used in complexity theory are based on new mathematical techniques used in physics, biology, artificial intelligence, politics and telecommunications. This interdisciplinary framework reflects the theory's wide application.

The classification of complexity is itself complex. However, complex systems share some common features, chiefly their extremely large number of related elements. The relationships among the elements increase exponentially with their number. For example, the human brain has about 1012 (or a trillion) neurons, and each is linked by about 1,000 dendrites, which involves as much as 1015 connections.

A complex system is a functional whole comprising a very large number of interdependent and connected parts whose shifting boundaries are difficult to define precisely. Unlike a conventional system, such as an aircraft, the parts do not need to have fixed relationships, behaviour or quantities. Thus, their individual functions may also be undefined in traditional terms.

Despite the vagueness of this concept, these systems form most of our world, from living organisms to political systems, along with many inorganic natural systems such as rivers and volcanoes.

Not only is complexity theory being applied to dynamic systems such as particle physics and cosmology, but scientists have also begun to use it in the study of human intelligence. In learning, for instance, an adult gains proficiency in a second language by mastering another system of symbols, rules and structures - something like the way flocks of geese fly in formation based on simple rules.

How our adult brain, with its networks of neurons and dendrites, masters the complexity of another language by deciphering simple symbols and rules may offer clues about the nature of human intelligence.

Complexity theory has established a new paradigm in analysing how systems behave. It is accompanied by impressive literature with a specialized vocabulary to match. But, as with any new concept, there is a tendency to believe that a single theory can explain everything in the universe. This may be misleading.

Just like our lives, complexity theory is, well, complex. And that goes for any theory that attempts to explain it.

Tom Yam, a Hong Kong-based management consultant, holds a doctorate in electrical engineering and an MBA from the Wharton School of the University of Pennsylvania. He has worked at AT&T, Ernst & Young and IBM