The discovery of complexity
Networks are an essential ingredient in any complex adaptive system. In biology, molecules interact in cells, cells interact in organisms, organisms interact in ecosystems. As Eric D. Beinhocker points out in one of my favourite books, ‘The Origin of Wealth’:
“The economic world likewise depends on networks. The earth is girdled by roads, sewers, water systems, electrical grids, railroad tracks, gas lines, radio waves, television signals and fiber-optic cables. These provide the highways and byways of the matter, energy and information flowing through the open system of the economy. The economy also contains massively complex virtual networks: people interact in companies, companies interact in markets and markets interact in the global economy. Just as in biology, the networks of the economic world are arranged in hierarchies of networks within networks.”
BUT… traditional economics glossed over networks because they didn’t fit neatly into the equilibrium paradigm, whereby the economy was likened to an equilibrium system, i.e. behaving like a ball dropped into a bowl, rolling around until finally settling in a predictable place, until something external disrupts it. More recently the perfect sums have been ditched in favour of the idea that the economy is a complex adaptive system, i.e. a system of dynamically interacting parts in which micro-level interactions lead to the emergence of macro-level behaviour patterns. A single ant or water molecule is boring on its own, but naturally becomes an army or whirlpool as a byproduct of complex interactions. People are the same – the internet is the same. If a system reaches a state of equilibrium, it’s essentially dead.
Physics has likewise evolved to embrace complexity in favour of neat maths that doesn’t fit reality. The second law of thermodynamics states that entropy, a measure of disorder or randomness in a system, is always increasing. The universe as a whole is drifting from a state of order to disorder.
Our brains are made to deal with complexity, but we don’t make decisions by logically churning through every available piece of information. Instead we satisfice, taking the information we have and doing the best we can. Cognitive science has grown to recognise that we’re much better at inductive than deductive reasoning. We spot patterns and weave stories around metaphors and analogies.
Computers are the opposite, helping make up for our deductive shortfalls. It’s interesting that the rise of agile development follows the same pattern as new knowledge in physics, biology, economics and other advanced fields of discovery; as does the creation of new business models that embrace our inherent sociability and the complexity of networks. We’re no longer seeking the perfect, no longer adopting unrealistic assumptions to make the maths work out in the equilibrium framework we’ve been convinced explains everything for so long.
We know the energy inherent in what we’re doing renders equilibrium not only irrelevant, but impossible.