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Chaos Versus Complexity

A useful way of distinguishing between chaotic and complex systems is to illustrate how uncertainty arises in each type of system. In chaotic systems, uncertainty is due to the practical inability to know the initial conditions of a system. If the initial conditions were either ‘x’ or ‘a tiny (immeasurable) variation on x’, we know that when non-linearity kicks in, this slight variation will eventually make a large difference in the outcome. But in complex systems, uncertainty is inherent in the system because of the concept of emergence: even if we could measure the initial conditions today to an infinite degree of precision (for the serious geeks: I am ignoring Heisenberg’s uncertainty principle here), we still cannot determine the future. However, as I noted in my article Patterns Amid Complexity, this does not mean the future is random because patterns are an important – and often repeating – feature of complex systems.


To conclude, it is tempting to believe that Chaos is a highly complex type of complex system. But it isn’t – Chaos theorists have revealed some excellent insights, like sensitivity to initial conditions, but Chaos Theory is still a study of deterministic systems. To understand non-deterministic systems, like social systems, it’s necessary to look at complex systems and Complexity theory.