CONTROL

Control depends on the relationship between cause & effect.

      Most people understand the concept of cause and effect. In fact, many adults live their lives dependent on the belief that the output, or result, of an event is strictly determined by the input, or cause of the event. However, the recognition and understanding of complex systems should cause one to question the veracity of such an oversimplified concept as cause and effect.

      Not all outcomes are input determined. There are several reasons for this. In some cases the results of an input cannot be known. In other cases, there are more inputs to a system (hidden or unexpected inputs) beyond those that are or can be identified. And in yet other situations, there are internal dynamics in the system that produce different results even when the same inputs are applied.

      The variance of perception from reality is one of the major stumbling blocks encountered when trying to predict outcomes. Although it is frequently believed that one can control an outcome through the control of all of the inputs that can determine the outcome, there is a flaw that centers around the perception of control. Rarely does one actually control all of the determinants of an outcome. It is wrong to confuse the appearance of control with actual total control. It is normal to think that if one controls the majority of the input, one should be able to control the outcome. Yet, majority control is different than absolute control. Sometimes the smallest, seemingly insignificant factor can cause a major change in outcomes. This is known in chaos theory as the butterfly effect. Known as "sensitive dependence on initial conditions", minute differences in input can cause overwhelming differences in output. The world is not as simple as we wish. Systems that never repeat themselves, aperiodic systems, nonlinear systems, are prone to constant, unpredictable change, regardless of the initial conditions.

      In the rare case when it appears that there is absolute control over all the input, the Theory of Volatility introduces that niggling little problem of random motion which can put events into play that are beyond anyone's control. In other words, the lack of linearity in a complex system means, by definition, that the predictability of the outcome is not entirely input dependent.

      The dynamics of complex systems do not totally negate the concept of cause and effect. They just raise the level of uncertainty from a largely determinant method of specific prediction based on specific cause, to a framework of probability that describes the likelihood of a given outcome without the ability to produce a guarantee in any particular case.