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Complex Adaptive Systems
In systems such as economies or stock markets, behavior often changes as a result of the behavior of others and of experiential learning (feedback loop). These systems therefore become difficult to predict as unexpected (emergent) properties may emerge.
A complex system involves large numbers of interacting elements. Interactions are non-linear. Consider a steady stream of sand grains that randomly pile up on top of each other. At some point the pile of sand reaches a critical state. The next grain of sand causes the pile to topple over and one has to start again. In this way, the sand grains are similar to a number of other systems, in that when they have reached the critical state the slightest change (shock) will cause the system to malfunction. Further, the smallest change can produce the most radical malfunction and this change may be unavoidable and unforeseeable.
The system is dynamic, the whole is greater than the sum of its parts, and solutions can’t be imposed; rather they arise from the circumstances — emergence.
The system has a history, and the past is integrated with the present; the elements evolve with one another and with the environment; and evolution is irreversible.
Though a complex system may, in retrospect, appear to be ordered and predictable, hindsight does not lead to foresight because the external…
