“The definition for complex adaptive systems seems to change with the different attempts at application. In order to make a good match between a hard-to-solve problem and a complexity approach, it is important to consider whether and how the problem exhibits attributes of a complex adaptive system. Research is indicating that CAS have a number of characteristics which are described in the following subsections.
There is no single centralized control mechanism that governs system behavior. Although the inter- relationships between elements of the system produce coherence, the overall behavior usually cannot be explained merely as the sum of individual parts. As noted earlier, complexity results from the inter-relationship, inter-action and inter-connectivity of the elements within a system and between a system and its environment. This implies that a decision or action by one part within a system will influence all other related parts but not in any uniform manner.
With co-evolution, elements in a system can change based on their interactions with one another and with the environment. Additionally, patterns of behavior can change over time. In 1993, Stuart Kauffman described co-evolution with his concept of fitness landscapes. The fitness landscape for a particular system, X, consists an array of all possible survival strategies available to it. As shown in Figure 1, the landscape comprises of many peaks and valleys. The higher the peak, the greater the fitness it represents. The evolution of X can be thought of as a voyage across the fitness landscape with the goal of locating the highest peak. X can get stuck on the first peak it approaches if the strategy is incremental improvement. If X changes its strategy, other interconnected systems will respond and the landscape will undergo some change.
CAS are sensitive due to their dependence on initial conditions. Changes in the input characteristics or rules are not correlated in a linear fashion with outcomes. Small changes can have a surprisingly profound impact on overall behavior, or vice-versa, a huge upset to the system may not affect it. Starting in the 1960s, Edward Lorentz, an American physicist, studied the solutions to equations describing weather patterns. His goal was to find a long-term weather forecast. With the aid of a computer, he traced out the solutions on a screen, as shown in Figure 2, and realized that he was dealing with a radically new type of behavior pattern. Very small changes in initial conditions in the weather system can lead to unpredictable consequences, even if everything in the system is causally connected in a deterministic way. The current state of the weather is no predictor of what it will be in a couple of days time because tiny disturbances can produce exponentially divergent behavior.
The consequences of Lorentz’s mathematical discovery are profound. Because most natural processes are at least as complex as the weather, the world is fundamentally unpredictable. This means the end of scientific certainty, which is a property of “simple” systems (e.g, the ones used for electric lights, motors and electronic devices). Real systems, especially living organisms, are fundamentally unpredictable in their behavior. Long-term prediction and control are therefore believed to not be possible in complex systems.”