"Everything should be made as simple as possible, but not simpler." This maxim, attributed to Albert Einstein, bears particular relevance to how problems should be approached. There is not a simple solution to every problem. Simple problems can be solved with straightforward techniques; complex problems call for more elaborate methods.
Simple problems can generally be solved with a high degree of success by following a recipe: a process, a guideline, a best practice. Complexity arises from the number of nodes and their linkages. For instance an information system grows more complex, as the number of function points increases. Allenby calls this Static Complexity. Cause and effect relationships are readily apparent, and structured, linear techniques and processes are applicable. Examples of such techniques include single-point forecasting and operational procedures. The decision model is to sense incoming data, categorize the data, and then respond in accordance with predetermined practice (Kurtz and Snowden, 2003).
Dynamic Complexity arises as the nodes of the system interact in new and unexpected ways, changing the relative position of nodes. As cause and effect relationships are separated over time and space, complicated problems require coordination and/or expert know-how. Analytical methods and systems thinking are called for. Examples of applicable techniques include experiments, expert opinion, and scenario planning. The decision model is to sense incoming data, analyze the data, and then respond in accordance with expert advice or interpretation of that analysis (Kurtz and Snowden, 2003).
Whereas simple and even complicated problems can be addressed with linear thinking, techniques and tools, these means render inadequate when dealing with complex problems of Wicked Complexity. This type of complexity arises from the reflexivity, intentionality and evolution of human systems and institutions that creates contingency and unpredictability: "Information developed by humans about human systems is, by definition, a new part of the human system that it arises from, and thus it immediately affects and changes the underlying human system." (Allenby, 2009). Complex problems are non-linear, i.e. inputs and outputs are not directly correlated. The solution is a part of the system and it cannot be applied as a recipe to other, like problems. Cause and effect relationships between interacting agents are mutual. Emergent patterns can be discerned, but only in retrospect. The decision model in this setting is to create probes to elicit the patterns, then sense those patterns and respond by stabilizing the desirable patterns, while destabilizing the undesired ones (Kurtz and Snowden, 2003). In this context, narrative techniques are powerful, as they convey a large amount of knowledge or information in a very succinct way. Other tools for managing in a complex context include large group methods, simple rules, experimental probes, and "ritual dissent" (Snowden and Boone, 2007).
Finally, Anthropogenic Complexity arises "because humans are impacting not just local environments and resource regimes but the global framework of physical, chemical, and biological systems is new and challenging, in that no discipline or intellectual framework enabling rational understanding at that scale yet exists." (Allenby, 2009). Chaotic problems pertaining to this type of complexity have no systems-level cause and effect correlation. The decision model in this space is to act, quickly and decisively, to reduce the turbulence, sense the reaction to the intervention and respond accordingly (Kurtz and Snowden, 2003). Immediate action and direct, clear communication are of great importance.
A common failure is to consider complex and chaotic problems as reducible to their constitutive parts and to apply the same linear thinking and rational planning that work in the simple and complicated contexts. Any attempts to categorize or analyze problems in a structured way are futile. As depicted in Figure 1, the cost/effort then becomes prohibitively expensive very soon.
Figure 1. Linear techniques are not applicable to complex problems.
As illustrated in Figure 2, the problem solving techniques should be contingent on the level of complexity. Whereas linear techniques are applicable, and affordable, in the face of simple and complicated problems, non-linear techniques are needed to adequately address problems of complex and chaotic kind. Although these problems may still be expensive to deal with, non-linear techniques significantly extend the range of problems that can feasibly be solved.
Figure 2. Requisite techniques are contingent on the level of complexity.
"We cannot solve our problems with the same thinking we used when we created them." Ergo, there is much need for non-linear techniques.