Painful Multi-Symptom Disorders: A Systems Perspective

A is a group of independent but interrelated elements comprising a unified whole. A collection of elements comprises a complex system if open and dynamic connections and interactions exist between its components and contribute to the behavior of the collective. An open system is one that exists far from energetic equilibrium; that is, it takes in energy and expels waste. Formally, a complex system is any open system that involves a number of elements arranged in a structure and that requires many scales for adequate measurement. Such systems go through processes of change that defy description by a single rule or by reduction to a single level of explanation. Complexity is a scientific approach for studying how the interacting parts of a system produce collective behaviors more complex than the sum of the contributing parts, and how the system responds to, and interacts with, its environment. Complexity theory provides a framework and language for describing and modeling such processes. Complexity as a science studies the behavior of complex systems as a class using mathematical tools such as differential equations, graph theory, neural networks, time series analyses, and genetic algorithms as well as descriptive, predictive, and simulation modeling. Complexity theorists differ from conventional scientists in that they address unpredictable, nondeterministic processes within systems that do not decompose into simpler elements. This allows them to engage natural phenomena in natural settings more readily than their conventional science counterparts. Broadly, the complexity approach employs multi-scale descriptors to characterize dynamic systems and their phenomena. Applications include the study of economies, ecologies, weather, traffic flow, social organizations, and cultures, in addition to such physiological processes as gene and immune networks. When living organisms engaged in adaptation and survival are the systems of interest, then complexity analysis falls under the heading of complex adaptive systems (CAS) (Gell-Mann 1994; Kelso 1998; Kaneko 2006). Such systems are purposeful, pro-creative and pro-active in relationship to their environments rather than simply reactive. An insect hive exemplifies a CAS, as does the immune system. When elements of a system are of interest, for example worker ants within an ant colony or antigen-presenting cells within the immune system, then modelers may designate the CAS as individual based or CASs manifest ever-changing, self-organizing behavior in response to a variable environment, and they move toward, but never sustain, equilibrium. In a classic paper, Prigogne and Stengers (1984) termed this behavior “order through fluctuations.”