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that it is good at doing the appropriate medical or surgical interventions. The same people given the right training and tasked with organizing the introduction of a new computer system might struggle to work as an effective team simply because the relationships required are likely to be very different. Two major criticisms of General Systems Theory are worth reflecting on at this point – one is that it is too general, and the other is that it is not really a theory. Bertalanffy was keen to insist that the aim of GST was not to provide a general theory of everything that would be so general as to have no practical application [2]. In his view, GST should provide a perspective where it is useful in providing a language or a framework for thinking about and discussing systems, particularly between disciplines that could benefit from sharing fresh ideas. While there are a dizzying range of potential hierarchies of interacting systems and sub systems in our surgical team example, a sensible use of GST is to focus on just those systems where a systems perspective generates fresh and useful insight. The second criticism of GST as “not really a theory” has some foundation. Bertalanffy himself argued that GST was conceived as a working hypothesis, a goal rather than a clear axiom [5]. Tom Mandel in “Yes, there is a general system principle, No it is not a theory” [6] makes a fair case for GST being regarded as a principle although the counter argument might be that the theory is that the principle applies. Semantics aside, it is perhaps best to regard GST, as Bertalanffy intended, as a “theoretical model” whose value lies in the practical “explanation, prediction and control of hitherto unexplored phenomenon” [5, pg 99]. 1.3. Extensions to General System Theory In Advances in General System Theory [5], Bertalanffy explored how the explosion of post-war systems approaches might fit with GST to provide a broader general theory of systems developing the principles of communication and control that describe how systems work. Shannon’s Information Theory introduced the concept of information as quantity and “negative entropy” (information reduces uncertainty) and developed the principles for describing information transmission used in computer science2. Systems use information from their environment to reduce uncertainty about the range of appropriate responses, for example, when a medic uses diagnostic results to rule out possible diseases, narrowing down the options to identify the most likely disease and decide on the best treatment. Cybernetics, based on the role of information feedback in circular causal chains, helps explain how systems can be self-controlling. As early as 1948, William Ross Ashby applied cybernetic principles to build a synthetic brain, called the Homeostat, from four interlinked air force bomb control units that worked together as a system to maintain homeostasis through reinforcement and learning. Ross Ashby’s Law of Requisite Variety is useful here; the survival of a system over time depends on it retaining sufficient (requisite) variety in its internal structure to respond to the variety in its environment; systems fail when they are unable to adapt to their environment. Game Theory describes logical decision making in humans, animals, and computers and provides insights into how some systems are maintained through competition between components where each component competes to maximize gain and minimize loss. In organizational systems, market forces often dominate - students compete for higher marks, professionals compete for salary, roles and kudos, and both private and public organizations compete for work and resources. GST includes the idea that relationships 2 Discussed further in Chapter 3, “Information theory and medical decision making”. O.Johnson /GeneralSystemTheoryand theUseofProcessMining to ImproveCarePathways14