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cameron and green making-sense-of-change-management

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Emerging inquiriesUNDERSTANDING HOW COMPLEXITY SCIENCE APPLIESTO ORGANIZATIONAL CHANGEComplexity science has been drawn from the scientific world, <strong>and</strong>applied to organizations in an attempt to underst<strong>and</strong> <strong>and</strong> explain thebehaviour <strong>of</strong> large systems. There is no formal definition <strong>of</strong> whatcomplexity science means in an organizational context, nor indeed how itis best applied to organizations.In this discipline, large systems are <strong>of</strong>ten referred to as complex adaptivesystems. Complex adaptive systems are made up <strong>of</strong> multiple interconnectedelements, <strong>and</strong> have the capacity to <strong>change</strong> <strong>and</strong> learn from experience.Complexity science is a collection <strong>of</strong> theories which seek to explainhow these systems work. This branch <strong>of</strong> science is eclectic <strong>and</strong> draws itsideas from many other areas <strong>of</strong> science, for example the fields <strong>of</strong> neurology<strong>and</strong> microbiology. Examples <strong>of</strong> such large complex systems are communities,the stock market, the human body’s immune system <strong>and</strong> the brain.One <strong>of</strong> the most intriguing features <strong>of</strong> complex adaptive systems tothose who study them in the context <strong>of</strong> human social organization, istheir capacity to produce coherence, continuity <strong>and</strong> transformation in theabsence <strong>of</strong> any external blueprint or nominated designer. The control <strong>of</strong> acomplex adaptive system is highly dispersed <strong>and</strong> decentralized, <strong>and</strong> thewhole system’s behaviour appears to arise from competition <strong>and</strong> cooperationamong the local agents in the system, coupled with sensitivity toamplifying or dampening feedback. Even if a major part <strong>of</strong> the system isout <strong>of</strong> action, the system continues to function. A good example <strong>of</strong> this inthe field <strong>of</strong> biology is the human brain.At the Santa Fe Institute in New Mexico, where scientists have studiedthe behaviour <strong>of</strong> computer-simulated complex networks for some time,the following six characteristics <strong>of</strong> a complex system were identified:• there is no central control;• there is an inherent underlying structure within the system;• there is feedback in the system;• there is nonlinearity; things do not happen in a cause <strong>and</strong> effect manner;• emergence is an outcome <strong>of</strong> the system. This happens withoutplanned intent;312

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