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Assessing Risks in Systems Operating in Complex and Dynamic Environments
Published in Eirik Albrechtsen, Denis Besnard, Oil and Gas, Technology and Humans, 2018
In everyday life, ‘complex’ is often used to emphasize or highlight the idea of ‘complicated’ or ‘difficult’, apparently without any specific meaning other than the idea that something is far from trivial to resolve and manage. However, in socio-technical systems, complexity is a property that can produce unexpected patterns of behaviour. These patterns of behaviour are due to the number of components, their nature and the changing relationships between them. Because they are difficult to fully predict, complex phenomena (and systems) are sometimes characterized as residing ‘at the edge of chaos’ (for example, Sawyer, 2005: 3). Although a state of chaos renders any assumptions about risk worthless, it is a state most socio-technical systems are rarely expected to enter – and if they do, it is called an accident or incident. Nevertheless, complexity implies an unexpected proximity to chaos, which may be (dangerously) overlooked by traditional indicators. At the same time, complexity has a salutogenic potential, in that it provides and maintains flexibility and offers favourable conditions for adaptations and transformations resulting from human and organizational actions.
Leadership and Complexity
Published in Francesco Varanin, Walter Ginevri, Projects and Complexity, 2012
Complex systems approaching the edge of chaos exhibit a tendency to self-organize. This feature is neither good nor bad in itself: self-organization can either be of help in achieving the project goal or completely counterproductive. The important fact is that once started, self-organization is difficult to be either stopped or modified. Leaders should therefore pay great attention during the initial phase of the project, when self-organization is more likely to arise, in order to promote those forms that are really functional to the objectives. All the tools described in this chapter can be used.
Setting the Stage: Complex Systems, Emergence and Evolution
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
Complex systems are known to sometimes go into chaos. Derived from ancient Greek [145], it describes a state that lacks order or even predictability. Langton [114] coined the term ‘edge of chaos’, which was used to describe the point at which system starts exhibiting chaotic behavior, or the point at which it becomes extremely sensitive to initial conditions. This sensitivity sometimes produces bifurcations (or branches into two possible behaviors) that are difficult to predict (Figure 1.3).
Task complexity and operational risk management in military aviation
Published in Ergonomics, 2022
George Androulakis, Tom Kontogiannis, Stathis Malakis
An interesting definition of complexity has been proposed by Flach (2012) who uses the state-space approach where a system is defined by its process parameters. The number of parameters and the number of states that a system may assume under different situations is referred to as ‘dimesionality’ whereas the interactions between the different system states are referred to as ‘interdependence’. Flach (2012) has also looked into aspects of ‘circular causality’ where the distinctions between causes and effects are blurred. Circular causality characterises ‘closed’ systems and associated methods of feedback control which have been extensively studied by system dynamics. The implication here is that complexity may be studied in association to relevant approaches of system control (e.g. feed-forward vs feedback control, hierarchical vs network-centric control). More insights into the relationship between complexity and control modes can be found in the work of S.W. Dekker (2013). The performance of complex systems is typically optimised at the edge of chaos, just before system behaviour becomes unrecognisably turbulent which restricts the window for adaptations. Complex systems are usually controlled through incrementalism, that is, a step-wise normalisation where each next intervention is only a small deviation from previously accepted safety boundary, hence leading to a drift to failure (S.W. Dekker 2013).
Coordination of teams, meetings, and managerial processes in construction projects: using a lean and complex adaptive mechanism
Published in Production Planning & Control, 2019
PCF regulation involves a dynamic target. This target considers a growing structure (B size) moving towards the edge of chaos (Figure 25(b)). It also involves an area (A) along the pull line, which is complex, adaptive and moves with B over time. For example, the relationship between coordination (positive, complex prone), adaption (positive, flexible prone) and articulation (negative, lack prone) with performance, suggests improvement moving in this way (Tables 17 and 18). This view assumes optimum adaptation arises with critical self-organization, within the system boundary, and at the edge of chaos (ibid).