# Difference between revisions of "Complexity"

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− | [[Complexity]] seem to be an emergent behavior of the natural selection of small changes that are more information and energy | + | [[Complexity]] seem to be an emergent behavior of the natural selection of small changes that are more efficient in the use of information and energy. |

==Historical Context== | ==Historical Context== |

## Revision as of 10:30, 29 August 2020

## Full Title

**Complexity** seem to be an emergent behavior of the natural selection of small changes that are more efficient in the use of information and energy.

## Historical Context

- What has once been a simple Homeostatic ecosystem will try to become more efficient at using the resources available to it. Originally the resources were land and human labor.
- Archaeology has taught us that the rise and fall of complex societies
^{[1]}has been evident throughout human history. - Joseph Tainter observed
^{[2]}that each new level of social complexity is added at a cost, if only a cost in surplus production. As societies become more complex the marginal returns for increasing complexity increase. Collapse is often a result of the diminishing value of complexity

## Complex Adaptive Systems (CAS)

The emergence of complexity has been mathematically modeled as a CAS. The challenge in the real-world is that quantitative measurements are not always avialable.Concurrent high levels of uncertainty with respect to the precision with which variables or situations can be measured and assessed are also a problem. One approach to CAS is to use biological ecosystems to illuminate organisational processes. Dooley (1997) is perhaps the most widely cited example of this approach. CAS has also been used in health informatics research (e.g. Day & Norris, 2007; Ward, Stevens, Brentnall, & Briddon, 2008). While the use of biological concepts such as ecosystems and autopoesis (self organising systems) are described and applied in Dooley’s paper, there is an attempt to examine the underlying role of complexity and interdependence inherent in the CAS view. However, the biological ecosystem view of organisations has been justifiably criticised for its lack of clear connection between the biological concept of species and a corresponding unit of construction in human organisations (Young, 1988). Nonetheless, there is some recognition of the potential of CAS in the human sciences, as well as in the field of health informatics. Therefore, we will attempt to apply the CAS to our domain of study. We start with the assumption that while the biological approach to analysis of organisations is informative, there are no direct correspondences. That is, we assume the underlying phenomenology of things like resource limitation, the unit of information (i.e. DNA in biology, an unknown entity in organisation studies) between the two fields are sufficiently different so as to be not directly comparable. Ecosystems and organisations are both constrained by resource limitations, by the internal structure of their interacting components and by their relationship to their external environment. However the economics of the underlying resources are substantially different. While ecosystems are generally limited by nutrient availability, the resource limitations for human organisations are material, financial and human. What is common between the two systems is that the flow of these resources are important drivers of change and homeostasis. Therefore it appears that a direct analysis of the dynamic processes that underlie resource flows should be useful in defining a more robust conceptual basis for organisational ecology. Baranger (2002) provides an excellent non-technical summary of complexity theory which is outlined in the remainder of this section. Because Baranger’s disciplinary perspective is from theoretical physics, while his writing remains close to the mathematical underpinnings of complexity theory, his grounding in an application, along with his clear teaching skills is very instructive, as it provides us with a clear logical explanation of how to link the abstract mathematics of complexity to an applied dimension. Complex Adoptive Systems are difficult to understand because of the interaction between two fundamental components – chaos and complexity. Chaos can be a property of simple systems (i.e. systems with few parameters), and the results of chaotic models are by definition intrinsically unpredictable. Baranger states that chaos is “that part of mathematics where calculus does not apply”. One of the defining features of chaos is sensitivity dependent on initial conditions (e.g. in our study it may be that the initial training approach can vary between units in small ways, but that these small differences might have dramatic consequences). Complexity is different from chaos. The human body, weather patterns, and ecosystems are all examples of complex systems where the individual constituents self-organise, and the whole is greater than the sum of its parts. Emergence (as in emergent properties) is a phenomenon stemming from complexity where the organisation and interactions at one level of a system cause changes at another level. A system whose configuration is capable of changing over time is called a dynamic system. A dynamic model is a mathematical model or a set of rules describing the time dependence of a point's position in space (either physical space or a more abstract idea of space). A simple example of a dynamical system as would be described in any introductory physics book is the swinging of a pendulum. Chaos has a close relationship with complexity. Complexity has the property of multiple interacting components each of which may or may not be chaotic subcomponents. The network of interactions is compounded by stochasticity (probabilistically determined variation). In thermodynamics, the statistical model of probabilistic variation is described by the concept of entropy. An adaptive system is one which interacts with itself and its environment to achieve an end. A simple example of an adaptive system is Stevenson’s governor – a mechanical mechanism that prevents excessive speeding of engines. A more complex example is that of homeostatic systems in living systems, for example body temperature regulation. Entropy is an important part of any system as it helps define whether a system is closed (independent) or open (dependent on other systems). In thermodynamics, the entropy (degree of disorder) of a closed system increases over time. High entropy systems have high levels of disorder, and the components of a high entropy system are generally seen as possessing disorder whose atomic configuration are uninteresting. However, the effects of a transient increase in entropy can be interesting. A substantial outage of the electronic documentation system of our study site is a good example of a transient increase in the rate of the accumulation of entropy, which will be discussed next. One fascinating property of entropy is that even in the physical sciences, it is a constructed concept, which is used to make “reality” more manageable. The smoothing procedure used for entropy analysis defines the scale beyond which the analyst is unable or unwilling to keep track of details. Smoothing represents a self-imposed (subjective) increase in the entropy of the system – the key to understanding this procedure is to optimise the level of analysis at which it is performed. As our data consist of individual interviews, we need to understand the nature and quality of the data we gather, and at what level we maximise its meaning. This in turn allows us to improve our understanding of the flow of resources within the organisation. Complex Adoptive Systems’ quantitative roots do not exclude its use for solving qualitative problems. For example, a quantitative problem in electronics would be to calculate the change in voltage in a lighting circuit when a change occurs. A qualitative equivalent would be to determine whether the light bulb becomes dimmer or brighter as a result of that change. It should be clear that where the number of parameters is high, or measurement is uncertain, or where a chaotic system is suspected, a qualitative solution will be more achievable and likely more desirable. This brief summary should illustrate that CAS as an ontology (framework to generate meaning) is capable of bridging the divide between positivist and post-positivist. That is, between the perspective that there is a “true” reality versus the idea of a socially constructed reality (Lichtenstein, 2000). In the search for improved understanding in social research we need to evaluate this way of looking at things in order to determine how useful it is, and to determine whether this lower level of CAS compared to the organisation as ecosystem approach is useful in providing explanations of change processes.