Chaos and Complexity Theories

It is about the tipping point at the edge. It assumes that the system is

- dynamic (the system's behaviour at one point in time influences its future behaviour, ie everything affects everything else & systems are in perpetual motion)

- nonlinear (follows an exponential rather than additive relationship)

- Some major event can be the accumulation of many small, inter-connected events, ie a chain reaction

- there is inevitable divergence of all but identical initial states as they evolve over time, but small differences in initial conditions can produce very great ones in the final phenomena. Eg from his computer model, Edward Lorenz by rounding 6 digits (0.452386) to 3 (0.452) produced greatly different weather results

Complexity theory

Complexity theory is a way of making sense of advanced technologies, globalisation, intricate markets, cultural change, etc

Organisations have gone from complicated to complex

There is duality, ie fight and oscillation between order & disorder (chaos) that can result in events seeming both very predictable & very unpredictable at the same time

There are many diverse, interdependent parts interacting and they are in constant flux so that the final outcome is unknown, ie very simple things can behave in strange & mysterious ways when they interact with one another

The same starting conditions may yield different results

Seemingly simple actions may produce unexpected and/or unintended consequences, ie long periods of apparent stasis marked by sudden change that are very hard to predict

Rare events are becoming more significant than average ones

This concept applies to a system which has the following characteristics

"... - a lot of interacting elements - the interactions are nonlinear, and minor changes can produce disproportionately major consequences

- the system is dynamic, the whole is greater than the sum of its parts, and solutions cannot be imposed; rather, they arise from the circumstances. This is frequently referred to as emergence

- the system has a history, and the past is integrated with the present; the elements evolve with one another and with the environment; and evolution is irreversible

- a complex system may, in retrospect, appear to be ordered and predictable; hindsight does not lead to foresight because the external conditions and systems constantly change

- unlike in ordered systems (where the system constrains the agents), or chaotic systems (where there are no constraints), in a complex system the agents and the system constrain one another, especially time. This means that we cannot forecast or predict what will happen......More recently, some thinkers and practitioners have started to argue that human complex systems are very different to those in nature and cannot be modeled in the same way because of human unpredictability and intellect. Consider the following ways in which humans are distinct from other animals:

- they have multiple identities and can fluidly switch between them without conscious thought. (For example, a person can be a respected member of the community as well as a terrorist)

- they can, in certain circumstances purposefully change the systems in which they operate to equilibrium states (think of the six Sigma project) in order to create predictable outcomes..."

David T Snowden et al, 2007

. Need to be careful of assuming that

- statistical correlation means causation

- misleading noise, ie random patterns that can be mistaken for signals, ie data & modeling can hide the true signal/phenomena/trend, etc. For example, the complexity (including variability, risk, uncertainty, etc) of economic data has created much noise through daily, cyclic, seasonal fluctuations, etc that can hide the trends &/or generate conflicting meanings

- usually all the signals are present but we cannot read them correctly. The problem is not a lack of information but a failure of accurate prediction

- importance of luck

Some Impacts of Chaos & Complexity

. Use simplification, generalisation &/or approximations to understand complex events, eg making assumptions about key factors, rounding off figures, etc. This can be very misleading. This makes accurate predictions, about weather, economic/business cycles, recessions, political outcomes, etc very difficult to achieve, eg

- weather forecasting

i) It is a dynamic system ie everything affects everything else & systems are in perpetual motion

ii) Uncertain initial conditions

iii) Poor data

- economic forecasting

i) Hard to determine cause & effect from economic statistics alone

ii) Economy is not static, so some past explanations may not hold for future situations

iii) Impact of political decisions

iv) Lack of accurate data despite the huge amounts produced, eg the US Govt produces around 45,000 economic indicators annually plus private providers are supplying 4+ million.

. On the other hand, simplification, etc is powerful if it gives better initial understanding of the situation. Then we need to explore the impact of changing the assumptions (especially if they fail) used in the simplification.

. Change is like weather & economic forecasting, ie very hard to make accurate prediction, ie

"...there are too many factors to lay down fixed rules..."

David Hains as quoted by Andrew Cornell, 2009a

 

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