Science and research has led to the technically advanced society that we enjoy today. The return on investment for effort in thinking and research can be staggering. For example, the return on investment at the laboratory where I worked for nearly 25 years was over three hundred percent. The value of research is hidden by annual financial accounting cycles because it can take years or even decades for the outcomes to emerge in the marketplace. People are generally mystified by science as illustrated by the jokes about reclusive people in white coats.
Reductionist science, that saw the great advances in Physics, is based on a process of extracting a component from the system of interest, then applying a cycle of developing an hypothesis, testing the hypothesis by experiment, modifying the hypothesis and retesting until the theory becomes an acceptable description of the behaviour of the component under study. The role of mental models in the process is very clear. Mental models hold a huge place in the way we all view the world and the knowledge that helps us live in that world.
As we move to very complex systems, this reductionist approach has difficulty in explaining phenomena because the interactions between components in the system cannot be unambiguously defined. It is necessary to step back and take a more holistic view. These challenges are faced in biological science, medical science, agricultural science, eco science and economics. We all need to question the basis and models involved in all information provided to us.
Systems
More attention to an analysis of systems and their behaviour is necessary for improving the effectiveness of much of what we do.
The complexity of a system will determine the approach needed to examine that system. Dave Snowdon, a Welsh knowledge scientist introduced what he called the Cynefin framework to examine the behaviour of and approach to business systems in different quadrants of complexity. Ideas developed in this concept may have relevance to consideration of systems generally.
We have become very familiar with Simple Systems through Physics and the laws that underpin our modern technological world. Here reductionist science writes the relationship between cause and effect (input and output) as a mathematical equation to predict outcomes in new situations. The role of measurement and standards in this world is extremely important. For simple systems involving people, such as a small business, Snowdon indicates that it is relatively easy to establish “best practice” because of the predictability of the outcome.
Complicated Systems are familiar in the world of engineering, where an array of interconnected components form the system. These systems require the application of expert knowledge and analysis to establish the relationship between cause and effect. For example, the input of an electric current to an electric motor is transformed to shaft rotation and a mathematical equation can be written to calculate the mechanical output from the electrical input. The mathematical equations for each component can be built into a set of equations to calculate the overall behaviour of the system and the important role of feedback can be established. Feedback between the output and input is a very important concept in engineering and leads naturally to an assessment of stability. Cases of a poor choice of component parameters and inputs, can result in undesirable outputs which in extreme cases can become destructive (e.g. Tacoma Narrows bridge disaster). The engineering world illustrates the huge benefits that can be achieved in understanding the structure of a system and working to develop equations to relate the outputs to inputs both of components and the system as a whole. Mathematical models provide an ability to harmlessly exercise parameters in a system to assess the key causes of instability.
In systems involving people, Snowdon talks of “good practice” suggesting that instability needs to be considered in business too.
In a Complex System, the relationship between cause and effect is not unique and is typically determined in retrospect. Small changes within components of the system can result in much larger effects on the output of the system as a whole. The widely referenced “butterfly effect” is a classic illustration of this phenomenon. Models, mathematical tools, data sources and pattern recognition techniques are improving to provide better insights into the behaviour of complex systems.
Large systems in the natural world (ecosystems and weather) and global finance can be described as complex as it is more difficult to identify components and the many interactions between them than is generally the case in the engineering world. In the case of the Global Financial Crisis, it would appear that inadequate control of a greedy financial sector was exercised. We face similar issues with the effect of the human population on the Earth as too little attention by international leadership is placed on appropriate policies to moderate both the population and the resources individuals consume, simply because the focus is on economic growth. In both cases, better models and systems framework is required to exercise appropriate feedback mechanisms by many individuals co-operating to bring about meaningful change.
In a business environment, Snowdon uses the term “emergent practice” and this implies that evolving knowledge is a key ingredient to achieve improvement.
A Chaotic System, has no relationship between cause and effect at a systems level. Jim Al-Khalili, Professor of Theoretical Physics at the University of Surrey presented a BBC documentary “The Secret Life of Chaos” where he looked into chaos through the question of “how did we get here?” and the great mystery of “how does a universe that starts off as dust, end up with intelligent life?” He observes that the application of feedback in a closed system can lead to unpredictable patterns over time as a result of tiny errors that creep in between inputs and outputs in the system. He also observes that chaos is an “intrinsic part of the laws of physics” and the “ mathematics of chaos can explain how and why the universe creates exquisite order and pattern and the best thing is that one doesn’t need to be a scientist to understand it.” In a business environment, Snowdon indicates that an organisation in this state requires strong leadership and “novel practice” is a potential outcome.
The message appears to be for us to keep alert to the level of complexity of the system. It does raise the question of the extent to which knowledge and understanding can reduce the perceived complexity of the system and to what extent growth in skills though attempting to improve maturity can help move us toward “Improved practice”.
Sustainability
The evolving processes of science, understanding systems and dealing with complexity, each contribute to our approaches to achieve sustainability.
