I am often asked the method of observing complex issues enabling us to innovate (creating something new) in a complex environment, which might be applicable to diverse domains such as engineering, maintenance, management, strategy, marketing, etc.
Given below are the fundamental principles of Observation based on the science of complexity, which help us investigate or look carefully at any complex problem in any domain and come up with new solutions that create or help support desirable outcomes. Much depends on what we choose to observe and how we see those. Therefore, the principles act as general guideposts for observing reality as accurately as possible.
The Principles of Observation of Complex Systems:
1. Interactions between elements (usually diverse) create complexity and the quality of the interactions change over time. Hence observing interactions is the first important step.
2. Quality of interactions change because some quantity within the interactions change “non-linearly“. As quantities change monotonically problems appear. This can also help us solve problems. Therefore, observe what changes non-linearly.
3. The “non-linearity” of all complex systems is hidden in the interactions. This changes the system behavior as a whole, which also exhibits non-linearity. The outcome might be ‘desirable’ or ‘undesirable’. So, observation of the non-linear system outcomes and their effects and patterns is the third most important step in observing complex issues.
Hence, the solution lies in taking care of non-linearity. This is not to say that non-linearity would magically transform into something linear that makes it easy for us to manage but we attempt to keep it within some limits or introduce or amplify some other non-linear phenomenon or eliminate or mitigate certain non-linearity as the case might demand. That is the essence of obtaining a solution.
Based on these three principles of observations we can see more deeply into all changes that happen in the real world. This leads us to few more principles of observation, which are the following:
4. Changes happen due to interactions of physical forces, chemical reactions or electromagnetic interactions or human interactions or interactions between nations or communities or interactions between organizations and customers or economy and the market etc.
5. These create specific patterns and shapes which if we are lucky might be captured as data or information. However, such data or information are always relational else no movement would ever happen (consider the force of walking vs the force of friction — they are in tandem – one never leaving the other — forming a movement).
6. So when we look at relational data and trend them we find a pattern. Such patterns can be of various types and shapes (like spirals, waves, cracks etc. )
7. Since real world systems work far from equilibrium conditions (that is interdependence of diverse elements – where everything affects everything else) such relational trends either run in ‘sync’ or ‘out of sync’. (that is ‘in phase’ or ‘out of phase‘ – even happens in all machines and human activities like a group of people walking in the park or even in heart cells).
8. When they are in ‘sync’ things might be normally considered as ok i.e. the system is stable. When they are out of sync (or out of phase) it indicates an impending change, which might be desirable or undesirable.
9. The impending change is predicted by a sudden amplification of one of the relational parameter that tends to bring movement to a halt (consider friction either suddenly going up or down while walking and how it would immediately stop movement).
10. Such ‘out of phase’ and ‘amplifications’ are the hidden imperfections in any complex issue, identification of which help us solve or resolve any complex issue.
11. Fortunately, we can understand complexity by starting out anywhere in the system to find out the critical pairs of relational parameters that cause movement. In all cases it is the energy that causes all movements of the system. So for machines it might be understood by temperatures or vibration or wear. Similarly for human interactions it might be considered as movement of people or money or goods or output of something — any suitable variable depending on the context and our area of interest. Suppose the movement is spiral (because of the swirling nature of the hot gases or fluids with high energy) the movement is best captured by the two temperature points which would either be in ‘sync’ or out of ‘sync’. These two temperature points form the ‘critical pair of relational parameters’.
12. By trending such ‘critical pair of relational parameters’ we attempt to make the ‘invisible’ patterns very ‘visible’ enabling us to make or take the right decisions.
I have tried to explain the 12 principles of observation based on which investigation of any complex system may be carried out. Once the patterns are understood we can then create proper algorithms that enable us to take decision quickly and precisely.
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