Summary on systems thinking
Topic: Systems thinking
by PJ, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
When a toy slinky is tipped down a staircase, the interconnectedness between all its coils is what causes the slinky to begin and maintain its dissent. This particular pattern of movement cannot be attributed to each individual coil.
Slinkys demonstrate the important relationship between the structure and behaviour of a system. It is an example taken from the article Thinking in Systems by Donella H. Meadows, an author who has made me approach systems with a keen-eye for interconnectedness.
Meadows raises a number of fascinating insights into why we should focus on the sum of the parts, rather than the parts. I consider her insights relevant to most aspects of my life, as I now notice systems when cooking dinner, learning in a classroom or working as an Uber driver.
Meadows asserts there is a problem with discussing systems only with words. This is because, unlike language, she considers systems to be non-linear. Language is a limited tool because it builds in a strictly progressive direction. Systems on the other hand, produce progressive and regressive outcomes. If you are thinking well then why is the English language sometimes so non-nonsensical if it is linear?, that is because of poor design, rather than lack there-of. A system, such as climate, on the other hand, will behave according to its non-linear structure. It is controlled by a web of variables environmental and human- to the infinity. Therefore, words should be supported by describing tools such as graphs, videos and pictures. Imagine a weather report with only
This idea of non-linearity seams to relate to a different idea raised by Meadows. That is, well-meaning actions too often add up to a perfectly terrible result. Systems, namely climatic ones, can be so beautiful and yet so confounding. Take the unexpected Hurricane Harvey in 2017, inflicting 82 deaths and at least US$125 billion in damage, primarily from catastrophic rainfall that triggered flooding in Houston. If systems were linear, then the well-meaning actions by scientists predicting that the hurricane was was going to miss Houston, would have resulted in the realisation of exactly that. Instead, Houston was hit That is non-linear.
The story of the Elephant, told by Meadows, made me think about weather scientists constantly seeking the big picture relative to the site they are studying. More generally, this story reveals the utility of exercising systems thinking. The story is about a group of blind soldiers discovering a still elephant while walking through the desert. Each group member forms a different opinion about what exactly they have just encountered a threat, a trove, a rug, a pillar? Each body part said one thing, and the sum of the parts said another.
It is this interconnectedness between elements of a system that is so interesting, beautiful and worthy of study.
Disclaimer#
This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au