Beginning to Learn Systems Thinking

I'm getting a little self conscious about how much time I spend thinking about thinking...

I’m getting a little self conscious about how much time I spend thinking about thinking…

I don’t think I’ve explained that I’m currently doing a masters in design. It is a program called “Strategic Foresight and Innovation” which is a vague and ambitious title that allows me to answer differently every time someone asks what it is. An Austrian friend asked me if we get given crystal balls when we sign up. I’m very disappointed that we didn’t.

This semester one of my classes is titled: “Understanding Systems & Systemic Design”. Anyone reading my bouncing thoughts would clearly recognize this as a topic I really enjoy. The readings are great, ranging from incomprehensibly dense to “forehead-slap” worthy. If you haven’t heard that before, it’s when you realize something that makes you hit your own forehead thinking: “oh yeah, that’s what I’ve been thinking but couldn’t say”. I’ll share some of the content as I make sense of it.

Anyway… being the keener that I am, I recently attended a systems dialogue event co-hosted by my instructor; Peter Jones and systems expert David Ing. David and Peter set up a space at OCAD University where brilliant people came and shared their knowledge and experience with newbies such as myself.  There was a paper that a few people debriefed insights to the group and then we broke out and started a discussion around the question:

Is systems thinking a science or a compliment to science? 

I really don’t know enough to understand why this is an important question. In the break out group I was in, the question gave us an opportunity to explore some very broad topics. Nothing conclusive emerged, which I’m now learning is a recurring theme of systems thinking. I’ve tried a couple of times to assemble a narrative of the thoughts, but it is too difficult, so I’ll simply give you a point form of the key ideas and I’ll try to expand.

Normal vs. Post-Normal Science

I’ve just been introduced to the idea that a new scientific world view is being discussed that recognizes it is not always possible to know all the factors in play when making a decision. Where Normal science seeks to reduce investigations into verifiable and repeatable experiments that produce quantifiable results, Post-Normal embraces uncertainties and seeks new models for communicating research. It is not a very mainstream idea, but feels quite comfortable for me when I put my “design” hat on.

I really like this diagram...From Wikipedia: English: Increasing decision stakes and systems uncertainties entail new problem solving strategies. Image based on a diagram by Funtowicz, S. and Ravetz, J. (1993) "Science for the post-normal age" Futures 25:735–55

I really like this diagram…
From Wikipedia: English: Increasing decision stakes and systems uncertainties entail new problem solving strategies. Image based on a diagram by Funtowicz, S. and Ravetz, J. (1993) “Science for the post-normal age” Futures 25:735–55

Hard Systems vs. Soft Systems

Hard systems are predictable models of complicated elements that can be used to test ideas. In a hard system the authors agree that the problem they are investigating can be fully defined and repeatedly tested against to find an optimum solution. In soft systems the authors agree that the problem will always be changing as the context is messy and fluctuating. The purpose of a hard system is to test and predict, the purpose of a soft system is to interact and learn. As more researchers embrace soft systems there becomes an increasing friction with Normal science, as the results are not conclusive in the traditional way. Someone in the group said that: “Systems thinking defines what is unknown”, and that science “defines and categorizes what is known.”

It was suggested that Systems requires “scientific” results to provide meaning and to be able to form a systems model, but needs to be independent of science in order to get valuable results.

Some fun questions that emerged:

  • Is systems thinking fluid, while scientific thinking is fixed/solid?
  • Are we having a debate between the difference of “good science” and “bad science”? Someone defined bad science as research that does not take into account context and that systems thinking is specifically used to encourage an understanding of context.
  • And finally, this discussion appears to be a tension of phenomenology – i.e. what can be observed or experience and labeled. Properties of systems tend to be emergent, abstract and difficult to “observe”. 

As you can see, things remain abstract and inconclusive. Over the rest of the semester I am making it my personal mission to gather more tangible case studies and stories which I will bounce around.

Many thanks to David Ing for letting me be the note taker and be patient as I process these thoughts. Thanks to Peter for inviting me along. Huge thanks to Jamie Miller, who is now teaching biomimicry with me at OCAD U and introduced me to Post-Normal science, and of course to Ian Clarke for his never ending mind blowing explanation of biology and ecology that makes it possible for me to play in this space.

And… I’m really hoping Peter Niewiarowski reads this… I’m sure he’s got an opinion about my understandings of science…

7 Comments on “Beginning to Learn Systems Thinking”

  1. […] breakout group.  He’s incorporated some of their conversation into his reflections on “Beginning to Learn Systems Thinking” particularly on “normal versus post-normal science” and “hard systems […]

  2. Casey says:

    Very cool. All the best in your studies!

  3. kzarsky says:

    Love this entry, Carl! Truly some “forehead slapping” moments for me here:)

    You might really enjoy this article by The Long Now Foundation about long data. I’m learning that the story of context has many narratives that are dependent upon both temporal and spatial understanding. In defining properties of systems, it’s also become clear that we do not yet have a universal language that everyone can use in the same way, i.e., an ecologist and a mathematician may use terminology quite differently. Random conversations on airplanes can be quite eye-opening.

    I’ve never experienced anything as humbling as systems thinking. The 2nd two questions you raise about context and phenomenology are very difficult to introduce into real practice with non-systems thinkers. Crafting the bigger picture, however, can be a very empowering skill if it helps others discover their strength and potential within the system(s). It also allows other to evolve their own story beyond their product or service into the voids of societal and scientific constructs.


    • Carl Hastrich says:

      Thanks Kathy – I need to add a blog of the readers we are diving into – maybe you can share on that post any of the other readers you have?
      Absolutely love the Long Now Foundation … There are a couple of fantastic things (the layers of change – speed diagram – is really great).

  4. Ian Clarke says:

    Is Systems Thinking a Science? I’ll go out on a limb here and say NO, or more realistically, not necessarily. It is your own fault Carl, you got me started, so here is the long response.

    I think we need to remember that the the goal of science is not to define and categorize what is known, but to use our current knowledge of what is know to discover and explain what is unknown. I think this common misconception is understandable when most of our science education is based on teaching the history of science. We learn what was discovered by scientists, but this is not science. It is the process of discovery that is science. Fundamental to my understanding of what it is to do science is to acknowledge my vast ignorance but to create the conditions for a whole lot of forehead slapping.

    Scientists have always thought in systems. As a molecular biologist I have often been accused of having a reductionist view of the world. However, when I think about how a gene works I also have to think how it is interacting with and regulated by 30,000+ other genes in an almost infinitely complex system. Ecologists have always studied organisms in detail as part of the complex ecosystems that they live within.

    Systems biology is at the forefront of science today not because scientists have not recognized the need to think in systems, but because they have worked with such complex systems it has only been recently that they have the computational power to model systems in toto. This of course does not mean that ALL scientists have thought is systems.

    Complex systems seem to have emergent properties, but they are still made up of individual parts that respond to their local environment and feedbacks. In ecosystems larger properties emerge out of the characteristics of their parts, we need to study both to comprehend the system. Different species and different successional histories (chance) create different stable systems.

    As designers I think we can learn a lot from systems biology. We can gain new knowledge about systems even when we have a lot of ignorance. But lets not forget that the individuals to the system too.

    • Carl Hastrich says:

      Hey Ian! Thanks so much for the reflections.
      Systems thinking linked to the capacity to process large quantities of complex data — that makes sense to me.
      I also think that reading organizational systems theory as it is applied to business management is a good insight into the dangers of forgetting the “individual”. The latest evolution of the theories appears to be trying to integrating the “one” into the “many” a lot more.
      Thanks mate

  5. I want to point you to my favorite process explanation of science –

    I wonder how much of the hard vs soft systems thinking is really just describing the culture you are in, and the value placed on different kinds of knowledge?

    I have found the idea of hard vs soft systems compelling and useful in considering and talking about different approaches – but I think it is not a question of if it is science or not, but what are the cultural or community supported attitudes, ideas, and norms that you are tuned towards? What get’s results, and over what time period does that matter?

    Evidence based cultures might have an easier time incorporating hard systems type of science, while holistic based cultures might have an easier time incorporating softer systems type of science. I also tend to think that a soft-systems approach is really a sub-set of ‘learning’ about a system and that a true ‘science’ output would be describing a hard-system type of language based on research from a soft-system inquiry (follow that?). This is why we get amazing results when designers visit a science lab- because they are asking soft systems questions that a typical ‘hard-system’ culture has not considered.

    Like Ian points out- scale is not the issue- and I think when talking about systems we have a hard time translating between cultures or even areas of concrete knowledge. In part, because I think we feel we need to explain the system – which in effect defeats the purpose. Soft systems approaches allow us to learn more without knowing all- which is needed if we are to attempt to adapt to a dynamic world, of many complexities, that take more than a lifetime to understand.

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