A System is a way of looking at the world.
What is a system? I like Gerald Weinberg’s (1975) answer to this question, that “a system is a way of looking at the world.” This emphasizes that a system is analogous to a piece of art – it is a representation or model that an observer creates. It emphasizes that the system is not an ‘objective’ thing that exists independent of the observer. Rather, a system is a representation. As a representation it will make some things about the phenomenon being represented salient, and it will hide other things. I’ve long understood this position intellectually – but it has taken me much longer to appreciate the deeper meaning and the practical implications of this definition.
Early in my career, I was exposed to control theory due to working with Rich Jagacinski to model human tracking performance as a graduate student. Ever since, I see closed-loop systems everywhere I look. It seemed obvious to me that the language of control theory and the various representations (e.g., time histories, Bode plots of frequency response characteristics, state space diagrams) provided unique and valuable insights into nature. And I have bored countless students and colleagues to tears as I tried to explain the implications of gains and time delays for stability in closed-loop systems. The power of control theory led to an arrogant sense that I had a privileged view of nature! I sought out those who shared this perspective, and I expended a lot of energy to convince other social scientists that the language of control theory was essential for understanding human performance.
The mistake was not to believe that control theory is a valuable lens for exploring nature. The mistake was to think that it is the best or only path. My infatuation for control theory colored everything I looked at. Everything I observed, every paper I read, every debate or discussion with a colleague was filtered through the logic of control theory. I classified people with respect to whether they ‘got it’ or not! I tended to discount everything that I could not frame in the context of control theory. The problem was that I was so intent on preaching the ‘truth’ of control theory, that I stopped listening to other perspectives.
The deeper implication of Weinberg’s definition is captured in his principle that
“the things we see more frequently are more frequent: 1) because there is some physical reason to favor certain states; or 2) because there is some mental reason.”
While I still believe that control theory captures some important aspects of nature, I now realize that the reason I see it everywhere, and the reason that I dismiss other perspectives is in part due to my own mental fixation. I now realize that it is impossible to separate these two possibilities from within control theory. You simply can’t tell whether your observations reflect natural constraints of the phenomenon or whether they reflect constraints of your perspective – if you only stand in one place. This is nicely illustrated by the Ames room illusion.
This is important because I am not the only victim. Over my career, I have watched others get locked into specific perspectives and have observed vicious debates as people defend one perspective against another. In an Either/Or world, there is a sense that only one perspective can be ‘true.’ So, if my perspective is right, yours must be wrong. I’ve watched constructivists war with ecological psychologists. I saw the development of nonlinear perspectives, and suddenly everything in nature was nonlinear – and all the insights from linear control theory were dismissed.
Gradually, I have come to understand that an important implication of the first principle of General Systems Thinking is:
To be humble.
Nature is incredibly complex relative to our sensemaking capabilities. Any representation or model that will makes sense to us, will only capture part of that complexity. Thus, every representation will be biased in some way. But also, many different perspectives can be valid in different ways. The challenge of General Systems Thinking is to be a better, more generous listener. Don’t let your skill with a particular perspective or a particular set of analytical tools blind you to the potential value of other perspectives. This is not simply about listening to other scientific perspectives. This is not simply about a debate between constructivists and ecologists, or between linear and nonlinear analytical tools. This is about listening to other forms of experience. Listening to the poets and artists. Listening to domain practitioners, listening to people from all levels of an organization.
In some sense General Systems Thinking is an attempt to find a balance between openness and skepticism. On the one hand, we need to be skeptical about all perspectives or models - including our own. On the other hand, we should be open to the potential values of different models and we need to be capable of using multiple models as we seek to distinguish the constraints that are intrinsic to the phenomenon of interest from the constraints of specific perspectives on that phenomenon. Our enthusiasm for the perspectives that we find to be most useful should be tempered by an appreciation of the potential of other perspectives and an openness to the insights that they offer. We need to move beyond the Either/Or debates to embrace a Both/And attitude of collaboration.
I can’t stop seeing closed-loop systems everywhere I look, and I will continue to share my passion for control theory with those who are interested, but I am working to temper my enthusiasm; to be a better listener; and to be a more generous colleague.
In sum, a system is a representation created by an observer; and General Systems Thinking is a warning against getting trapped by a narrow perspective on nature. It is a reminder that nature is far too complex to be fully captured within any single representation that we could create or that we could grasp. If you aren't boggled by the complexity of nature, you aren't looking carefully enough.
Very well done.
My predilection is to look for patterns and network linkages.
The question is, "Do I look for them a priori? Or do I look for them because they made sense of what I was seeing?
And because I had no formal training in Systems Thinking, I came up with my own definition.
"A system is an interdependent network of functions within a social or organizational structure."
It applies to what I see.
Very well done.
My predilection is to look for patterns and network linkages.
The question is, "Do I look for them a priori? Or do I look for them because they made sense of what I was seeing?
And because I had no formal training in Systems Thinking, I came up with my own definition.
"A system is an interdependent network of functions within a social or organizational structure."
It applies to what I see.
Thanks. I agree. (Nice drawings, by the way, like the "Klare Lijn").
"A system is anything the customers says it is", I read years ago about "What, if anything, is a Relation Database Management System".
I've always said, when you don't know, use the word "system" and you'll get away with it.
"Perceiving" is the key word in thinking; thinking divides perceptions in useful elements.
Thanks. I agree. (Nice drawings, by the way, like the "Klare Lijn").
"A system is anything the customers says it is", I read years ago about "What, if anything, is a Relation Database Management System".
I've always said, when you don't know, use the word "system" and you'll get away with it.
"Perceiving" is the key word in thinking; thinking divides perceptions in useful elements.
Systems do, in fact, exist in real life... But not in nature - they're born in the minds of humans. Most often engineers.
If I construct a pendulum or say a Newton's Cradle I have created a simple system that certainly exists in real life. Likewise an engine, a robot or any machine.
It's when we are hubristic enough to think we can so simply understand natural ecosystems developed over millennia that the trouble begins!
I mention this to avoid a confusion but also to point out that it is our minds that. Necessarily simply.. and so often when dealing with the works of nature (and sometimes others) over simplify. Mistaking our mental map for the, richer, external, reality.
Hence the wisdom of your call for, and the place for, humility.
Systems do, in fact, exist in real life... But not in nature - they're born in the minds of humans. Most often engineers.
If I construct a pendulum or say a Newton's Cradle I have created a simple system that certainly exists in real life. Likewise an engine, a robot or any machine.
It's when we are hubristic enough to think we can so simply understand natural ecosystems developed over millennia that the trouble begins!
I mention this to avoid a confusion but also to point out that it is our minds that. Necessarily simply.. and so often when dealing with the works of nature (and sometimes others) over simplify. Mistaking our mental map for the, richer, external, reality.
Hence the wisdom of your call for, and the place for, humility.
To determine arbitrarily what a system is, attributing it a meaning and assigning a function to it is not a scientific approach to systems.
This approaches removes the capacity to benefit from systems science in understanding its fundamentals, and to simplify our understanding of it.
Systems develop naturally within their environment as stable structures during their lifecycle, and acquire their identity, boundaries and properties. They only depend on their internal dynamics. They do not have a function, they just do whatever they do, in the niche where they maintain their utility in equilibrium with their environment.
Arbitrarily trying to define systems complexifies the view of the structure and interfaces of the system, as they would not correspond to the actual processes at play. The analysis of systems becomes subjective to fit to an intention, a perception, not to deep dive into its reality. We see the whole with distorted boundaries, properties and interactions, that lose the coherence with the an objective view of the system as it arises in its environment.
This is the issue with defining a system as a representation created by the observer. We build an arbitrary model that is not in line with the actual system, and we analyse the model, not the system.
Tackling with the complexity of nature cannot be achieved by making its representation misaligned on the underlying reality.
In artificial systems, we are plagued with this issue of arbitrarily imposing a function, boundaries and interfaces that fit more or less with the surrounding reality. We only make them more complex. But nature does not do that. We need to uncover its systems as they are intrinsically,and coherently across its inner levels of organisation.
To determine arbitrarily what a system is, attributing it a meaning and assigning a function to it is not a scientific approach to systems.
This approaches removes the capacity to benefit from systems science in understanding its fundamentals, and to simplify our understanding of it.
Systems develop naturally within their environment as stable structures during their lifecycle, and acquire their identity, boundaries and properties. They only depend on their internal dynamics. They do not have a function, they just do whatever they do, in the niche where they maintain their utility in equilibrium with their environment.
Arbitrarily trying to define systems complexifies the view of the structure and interfaces of the system, as they would not correspond to the actual processes at play. The analysis of systems becomes subjective to fit to an intention, a perception, not to deep dive into its reality. We see the whole with distorted boundaries, properties and interactions, that lose the coherence with the an objective view of the system as it arises in its environment.
This is the issue with defining a system as a representation created by the observer. We build an arbitrary model that is not in line with the actual system, and we analyse the model, not the system.
Tackling with the complexity of nature cannot be achieved by making its representation misaligned on the underlying reality.
In artificial systems, we are plagued with this issue of arbitrarily imposing a function, boundaries and interfaces that fit more or less with the surrounding reality. We only make them more complex. But nature does not do that. We need to uncover its systems as they are intrinsically,and coherently across its inner levels of organisation.