Part 1 of a series of 3 posts:
Glenda Eoyang is the Founding Executive Director of the (HSD). About which, Glenda says:
Human Systems Dynamics is a body of theory and practice that brings ideas from complexity science into practical application to help people deal with intractable issues.
The goal of the HSD Institute is to develop and disseminate the theory in practice of HSD. It works across four scales at the same time:
1) Individual practitioners understand and apply HSD.
2) The HSD Institute manages processes and products to support others as they apply HSD. It is a bounded, fairly traditional non-profit institution.
3) The network of certified HSD professionals connects individuals as they practice and learn about HSD and its applications.
And 4) The Field of HSD Theory in Practice includes the intellectual property–ideas and products–that both feed into and emerge from the shared work of individual practitioners, the Institute, and the network.
One of the important things about the network of HSD professionals is that it keeps people in touch as they continue learning. Our experience is that no one is ever finished learning HSD. Like learning to play a musical instrument or to swim, you can always get better, no matter how good you are. Learning HSD is like that, It’s a continually emerging process, so it’s really important to stay engaged in a community where dialogues about learning and practice can continue. Because of the HSD Associate Network, if you make a discovery, you can share it with others.
If you have a question, there’s somebody who can help. If you are lonely because you are the only one in your team using HSD, you can find other people to connect with. So, those relationships in a networked community are a really important aspect of learning and practice of HSD.
Tim and I recently had the pleasure & privilege of helping the HSD institute create their very first . It was especially an honor because of Glenda’s historical attitude toward network maps:
I have never been satisfied with network maps as a useful tool for planning and decision making. In my experience, they represented only one state of the system at one point in time. And they were incredibly resource intensive.
It is hard to justify investing so many resources to collect the data and map the thing, when the product represents only one point in time. I found it difficult to justify the return on investment. Several things about sumApp convinced me that, finally, the investment would pay off.
First, you can easily change the design of the map over time. Second, the map emerges and shifts as nodes and relationships change. Third, you can learn different things at different times, as you add or take away factors. Fourth, the map is accessible, so many different people can see and play with it. Fifth, and most practical of all, it is relatively inexpensive.
All of these features of the tool increase the benefits and reduce the costs of network mapping, so I was convinced to say, “Ok, we should do this.”
Given Glenda’s deep insight and experience in working with complexity in human systems, paired with her recent experience with our mapping approach, I was especially eager to get her thoughts on how well our process fit (or didn’t fit) with the needs she had set out to meet with us.
On the Relationship of Human Systems to Networks
Because I used Glenda’s CDE framework in my Master’s Thesis in 2011, I was particularly interested in learning how she understood network mapping in relation to her theories of change in human systems – especially change in the context of ‘change networks’ working on what Glenda calls intractable problems such as climate change, poverty & inequality, systemic violence, etc.
Dealing with intractable problems, she told me, requires understanding kinds of causality and change.
Glenda talked about : The first and simplest is two-dimensional ‘Static Change’ – the idea that a thing moves from one stable state to another stable state–and stays there. The second is more complex and multi-dimensional – the Newtonian equation of Mass, Distance and Time. ‘Newtonian Change’ is predictable. It has definable milestones, giving us things like Project Management and Developmental Models. This second type of change-causality works great on a lot of things, but not on intractable problems.
And the third, the kind of change Glenda’s work focuses on is ‘Dynamical Change’, which comes out of complexity.
Dynamical Change Creates a Context of NOT KNOWING. No matter how much data you have about a complex system, you cannot predict when or how it will change.
This kind of change causality has to do with the accumulation and release of tension at multiple scales of the system at the same time. So, it’s like an avalanche. You see the mountain. The mountain looks like it’s not changing, but inside it, small motions and shifts create tension. The tension accumulates. And outside the mountain, forces are also at work. Barometric pressure and sun and wind also create tensions that stress the structure and stability of the mountain. At some point the tension gets so strong that the structure can’t hold it anymore. And the mountain surface breaks a little bit. And that little bit increases the tension inside the system. Then, the next change is a little bit bigger. And then it’s a landslide. This kind of change is very different from Static and Dynamic change. It is so different, in fact, that it requires a new approach to mathematical analysis. This is the kind of change that we believe generates what people see as intractable problems. The change is driven by many different forces you cannot see. It depends on influences from inside and outside of the system. It is unpredictable, and it is often catastrophic.
Breakout of violent conflict is that kind of change. An ethical decision is that kind of change. Falling in love is. A financial crash. Refugee crisis, Bombing Syria.
If you think about all of the major issues of our day (climate change, poverty, war), we believe they all result from dynamical change. That dynamical change is what really drives transformation of all kinds across human systems, for individuals, teams, organizations, and communities. Dynamical change is by nature uncertain because it involves open boundaries, too many forces and interrelationships to track. There’s no way that you can get enough data to predict or control this kind of change. It’s not just not known yet, it is essentially unknowable. Unless we understand and can comprehend the accumulation and release of tension in systemic structures, we have no hope of dealing with the unpredictability and intractability of our most pressing, systemic issues.
By thinking about patterns in human systems in this way, you are able to make rational choices and do rational things, even if you don’t know what’s going to happen. You can call upon a tension-informed, alternate rationality that’s not linear and predicted. Given that we think about the world in this way, and we want to share these insights with others, we need to represent complex systems so that other people can understand their behavior without REALLY understanding the complex science of dynamical causality. The paradigm shift to the theory of dynamical change is unintuitive and quite challenging for most people. On the other hand, it’s very familiar in people’s practice and common sense. Transformation does feel like and look like an avalanche. People who practice know that this is true. They say, “oh yeah, yeah, yeah, there’s no question about it.” In HSD, we want to help people to be able to take action in a dynamical reality before they really grasp the theoretical underpinnings.
But, I asked her – how does that relate to change networks?
The ‘CDE’ and Self-Organizing Systems
What originally drew me to Glenda’s work was the concept of . Natural systems self-organize all the time, and we think nothing of it, yet the idea seems so counterintuitive in human systems, and really difficult in practice. At that time, (~10 years ago), I was reading and learning as much as I could about the idea of Self-Organizing Systems – because, intuitively, yeah – it just seem so Right and so True. But still, at the practical level, what enabled, or triggered, or catalyzed self-organizing was all really confusing and vague to me. It clearly didn’t just happen. Then I came across Glenda’s , which I found super-helpful. It gave me a way to think about self-organizing. So what is the CDE?
In HSD the CDE are the three conditions that influence the behavior of self-organizing in systems. They are: a Container (C), Differences (D), and Exchanges (E).
So when I asked Glenda about how networks relate to dynamical change, I suddenly saw the overlap – to the degree a network reflects the CDE in human practice, a network is a self-organizing system.
If we take those three (3) conditions, which are really fundamental, and we think about networks, we can see why a network is a good model of self-organizing. A network models the container [C], insofar as there’s some subset of agents that are nodes. It models the differences that make a difference, insofar as we say which ones have which characteristics [D]. And it models the connections because edges represent all kinds of exchanges [E]. And, so, in that sense a network map captures and represents those three (3) fundamental characteristics that we think define the nature of all self-organizing systems.
Networks, Intractable Problems, and Adaptive Action
But still – networks may be self-organizing systems, but that doesn’t mean they’re inherently able to solve intractable problems – where does that part come in?
For that, Glenda says, we need – that ability to take meaningful action within a context of not knowing.
So, the CDE is one part of HSD. The other part is this Adaptive Action cycle, an iterative, three-step, action learning model. In the first step, you ask What?, so that you become conscious of the patterns and tensions in a moment. In the second step, you ask So what?, so you can make meaning of the patterns you perceive. Finally, you ask Now what? to make a decision and take action to shift the conditions and change the pattern. To make a difference, you have to choose, but you can’t choose unless you’re conscious, and you need a way to help you be conscious. The network map is a model that can help you be conscious of the conditions that shape the self-organizing processes so you can make a difference, even when you can’t predict or control it.
Can a Network Become Conscious?
Ok – so Container-Differences-Exchanges, and . . .But consciousness – how does that fit in? That’s what we’ll address in the next post in this series. for our newsletter to be sure you don’t miss it.