Glenda3

Theory and Practice in Network Mapping: An Interview with Glenda Eoyang

Part 1 of a series of 3 posts:

Glenda Eoyang is the Founding Executive Director of the Human Systems Dynamics Institute(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 network map of 800+ certified professionals across the globe. It was especially an honor because of Glenda’s historical attitude to 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 three kinds of cause: 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.

Uncertainty

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 self-organizing systems. 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 CDE Model, 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 Adaptive Action – 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?

So, among organizational systems thinkers, there’s this idea of the system ‘waking up’ to itself, or ‘seeing itself’ – which I always imagine as this mystical moment, like when a clay dragon comes to life, shakes out its wings, belches out a tongue-lashing of fire, and swoops off to destroy the evil empire, or whatever. But it’s not really a dragon, it’s actually a murmuration of starlings or a swarm of bees or something, which somehow makes it even more powerful and awe-some than a single big drooling dragon would be. And the ‘flying off to destroy the evil empire’ is actually more like shifting tiny bee-sized patterns in a way that ripples out and has a dragon-sized impact.

By my reading, that generally only happens in those rare historical moments when so much is so imminently and clearly at stake for so many, that masses of people willingly set aside their everyday lives for a period needed to address a great risk – or when their everyday lives have already been destroyed, the disaster has happened, none of the old rules apply and pulling together is the only path to recovery. But once the threat recedes a little, or the crisis is recovered from or becomes normalized, we return to our separate selves, we abandon the collective order and revert to our everyday self-reliance and personally-ordered survival. Because what sustains that spontaneous kind of self-organizing is extreme and non-ordinary, it’s not sustainable, (I’m just saying, that’s how it seems to me) or, in the words of my new friend Jeffron Seely – it’s a ‘change’ (which means it’s reversible) not a ‘transformation’ (which is irreversible).

In other words – what a cool idea, the system waking up to itself – but WOW, do we humans generally neither understand nor act in ways that make that likely! I see that when presenting network maps all the time – ‘here’s who comprises your system’ I say to people ‘and all the things you can know about them, and how they fit together’, and the majority of them smile questioningly & say – basically – “so what?”.

So I asked Glenda, “can you talk about the challenge of helping the individuals within the system learn how to ‘see the system’, and most importantly what they could do differently as a result?”

We think that self-organizing is happening all the time. Sometimes it is slow because the conditions are not changing very much. Sometimes it is fast because tension has accumulated, and the conditions shift quickly and radically. We think it is easier to help people “wake up” to the tiny, everyday avalanches they experience. Then, when the big, obvious crash comes, they are ready for it.

 

For me, the network map is a tool in that process of seeing and understanding the systemic patterns of self-organizing. I have empathy for your clients and mine. If it’s so hard for them to see systemic, self-organizing patterns with a map, think how hard it is without the map.

 

Native Americans make a distinction between what they call the eagle eyes and mouse eyes. The eagle flies high and can see the whole thing, right? It’s not a lot of detail, but it can see many things and a long way. Mice see what’s right in front of them. Both kinds of seeing are important for decision making, but they are very different. The distinction helps me understand why it is sometimes hard for people to see their own systems. I can imagine an eagle view of the network, ,but I can’t really see it. I’ve got mouse eyes. In my normal life, I’m standing in a place, I know who’s connecting with me, I know who tells me they’re connecting with other people. But it’s all me-centric, not a systems-eye view.

 

The map gives me an eagle view of the network so that I can see it. Better than that, it isn’t just how I see it, but how other people see it—and all of them at the same time. So, for me, that’s what the map does.

There’s this question that haunts me – wouldn’t a directory be just as good? At the core of this question, I think, is that in a lot of ways, the maps we make are presented and used predominantly as directories – we make it easy to search for someone with X perspective, or X skills, we help you see ‘who is here’ – which is extremely useful.

But those are parts. And there are easier methods for finding out about the parts. I’m always left with the sense that we’re still overlooking the greater whole. I want to understand and help us all learn more about that ‘whole’ perspective, because I imagine that would help our systems ‘wake up to themselves’. But the whole, any good systems thinker will tell you, is in the connections at least as much as, if not far more than, in the parts. Yet, when sharing maps with groups, it’s the connections between the people/actor/organization-nodes that generally don’t seem to matter to most people (except if it looks like they’re winning the popularity contest). They often don’t see the relevance – and I don’t feel like I do a good enough job helping them recognize it.

So I press Glenda, because here is an opportunity for me to get better at that: “But why does a network map increase consciousness and choice more than something like a directory would?”

The theoretical response is that it can help us represent systems that are open (wide range of containers (C)) and high-dimension (many differences that make a difference (D)) and non-linear (complex exchanges and interdependencies (E)). A directory, on the other hand, gives us a picture that is closed (at any given moment), low-dimension (what you see is what you get), and only one kind of simple connection. Most models of systems have these or similar limitations. There just aren’t that many ways, beside network maps, to model the three conditions of self-organizing that represent the most complex of complex systems–open containers, high-dimension differences, non-linear exchanges. So, theoretically, that’s why the network can help you see and be conscious of the system.

 

It’s about consciousness and choice, right? So, if I can become conscious of relationships that are current, then I can see potential for what might be, and then I can choose my next wise action—my adaptive action—in order to leverage what is. If I can see it, I can understand it in some way, and I can make choices about action. When I do that, the system shifts in my hands. . . the network helps you see the current patterns in a way that’s useful, so you can choose to take action to influence the pattern in the future.

 

Practically, the map helps me manage my connections as an individual or an institution. When I am aware, I can make connections more intentionally, more efficiently, more effectively. It helps me be conscious in a deeper, wider way to see things I wouldn’t see otherwise. When I see, I can do things that I wouldn’t do otherwise. It helps me choose. It gives me the information I need to choose.

For instance:

. . .when I have to make a decision about how to spend my time or my influence or my energy or my money, I can look at the map. There, I see connections that I could amplify or possible connections that I could create with that resource. The map helps me know how to invest and, because sumApp is able to show changing relationships, it also gives me feedback about return on investment.

Theory in Practice – the HSD Network Map

I asked Glenda what she’d hoped for when we started mapping – why did we go ahead?

Well, we started the Institute in 2003. We’ve been talking about our network for 15 years, but it only existed in imagination. To be able to see it on the screen was just thrilling—is thrilling…continues to be thrilling. Every time I open it up I just get a tingle. And the reason for that, I think, is that we’ve talked about the HSD Associate network; we’ve described it, we’ve brought people into it, we’ve made decisions based on it, but it has always been a miasma; an imagination. It existed primarily in my mind and sometimes in other people’s minds [chuckles]. And, so, to make it manifest; seeing it in a concrete way, was just thrilling.

 

The practical reason that we decided to do it was that we’ve gotten large enough that the Institute can no longer sustain the central hub. (Think about tensions accumulating and requiring a structural shift!) Up until now, a few of us formed a central hub. We had connections to everybody; hub and spoke. And then we added a couple of other hubs; hub and spoke. We put those new hubs inside the institute, so, the institute became the hub. A bigger hub and more spokes increased the capacity of our network, but it still wasn’t enough, and we’ve even moved beyond that. Now we need some way to allow people to connect to multiple nodes and for multiple hubs to emerge. So, what we’ve seen has not been a scale-free network. It’s still hub and spoke, basically, but we hope the model sets the conditions for us to think about the network’s dynamics and to begin to work on setting conditions for a scale-free network to emerge–which is, ultimately, the most sustainable.

Me: “Can you define what you mean by scale-free network here?”

Sure, in a scale-free network, there are many hubs, and each hub has nodes connected to it. If you zoom in on any node it can look like a hub and if you zoom out there are many nodes. No matter how close or far away you are, the structure looks the same, so that the structure is fractal and coherent. The scale-free network is more resilient than a hub and spoke, because the stress is distributed across multiple hubs. It is also more efficient than all-to-all network because when there are so many connections, the nodes are locked in and the system has reduced freedom to self-organize and adapt.

Consciousness & Choices Generated by HSD Network Map

The first thing I wanted to know was: Who are connected in the constellation but really far away, so that we don’t even know they’re there?

 

Out of eight-hundred (800) HSD Practitioners, we have about ten percent (10%) who are close enough in to send their data for the mapping. When we sent out the request to come and join the network, the first people who came and signed on were ones we hadn’t heard from in years. They had gotten certified, got the skills they wanted, and then they disappeared. From what we knew, they haven’t hooked back into the network, and we can’t tell if they have stayed connected and silent or totally disconnected. In responding to the survey, they let us know they were there! That happened for several people. It gave them a concrete way to engage with the network that they didn’t have before and for us to know that they were connecting. So, that’s one thing that has been really useful.

 

The second thing we wanted to know was: Which of our messages are most relevant to which people?

 

In response to this question, we have been using the map very explicitly for communications. We host open program Adaptive Action Labs, and we want to promote them to members of our network, but we don’t want to send people messages they’re not interested in. With the map up and running, we go into the network [map], extract the names of people who are interested in a particular topic, send emails to them saying, “we know you’re interested in this, we know you know people who are interested in this. Please join us and pass the word along to your friends.” We just started this as a formal way in January, so, we haven’t had a lot of time ,but it has significantly increased our marketing and sales reach.” So, that’s the second thing. We’re using it really explicitly to target our marketing.

 

The third question is: Who will make the best network leaders for geographical or interest groups? The scale-free network idea is that we will have distributed hubs that are somewhat formal. We have a hub here in the Twin Cities, one in the UK, and one in Amsterdam. We would like to establish others, and we’re using the network to see who and where they should be. We have used databases and anecdotes to do this same thing in the past, but the network map is going to make it much easier to identify potential groups and potential hubs.

 

So the network map is helping us by, (1) making us conscious of the edges of the network in a way that we weren’t before. (2) letting us know that there are clusters of people who are interested in the same things or living close together; and (3) being conscious of the people who are natural hubs so they can work with us to strengthen the network.

***The Tensions (oh, those perpetual tensions. . . )

I may be delusionally aspirational, but I consider my core work to be developing the data-gathering & visualization tools needed to help with this ‘system seeing itself’ problem. I’m content to let others who are wiser develop the activities and the stories and the frameworks and the processes that help with the ‘seeing’ – I just want to make sure they have valid and meaningful information to look at in support of that process. But in order to do that, I need to be conscious of what those others are doing with the existing tools and what more they’re looking for. I have to live in the midst of that particular tension, or gap between what is & what’s needed.

Glenda gave me some great ideas about how to inform the sense-making system-seeing process, which I’ll put into another post soon. She also helped me become more conscious of the tensions accumulating for her in the present moment – relative to our current data-toolset.

I don’t know that there’s anything I’ve done in the last ten (10) years that has been as exciting to me as getting our network map. And, still, the map at any moment captures only one set of relationships. It privileges a set of relationships that we’ve defined at a specific time. We think that is a good representation of reality, but it is not reality. It may be good enough for now, but how can it be better?

 

We’re thinking about the real network we are trying to model. We want to make our network map as close to reality as possible, so we are looking for how the lived network is different from the modeled network.. What we’ve noticed is every node has a few named connections and lots of potential connections at any given moment. For example, Toine is an HSD Associate who lives in the Netherlands. He is a consultant for public infrastructure construction projects. Toine belongs to one learning group and serves as Praxis Partner for others. He works with a team of Associates to expand his theory and practice. He has connected with other Associates in the Netherlands and across Europe. All those connections show on the map, but there is much more. He can have coffee with Kevin, call me up, write an article with Judy in New Zealand, sponsor a consulting job with a client, hold a public event, go to a conference in his field and talk to people about HSD. None of these shows up on the map right now, but they could. They are potential connections.

 

At any given moment he’s not going to be doing all that stuff, but he has the potential for it. And that’s just one person, we’ve got 800 of them. So, how can we think of a model in which each node has an infinite number of possibilities and is at a moment manifesting some of them; actualizing some of them. Depending on who they are and where they are and what they’re doing, each Associate is going to choose one connection or another. They will continually make new connections and let others fade away. The result is a stable network that emerges in a place and time for a certain purpose and then it dissolves again. Then it emerges in another configuration and dissolves again. We talk about this as a “cloud network.” I have no idea how we might model it, but it is how I now think and talk about our network of Associates. I don’t think I could have conceptualized this more realistic and complex picture before I saw our current network map. It has helped me become more conscious of what is, so I can understand what is happening, and make better decisions to inform the future.

The tension accumulating for me is the need to translate what exists in other’s imaginations, is infinite, abstract, and non-linear down into processes and code that are easy to use, expressed in bits & bytes, fanatically linear, very finite, and as un-imaginative as hell, so that we can then model or represent all of that in visualizations that capture the imagination, point toward, and at least minimally ‘feel’ like those open, high-dimension, non-linear models Glenda and others are asking for. That may seem easy (I get that a lot ‘why can’t you just. . . . .’) to you, but to me – it feels intractable. But I take heart from Glenda’s approach to the so-called intractable:

It’s never intractable because there’s always the next wise action. There’s always something you can do.

Interviewing Glenda was one of those next wise actions on my part – I learned a lot! Thank you Glenda – on so many levels!

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