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Exploring complexity and the implications for leadership and decision making in a changing world. In conversation with Marco Valente.

One of the nicer aspects of exploring, reading and learning about systems thinking and complexity is to discover interesting blogs, papers and books and to get in touch with the authors to learn more about their points of view and their journeys into systems. 

Some months ago, one of my Google searches brought me to a blog on complexity written in 2017 by Marco Valente. Marco wrote it as a way to take stock and organize his thinking around some of the key concepts of complexity and systems.

It really resonated with me. The amount of information and ‘must reads’ on systems and complexity is overwhelming and it can be quite a challenge to organize that information and draw the implications (the so what) for the research and advisory work I do in international development. 

Exploring some of the implications of complexity, Marco Valente

I was curious, and reached out to Marco to hear from him about his journey into complexity and systems thinking; what is he learning and does he make sense of it all? Marco has been living in Malmö for the past five years and is a team member with Cultivating Leadership. He works as a facilitator of multi-stakeholder and leadership dialogues with a diverse range of partners, and brings complexity thinking into these spaces of sharing and exchange.

Marco, thank you for making time for this conversation. When did you start to be interested in complexity and systems, and why? 

It’s been a long and non-linear journey. I have always had a deep curiosity for understanding the world. I studied social science at the University of Salerno and the courses I took were meant to provide the tools for understanding the world – so to be able to act in it for the better. At that time I genuinely believed that approaches such as linear causality and multi-linear regression were good enough to understand the social systems in the world. My interest in understanding “what makes societies better” continued to expand the more I learned about it. So, I went on to study sustainability science at BTH university, Sweden, in the Masters in Strategic Leadership for Sustainability (MSLS). During those studies I took a deep dive into ‘systems thinking’. I learned about causal-loop-diagrams and saw for the first time non-linear causality, tipping points, feedback loops, etc. The main insight for me was that there was a different way to look at problems by connecting the dots, seeing the whole system, and looking at the root causes through a different lens. When I think today about those studies, I realize that I was taught a very specific approach to systems thinking: in its mainstream applications it still aimed to figure out the world with exactitude, accuracy and finding the right answers. A few years later, I came across the work of Dave Snowden and Brenda Zimmerman which made me reconsider almost everything I thought I knew about systems.

What caught your attention from their work and why did it change your perspective on systems thinking?

An assumption among some of us was that systems archetypes could help us understand the challenges at hand. Reading their work made me realize that the view I had was too simplistic, that a system representation is always a rough approximation. Systems cannot be fully mapped or visualized; they are just too complex. A visualization through causal loops and lines between elements of the system is useful but is it not the same as understanding a system.

Getting to Maybe by Brenda Zimmerman et al., and Dave Snowden’s foundational Cynefin articles opened my eyes to the shortcomings of a narrow perspective on systems thinking that was part of the academic courses I had taken. Zimmerman’s book highlighted how our attempts to create positive social change could not rely on a mechanistic worldview and made me realize that some of the systems change theories I had studied and researched were often rooted in a mechanistic paradigm. Snowden’s Cynefin made clear to me the fundamental difference between the predictable and the unpredictable in the world, and that we need appropriate lenses depending on the nature of the problem we are looking at. All in all, reading Zimmerman and Snowden made me realize how infinitely complex, messy and unpredictable the world is, and made me continue my search for ideas, concepts and theories about complex adaptive systems. 
 

The Cynefin framework

I am new to the field of systems and complexity. I feel that there is something right in systems and complexity thinking but I also see that using words such as systems, systematic and complexity in my research work can put colleagues off. One colleague told me recently that the ideas and methods around systems thinking for researching education problems in low- and middle-income countries are interesting but also abstract and it is not always clear how to use them in a policy research project. What I struggle with is describing in words what my instinct tells me: that a systems lens on social and developing problems leads to much richer insights than a reductionist analysis of a problem separated from the systems and relationships that cause it. 

I see what you mean. It’s a dance. On one hand we want to be rigorous and to make sure we use precise language. At the same time, we don’t want to scare people away with too much technicality, because we are both experts and novices at seeing complexity (this is one of the many paradoxes that I find in complexity). 

Thankfully people around me see that the ‘usual’ ways of analysing problems have shortcomings and struggle with the rapid acceleration and increasing complexity in modern life. Probably, I am lucky, because in my work most of my clients readily admit that the world is messy, unpredictable, non-linear, and we take as a starting point that we need very new approaches 

How do you think we should look at the problems in today’s world to try to solve them? What new capabilities and lenses are required? 

To be fair, it is easier to describe the shortcomings of the traditional methods and approaches used to analyse social systems than to come up with new approaches and methods. Complexity science is relatively new and the type of praxes that many people are trying out are even newer still. I am very inspired by so much good work happening today, but I also realize that we are in new territory. 

First, it’s important to understand when a problem is predictable and when it’s not. In my training and with clients I use some questions to help with this. For example, Is the system resembling a zoo or a jungle? Is it more like a steamboat in calm waters, or like a kayak through the rapids? Is it a game of chess or more like the game of life?

The biggest challenge that I see in the people I work with during my training is for them to stop applying a mechanistic logic to a problem that is complex and accepting that the logic they have been taught of a predictable and stable world does apply to complex and unpredictable systems. Sonja Blignaut put it perfectly: it’s like trying to survive in a jungle when all you know is the management book written for a zoo. 

Let me give you a couple of examples. A complexity-informed world view tells us that solutions to a specific problem are context-dependent. And yet most research grant applications of development project proposals ask to state something along the lines of, “How could this solution scale in different contexts?” I appreciate the intent, that is to rapidly scale up our solutions that seem to work, but we know that complex behaviours cannot just be scaled and adopted elsewhere! It is possible to standardize and ‘scale’ the manufacturing of ready-made bungalows because that is not a complex problem. But it is not possible to scale the fabric of cultural, social and economic relationships that form a system. 

A second example. All EU funding applications state the specific outcomes of the project. It might be possible to project outcomes that address simple to complicated problems. But projects that address complex problems cannot state a priority and detail what the outcomes will be. I think that funders know this; operation and decision-making systems for the most part prefer to think along straight input–output–outcome lines, and by and large we play the game to access the resources we need for our research and project work. 

What you are saying about funders reminds me of many governments in high-, low- and middle-income countries who put a lot of effort and resources into producing five-year development plans that describe in great detail what the complex systems of ministries, line agencies, laws, policies and people (in other words a government, a society) will do, in great detail. All clearly measured in terms of indicators, milestones, timelines. It seems to be that the messiness of a society or a government system and its development challenges do not fit into this logic.

The five-year plans! Let me clarify some things here. If we believe in human agency and free will (I do! Do you?), a vision of the future can be a powerful force of individual and collective inspiration. A vision becomes problematic when it is confused with a plan, with clearly identified steps. This tends to happen when a vision becomes set in stone and closes us off from the constant changes that emerge in the contexts of which we are part. A locked vision leads immediately to a locked strategy defined by predetermined goals – and research strongly suggests that it can backfire. It is almost as if a locked vision, strategy and plans become the territory, but they are not. 

So, the issue is not so much to design a vision of the future, or a strategy and plans, but how we use these once we accept the principles and uncertainty that are built into complexity thinking. Alfred Korzybskiremarked that “the map is not the territory” and the abstraction derived from something is not the thing itself. A vision and a strategy are abstractions, simplifications of reality and intent, but not the territory.

That is another paradox of complexity. You need to plan for an uncertain world. One of the best books that highlights this for me is Simple Habits for Complex Times, by Jennifer Berger and Keith Johnston (full disclosure: Jennifer Berger is the CEO of the organization that I work with). In their book, they describe a vision as a sense of destination, and some guardrails and boundaries around that. Imagine two scenarios. You need to go to the bookstore from your house: you place a pin on the GPS map and it shows with precision the shortest route and where to turn. That is, a destination and a path to get there in a predictable world. Now imagine you are in the forest and need to head north before dark, without instruments. With a minimum of orientation, you know the angle you need in relation to the sun. Without chartered maps, you need to make moment-by-moment choices about where to cross a small river, what places to avoid, but you still have a clear sense of where you are heading. That is a lot more like a vision as direction and boundaries and less like a 10-step pathway to success, with each step clearly pre-determined. 

In your work you design and facilitate dialogues for people to make progress on complex challenges. You also work with leadership capability development. What does complexity and system thinking mean for leadership and decision making?

Everything. It means that we need to critically examine some of our approaches, and rethink the role of leaders, the place for expertise, the assumptions about rational decisions, and much more. Let’s touch on a few points. 

The role of leaders in complexity as I see it is increasingly about navigating paradoxes and polarities. They need to create (not set in stone) visions for an uncertain world. They need to look at big trends and statistical data and yet be open to granular data from anywhere. They need to navigate between the technical challenges such as products, supply chains and technologies and the adaptive, human challenges of how teams can be innovative together, and how people respond differently to ambiguity (some love it and probably more hate it). 

Decision making in complexity needs to let go of assumptions of deterministic science and predictability. As a facilitator, I have come to see the power of tapping into the larger wisdom of groups when appropriate. For making sense of a system, we can gather statistical, big-picture data while adding rich, nuanced, local pictures of what is going on at the more granular level. For decision making we do something similar: leaders create boundaries and outline some minimum specifications. In this way we honour local, tacit knowledge in distributed decisions, while also enriching our understanding of patterns and weak signals from anywhere in the organization. I think, for example, of international NGOs focused on poverty eradication and inequality and how they could embrace complexity. Leaders will probably need to place greater emphasis on the role of deliberate experimentation of controlled small actions before scaling up something in a system that we do not understand yet. But for such experiments we will need a culture around sharing and experimenting in complexity, and for that I bet you will need to educate the donors and your other funders as well! 

Marco Valente, thank you very much.

Top photo credit:  delfi de la Rua on Unsplash

Photo by delfi de la Rua on Unsplash

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