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On the ways of learning by Mary Catherine Bateson

I am reading the Peripheral Visions: Learning along the Way by Mary Catherine Bateson. What a wonderful book.

A book of travels and of reflections about the multiple ways of learning, in particular learning from experiences.

Here few quotes I have underlined so far:

‘Sometimes change is directly visible, but sometimes it is apparent only to peripheral vision, altering the meaning of the foreground. While our society puts a premium on specialization and devotion to one pursuit at a time, narrowly focused attention tends to limit our learning and hamper our ability to make meaningful connections between different life experiences’.

‘Insight, I believe, refers to the depth of understanding that comes by setting experiences yours and mine, familiar and exotic, new and old, side to side learning by letting them speak to one another.’

Arriving in a new place, you start from an acknowledgment of strangeness, a disciplined use of discomfort and surprise. Later, as observations accumulate, the awareness of contrast dwindles and must be replaced with a growing understanding of how observations fit together within a system unique to the other culture. Having made as much use not as possible of the sense that everything is alien, you begin to experience, through increasing familiarity, the way in which everything makes sense within a new logic. Eventually an ethnographer will hope to develop a description of a whole way of life that will convey this internal consistency, in which the height and placement of a chair, the adult response to a crying baby and to voices raised in dispute, and the rules about when to relax and the rhythms of the day can be integrated, although never perfectly. The final description should deal with the other culture in its own terms. Yet it is contrast that makes learning possible.’

‘To become open to multiple layers of vision is both practical and empathic, to practice the presence of God or gods and to practice wilderness. Learning the traits of human culture, we are attentive to the undomesticated outdoors and the essential wildness spinning on in subatomic spaces, forever generating new patterns.’

‘This is a book of stories and reflections strung together to suggest a style of learning from experience. Wherever a story comes from, whether it is a familiar myth or a private memory, the retelling exemplifies the making of a connection from one pattern to another: a potential translation in which narrative becomes parable and the once upon a time comes to stand for some renascent truth. This approach applies to all the incident of everyday life: the phrase in the newspaper, the endearing or infuriating game of a toddler, the misunderstanding at the office. Our species thinks in metaphors and learns through stories. Many tales have more than one meaning. It is important not to reduce understanding to some narrow focus, sacrificing multiplicity to what might be called the rhetoric of merely’

‘Because learning is the most basic of human adaptive processes, we hope that it will lead toward a relationship with the rest of the biosphere that is both satisfying and sustainable.’

‘Ambiguity is the warp of life, not something to be eliminated. Learning to savour the vertigo of doing without answers or making shift or making do with fragmentary ones opens up the pleasures of recognising and playing with pattern, finding coherence within complexity, sharing within multiplicity.’

‘… and most learning is not linear. Planning for the classroom, we sometimes present learning in linear sequences, which may be part fo what makes calssroom learning onerous: this concept must precede that, must be fully grasped before the next is presented. Learning outside the classroom in not like that. Lessons too complex to grasp in a single occurrence spiral past again and again, small examples gradually revealing greater ad greater implications.’

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Capitalism without competition

What a podcast episode from The Daily @ The New York Times: Who do You Want Controlling Your Food?

Since 1980s and Ronald Reagan, a continuous process of mergers and market consolidation in the food industries ash left small producers are the mercy of giant corporations. When small producers try to innovate to reach consumers directly they are crushed by corporations. In this episode, you can hear the chilling words of Ronald Reagan; that the wholesale meat market in the US is an oligopoly controlled by just 4 corporations; the lack of power of small farmers and ranchers towards these corporations.

Most of all, the political system, administration after administration, has either supported this lack of competition or has proven not to have the will and power to address it.

From The Daily site: “During the pandemic, the price of beef shot up. Wholesale beef prices increased more than 40 percent — more than 70 percent for certain cuts of steak. Yet ranchers reported that the profits weren’t trickling down. The conventional wisdom was that price increases simply reflected the chaos that the coronavirus had caused in the supply chain. But there’s evidence that they were in fact a reflection of a more fundamental change in the meatpacking business. We speak to ranchers about the consolidation of the industry and explore what it can show us about a transformation in the American economy — one much bigger than beef.”

Photo credit: Febiyan on Unsplash

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“Food policy as doing.” An example from the 1970s from Finland of how to solve a complex social and economic problem with a system approach.

Thank you to Adam Kremeia for sharing with me this Guardian Long Read about the failing food systems in the UK and for pointing me to the experience from Northern Karelia in Finland.

The article is titled We need to break the junk food cycle’: how to fix Britain’s failing food system. It is a very concrete example of a complex and interconnected problem that requires a systems approach (including the political will) to try to solve it. The example from Northern Karelia in Finland from the 1970s is a concrete example of how a systemic approach to finding solutions works in practice.

“Recent English obesity policies have spoken endlessly of “action” to help people eat healthier diets, but what they deliver, often as not, is another raft of patronising diet information leaflets, such as the bright yellow Change4Life diet pamphlets handed out in schools and GP surgeries. (One uninspiring gem: “If you’re shopping for packaged snacks for your children, try sticking to 100 calorie snacks.”)”

“For three decades, Theis and White found, successive governments have repeatedly proposed “similar or identical policies” and then not done anything to see them through. What counts as an obesity policy could be anything from a plan of action to a statement of intent. Whichever party has been in charge, the most popular policies have been ones placing high demands on individuals to make personal changes (such as the 5 a day campaign) rather than meaningful reforms such as restricting the sale of unhealthy foods, or subsidising fruits and vegetables to make them more affordable. Most of the ideas for structural interventions – for example, that the food industry should reformulate its unhealthiest products – were voluntary. Unsurprisingly, compliance was not high. “

“Decades of research show that obesity is determined to a large extent by environmental factors such as socioeconomic inequality, the rise of ultra-processed food and the way that cities are built to facilitate car use. But policymakers of England have stayed wedded to the idea that weight is all about personal responsibility: just eat less and move more.”

“But it’s also worth remembering that food cultures are not static, and just sometimes food policy can succeed in changing cultural attitudes for the better. In the 1970s, the region of North Karelia in Finland had some of the worst rates of fatal heart disease in the world. A visionary young public health official called Pekka Puska implemented a whole range of measures to address cardiovascular health, all at once. Puska worked with women’s groups to encourage people to cook new versions of traditional dishes, with more vegetables and less meat. He supported dairy farmers in diverting some of their land from butter to berries. He persuaded local sausage producers to take out some of the fat and replace it with mushrooms. And he recruited an army of local people to act as advocates for the new diet to their friends and neighbours. Puska also instigated smoke-free workplaces. By 2012, cardiovascular mortality among men in the region had dropped by 80%. Policy experts still debate which of Puska’s various measures made the greatest difference, but in a sense it doesn’t matter. This was food policy as doing, not talking, and it worked.

A good food policy is one that actually makes it beyond the announcement and gets carried out, with adjustments along the way for anything that doesn’t work.”

Photo credit: Robin Stickel on Unsplash

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Living in a World of Systems, excerpts from Donella Meadows

Excerpts from Thinking in Systems, A Primer by Donella Meadows

“People who are raised in the industrial world and who get enthused about systems thinking are likely to make a terrible mistake. They are likely to assume that here, in systems analysis, in interconnection and complication, in the power of the computer, here at last, is the key to prediction and control. This mistake is likely because the mindset of the industrial world assumes that there is a key to prediction and control.”

“Social systems are the external manifestations of cultural thinking patterns and of profound human needs, emotions, strengths, and weaknesses. Changing them is not as simple as saying “now all change,” or of trusting that he who knows the good shall do the good.”

“We ran into another problem. Our systems insights helped us understand many things we hadn’t understood before, but they didn’t help us understand everything. In fact, they raised at least as many questions as they answered. Like all the other lenses humanity has developed with which to peer into macrocosms and microcosms, this one too revealed wondrous new things, many of which were wondrous new mysteries.”

“Self-organizing, nonlinear, feedback systems are inherently unpredictable. They are not controllable. They are understandable only in the most general way.”

“The idea of making a complex system do just what you want it to do can be achieved only temporarily, at best. We can never fully understand our world, not in the way our reductionist science
has led us to expect.”

“Systems thinking leads to another conclusion, however, waiting, shining, obvious, as soon as we stop being blinded by the illusion of control. It says that there is plenty to do, of a different sort of “doing.” The future can’t be predicted, but it can be envisioned and brought lovingly into being. Systems can’t be controlled, but they can be designed and redesigned. We can’t surge forward with certainty into a world of no surprises, but we can expect surprises and learn from them and even profit from them. We can’t impose our will on a system. We can listen to what the system tells us, and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will alone.”

“We can’t control systems or figure them out. But we can dance with them! …. Living successfully in a world of systems requires more of us than our ability to calculate. It requires our full humanity—our rationality, our ability to sort out truth from falsehood, our intuition, our compassion, our vision, and our morality.”

” Living successfully in a world of systems requires more of us than our ability to calculate. It requires our full humanity—our rationality, our ability to sort out truth from falsehood, our intuition, our compassion, our vision, and our morality:

Get the Beat of the System – Before you disturb the system in any way, watch how it behaves.

Expose Your Mental Models to the Light of Day – We have to put every one of our assumptions about the system out where others (and we ourselves) can see them.

Honor, Respect, and Distribute Information – You’ve seen how information holds systems together and how delayed, biased, scattered, or missing information can make feedback loops malfunction. Decision makers can’t respond to information they don’t have, can’t respond accurately to information that is inaccurate, and can’t respond in a timely way to information that is late. I would guess that most of what goes wrong in systems goes wrong because of biased, late, or missing information.

Use Language with Care and Enrich It with Systems Concepts – Our information streams are composed primarily of language. Our mental models are mostly verbal. Honoring information means above all avoiding language pollution—making the cleanest possible use we can of language. Second, it means expanding our language so we can talk about complexity.

Pay Attention to What Is Important, Not Just What Is Quantifiable – Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can’t measure. Think about that for a minute. It means that we make quantity more important than quality. If quantity forms the goals of our feedback loops, if quantity is the center of our attention and language and institutions, if we motivate ourselves, rate ourselves, and reward ourselves on our ability to produce quantity, then quantity will be the result. You can look around and make up your own mind about whether quantity or quality is the outstanding characteristic of the world in which you live.

Make Feedback Policies for Feedback Systems – A dynamic, self-adjusting feedback system cannot be governed by a static, unbending policy. It’s easier, more effective, and usually much cheaper to design policies that change depending on the state of the system. Especially where there are great uncertainties, the best policies not only contain feedback loops, but meta-feedback loops—loops that alter, correct, and expand loops. These are policies that design learning into the management process.

Go for the Good of the Whole – Remember that hierarchies exist to serve the bottom layers, not the top. Don’t maximize parts of systems or subsystems while ignoring the whole. Don’t, as Kenneth Boulding once said, go to great trouble to optimize something that never should be done at all. Aim to enhance total systems properties, such as growth, stability, diversity, resilience, and sustainability
—whether they are easily measured or not.

Listen to the Wisdom of the System – Aid and encourage the forces and structures that help the system run itself. Notice how many of those forces and structures are at the bottom of the hierarchy. Don’t be an unthinking intervenor and destroy the system’s own self-maintenance capacities. Before you charge in to make things better, pay attention to the value of what’s already there.

Locate Responsibility in the System – That’s a guideline both for analysis and design. In analysis, it means looking for the ways the system creates its own behavior. Do pay attention to the triggering events, the outside influences that bring forth one kind of behavior from the system rather than another. Sometimes those outside events can be controlled (as in reducing the pathogens in drinking water to keep down incidences of infectious disease). But sometimes they can’t. And sometimes blaming or trying to control the outside influence blinds one to the easier task of increasing responsibility within the system.

Stay Humble—Stay a Learner – Systems thinking has taught me to trust my intuition more and my figuring-out rationality less, to lean on both as much as I can, but still to be prepared for surprises. Working with systems, on the computer, in nature, among people, in organizations, constantly reminds me of how incomplete my mental models are, how complex the world is, and how much I don’t know. The thing to do, when you don’t know, is not to bluff and not to freeze, but to learn. The way you learn is by experiment—or, as Buckminster Fuller put it, by trial and error, error, error. In a world of complex systems, it is not appropriate to charge forward with rigid, undeviating directives. “Stay the course” is only a good idea if you’re sure you’re on course. Pretending you’re in control even when you aren’t is a recipe not only for mistakes but for not learning from mistakes. What’s appropriate when you’re learning is small steps, constant monitoring, and a willingness to change course as you find out more about where it’s leading.

Celebrate Complexity – Let’s face it, the universe is messy. It is nonlinear, turbulent, and dynamic. It spends its time in transient behavior on its way to somewhere else, not in mathematically neat equilibria. It self-organizes and evolves. It creates diversity and uniformity. That’s what makes the world interesting, that’s what makes it beautiful, and that’s what makes it work. There’s something within the human mind that is attracted to straight lines and not curves, to whole numbers and not fractions, to uniformity and not diversity, and to certainties and not mystery. But there is something else within us that has the opposite set of tendencies, since we ourselves evolved out of and are shaped by and structured as complex feedback systems.

Expand Time Horizons – The official time horizon of industrial society doesn’t extend beyond what will happen after the next election or beyond the payback period of current investments.

Defy the Disciplines – In spite of what you majored in, or what the textbooks say, or what you think you’re an expert at, follow a system wherever it leads. It will be sure to lead across traditional disciplinary lines. To understand that system, you will have to be able to learn from—while not being limited by—economists and chemists and psychologists. Seeing systems whole requires more than being “interdisciplinary,” if that word means, as it usually does, putting together people from different disciplines and letting them talk past each other.

Expand the Boundary of Caring – Living successfully in a world of complex systems means expanding not only time horizons and thought horizons; above all, it means expanding the horizons of caring.

Don’t Erode the Goal of Goodness – The most damaging example of the systems archetype called “drift to low performance” is the process by which modern industrial culture has eroded the goal of morality. The workings of the trap have been classic, and awful to behold.

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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