Author Archives: Marcus

structure in the sand

The seven criteria of Warm Data

I have now gone through the bulk of the Warm Data Lab host training (only two sessions left) – yet I know that my journey into Warm Data and actually hosting Warm Data Labs or People Need People online sessions is only just beginning. In this post I want to go a bit deeper into Warm Data and what criteria Warm Data, or rather the process to generate Warm Data, should adhere to. I also want to show that Warm Data is not just about making us get a warm and fuzzy feeling; it is as much about rigour and scientific enquiry. It is based on the theoretical foundations built up by three generations of Batesons: first William, then Gregory and now Nora – as well as their collaborators at various points along the way. I’m still exploring how this particular body of knowledge is connected to the ones I have been diving in before like complex adaptive systems, anthro-complexity, complex reflective processes or systems dynamics. My suspicion is that while the language is different many of the ideas and concepts overlap, in particular between anthro-complexity, complex reflective processes and Warm Data. But I’m skipping ahead. 

Besides my notes from the Warm Data Lab host training, I also draw from an article on Warm Data Nora wrote back in 2017. 

Let’s start with the definition of Warm Data: 

Warm Data is transcontextual information about the interrelationships that integrate a complex system.

The notion of transcontextuality is central to Warm Data, as I already alluded to in my first article. What is also important is that Warm Data is about the relationships, the in between. It is not about describing the parts or elements of a system, but the relationships, interrelations and interdependencies between both the parts but also the various contexts. Learning happens at the interfaces between the contexts, where inconsistencies and paradoxes become visible. In traditional scientific enquiry, these interfaces are often not looked at, they are blind spots created by the boundaries of the various disciplines. While transdisciplinary approaches attempt to assess the relationships and interdependencies, Warm Data also challenges notions of objectivity, measurability and replicability in relation to the enquiry of complex living systems.

Warm Data can be described by using the following seven criteria, together with some examples from my own experience:

  1. Observing the observer: As we know from scientific disciplines like biology and physics, the observer cannot be located outside a system of enquiry but has an effect on what is being observed. Importantly, observing changes what occurs, our perceptions of what occurs also depends on who we are as observer (see below ‘cultural responsibility’). Hence, we need to know who is observing and what the observer can perceive and what not (what are our blind spots?). As we all have these blindspots that are either hardwired into our human brains or culturally and socially learned, generating Warm Data always happens in a team of diverse people. Or as Nora puts it in the article referenced above: “The observer matters, and teams of observers matter. Since data are always derived through the particular lens of the researchers, descriptions of their filters of perception are vital information and must not be sterilized out of findings.” In my own work in international development, this plays a very important role when attempting to observe the effect or effectiveness of a project, either through monitoring or evaluation. Interviews and surveys are a common data collection methodology. When I go out to interview people, I bring my own bias with me, so whatever I perceive will already be filtered. Did I like the project and the people working in the project? Or do I want to find that the project is not doing so well? But my presence also alters the behaviour of the respondents, as they are likely to respond to my questions with what they think I want to hear. They might also have their own agenda, for example hoping that project support would continue if they say that the effects were not yet really big. Of course experienced evaluators know about this effect, yet in practice there is very little done to mitigate it.
  2. Multiple description: Building on the above, we need to capture multiple diverse descriptions of a situation. But in Warm Data, it is not just about collecting a large number of descriptions from multiple perspectives, but also about asking what the relationships between these descriptions are. What are the differences in them that make a difference? Neither is it about having just diverse descriptions that all have the same form. We also want to have multiple tonalities, textures or other forms of descriptions (scholarly texts, essays, lectures, stories, poems, music, videos, photos, etc.). Nora puts it this way: “Multiple description both blurs the distinctions between contexts, and describes them through difference, comparison and relational perception. While it might appear that this process would lead to an untenable and infinite collection of perspectives, the Batesonian notion of information as “difference that makes a difference” is way [sic!] to study the relation between perspectives, through contrasting qualitative characteristics. The information is not located but diffused into the contextual contacts and boundaries.” In my experience, we often try to combine observations by the project teams with reports of the project partners, relevant news articles, interviews and focus group discussions as well as narratives collected from the partners. Sometimes, photos are also used to document changes. We have not gone as far as also taking into account other textures like poems or music – although I can remember some instances where project team members or partners wrote songs about what they experienced in the project. One of our Mesopartner associates is also a poet, but as far as I remember, he prefers to write poems about wine, rather than about logframes …
  3. Fluid patterning: The need for looking at patterns instead of independent events in order to better understand complex living systems has become well accepted. The Systems Iceberg shows patterns of behaviour as the first layer underneath the surface of the observable. In Warm Data, however, the emphasis it is not about seeing the patterns in a static way, but to also understand how the patterns change. And again, it is also about seeing relationships between contexts through “patterns that connect” (another Batesonian notion). How patterns of behaviour change over time is certainly an important aspect of monitoring systemic changes. For example, in one project that works on improving employment in Moldova, we use patterns of how various actors organise, exchange information and learn together to see whether there is a change towards more collaboration and common problem solving.
  4. Paradox, inconsistency, and time: complex living systems are constantly changing, yet there also are temporary stabilities that are often perceived as structures (see my post about ‘A dance between structures and events‘). Different aspects of systems are changing with different speeds, which inevitably will lead to inconsistencies and paradoxes. The question we need to ask is what we can learn from these inconsistencies and how we can hold these paradoxes over time, as they are important opportunities to learn. In Nora’s words: “Scientific research premised upon the complexity of a system in relation to its environment will produce paradox and inconsistency, by necessity. In order to keep the complexity intact, results should feature these dilemmas without resolving them. In fact these instabilities are sources of information about the relationships that are highly generative. Relationships over time change, and aggregated relationship such as a forest or society must produce responses to responses that are disruptive. The disruptions are rich with Warm Data.” We are currently, in the time of the COVID-19 pandemic, experiencing a time with massive inconsistencies between what we know, what legislation is being proposed and enacted, and how we act as a result of it. There are inconsistencies between countries and in countries that have federal structures like Switzerland and Germany we also see inconsistencies between the Länder/Cantons. This is a massive opportunity to learn about the relationships between research, knowledge, policy, politics and behaviour. All of this is happening in the paradox that we need to stop (or control) the spread of the virus while at the same time keep our lives, our children’s education and our economy going.
  5. Holism and reductionism: in my last blog post I tried to write about the difference between a transcontextual perspective and a holistic perspective on the world. While the latter divides systems into parts and wholes and sees the system as created out of the interplay of the parts, a perspective informed by Warm Data sees the parts and the whole as a paradox rather than as resolvable and fully separable. I’m still figuring this out so in my reading, this criteria of Warm Data refers to the need for understanding the boundaries of reductionist thinking and its usefulness as compared to holistic or I’d say rather transcontextual thinking. Reductionist methods are useful but the use is rather specific in taking a piece out of its context and freeze it in time, then study it and – maybe – putting it back into the context. Warm Data studies relationships and can therefore only happen in the context(s) and in time. Or to again use Nora’s words: “Information derived by zooming out to study context is as important as the information derived by zooming in on detail. These two forms of information are not alike. One is relational and overlapping, the other is isolated and (sometimes) linear. Both are needed in relation even when they produce contradictions. Smaller and larger contexts are tangled up mutually calibrating interactions. They are not concentric nor are they separable; rather they are steeped in interdependency.” This reminds me of the discussions on using Randomised Controlled Trials in evaluating development intervention. RCTs are deeply rooted in a reductionist stance, attempting to isolate a single variable and see how other variables change if it is present or not – for example how the attainment of pupils is changing if they are fed at school or not. Here, the context is ignored or even statistically removed. While RCTs can have their justified uses, a Warm Data approach would always emphasis the need to also understand the multiple contexts that come together in a school, to use the same example. What is the economic situation of the families of the children and the teachers? What is the nutritional situation? What is the ecological situation in the place the school is located? What social or socioeconomic are at play? What is the role of politics? A multitude of different factors determines the attainment of the children. Another quote by Nora I love that describes the interplay between reductionism and holism beautifully is this one: “To study the tango, a student must first know what the tango dance and music are. Then, when the steps are dissected into parts, they are still considered within the cohesion of the whole dance. Contextual knowledge gives a scaffolding to build detail into, but decontextualized details lack life.” (it is from Nora’s article Preparing for a Confusing Future  Complexity, Warm Data and Education)
  6. Cultural responsibility: Every culture and every single one of us has blind spots, so we need to take responsibility for them. By observing the observer and using multiple descriptions, we can uncover blind spots and mend them by working in a team – ideally a multi-cultural team that can also detect cultural blind spots. Even what we understand as knowledge in the Western society is limited by a Western academic frame. We need to hold the humility that there are other ways of knowing in the world that are not less rigorous or less relevant. Nora puts it this way: “Science and culture are deeply entwined. Development of inquiry that is simultaneously inclusive of multiple generations, cultures, and sectors is useful to keep observers’ frames relevant. Information is only as perceivable as the sensorial limits of the observer. A variety of perceptions lessens blind spots.” Cultural responsibility plays a huge role in my work in international development, where I predominantly work with people from different cultures. Listening to them and trying to see their realities is crucial, even though not always easy. Many development workers bring, consciously or unconsciously, an attitude of ‘we know better’ and ‘let us fix your system.’ But the systems in developing countries are not broken, they work perfectly well given the circumstances. We need to build from what is already there and as external agents it is not easy for us to see that. It does require a good portion of humility.
  7. Aesthetic, mood, rhythm: The tone matters to any enquiry. The mood or rhythm of a context shows the limits of the relationships or of the communications. The tone in a board room is very different from the tone in a bowling club. This difference is important to recognise. Nora: “In any inquiry of life, the aesthetic matters — perhaps above all else. This vital condition of any interrelational context is often ignored in favor of misplaced rationality. Given that complex systems are interrelational, the nature of the relationships needs to be noticed. The aesthetic is the conduit through which relation occurs. While the aesthetic need not be valuated, it must be noticed to better assess relational information. Keeping in mind that the opposite of aesthetic is anesthetic, it is clear that increasing sensitivity is preferable to numbness as it increases receivable information.” As a decent facilitator, you feel the mood of the people in a room and, for example, I often notice a difference if the boss of a team is in the room of not. Or just in general, you can notice how a team works together and whether they harmonise or not.

While I found some examples for each of the criteria, bringing them all together is still challenging, but I can see how they can come together to produce much ‘warmer’ results, for example in talking about the effects of projects. I have come across many of these criteria before in other contexts. For example, Theory U talks about bending the lens back on the observer, which is in line with the first criterion of Warm data. From anthro-complexity, we know that as individuals are not able to see the whole system, so we need to look for multiple perspectives – and look at the system from multiple perspectives –, which corresponds with the second criterion – even though the idea of also looking at different tonalities and textures is a new one for me, but makes a lot of sense. The school of thought on complex reflective processes also talks a lot about paradox in complexity. Seeing these criteria in this shape brought together in the theory of Warm Data is very encouraging and for if different bodies of knowledge share the same bases that is a good sign and makes them more credible. Also the way Nora describes them in very human terms is, no pun intended, warming, and makes them accessible in a new way on a new level. 

This post is quite dense and it took me quite some time to write this up, so I’m stopping here. More to come on my adventure with Warm Data.

Featured Photo by Viktor Forgacs on Unsplash

numbers

Holistic worldview and cold data

This is the second article in my Warm Data Series. In my last post, I talked about the basic understanding of what Warm Data is and how it is based on a transcontextual understanding of complexity. Today I want to start with responding to two comments / questions that I have received as a reaction to the last post on my website. The first one is from Ulrich Harmes-Liedtke, wanting to know how a world view based on Warm Data and transcontextuality is the same or different to a holistic worldview. The second is a comment by Shawn Cunningham about the difference between warm data and cold data.

First, let’s look at the question on holism. The worldview informed by Warm Data and transcontextuality has certainly some similar aspects to having a holistic worldview, yet it is still distinct. This is how I understand it (and as you know I’m still exploring this). A holistic worldview implies that there are parts that interact and together build a grater whole, the system. This separation of parts and wholes can be problematic. Firstly, when defining the parts, you need draw boundaries around them. But if you look closer and go down to what you think might be such a part, the part turns out to be a whole itself (yet not always). So where do we draw the boundary around a part? This inevitably leads to the question of finding the elemental part(s) that build up everything else, which is a reductionist perspective and not immediately helpful when looking at complex living systems. Secondly, finding parts and isolate them also implies that they can be acted upon. In this way, talking about parts leads to a mechanistic view on complex systems. There is always a tendency to isolate the parts, fix them. Or optimise the system for some parts (like optimising the economy to serve the poor). We know this does not work, as optimising the system for one type of parts will lead to unintended consequences in another part of the system. In a complex living system, nothing can change without everything else changing (this is actually how William Bateson conceptualised a system, if I paraphrase him correctly). In reality, all is entangled. To take my example of the tree and the forest: where does the part we call tree end and the whole we call forest start? Are the insects that live inside a tree in a symbiotic relationship as much part of the tree as the microbes that live in our gut are part of us? While we can draw boundaries around elements, like the skin being the boundary of the human body, these boundaries are not always helpful — for example if we look at behaviour, my behaviour forms the community as much as the community forms my behaviour – where is the boundary, where do I end and the community start? 

This is from Nora:

The way in which we have culturally been trained to explain and study our world is laced with habits of thinking in terms of parts and wholes and the way they “work” together. The connotations of this systemic functional arrangement are mechanistic; which does not lend itself to an understanding of the messy contextual and mutual learning/evolution of the living world.

Reductionism lurks around every corner; mocking the complexity of the living world we are part of. It is not easy to maintain a discourse in which the topic of study is both in detail, and in context. The tendency is to draw categories, and to assign correlations between them.

Bateson, N. (2016). Symmathesy–A Word in Progress. Proceedings of the 59th Annual Meeting of the ISSS – 2015 Berlin, Germany, 1(1). Retrieved from https://journals.isss.org/index.php/proceedings59th/article/view/2720

The way I understand this is not to completely loose the notion of parts but rather to hold the distinction of whole and part as a kind of paradox – as Nora puts it to appreciate that the study is both in detail and in context. The one requires the other and is dependent on the other. For example, we become ourselves because there are other selves. So the others are part of why I view myself as myself. At the same time, learning happens between the different selves through interaction. As Chris Mowles writes [2]: 

Human beings and what they are doing, thinking and acting is what causes social evolution.

Mowles, C. (2015). Managing Uncertainty. Complexity and the paradoxes of everyday organizational life. Routledge: Oxon and New York.

So in that sense, human society builds a learning whole.

Now to Shawn’s comment. He wrote:

Hi Marcus, thank you for sharing your thoughts from the Warm Data lab. From my understanding of warm data, understanding what cold data is also makes sense. If I recall correctly, cold data can be captured as exact numbers, it can be quantified. It could even be statistically analysed without understanding the context. In our field, it means that an index to assess global competitiveness might consist largely of cold data. Understanding why some people resent being benchmarked, or why they feel that important aspects are not reflected in the dataset would require a warm data approach.

Yes, cold data is statistics, numbers, measures. Particularly, it is data that is taken out of its context, which often happens when you quantify aspects of complex living systems. There are important applications for cold data and we can learn many things when looking at it. But it does not give us the full picture. And more importantly, it often doesn’t tell us anything new about the interfaces between contexts and how learning can happen there (I hope to post more about that soon). Cold data is generally focusing exclusively on one context – economy,  education, ecology, politics; or at best it draws simple correlations between measures from two contexts like the economic achievement of students from socially challenged backgrounds. Warm Data is not primarily about creating data, it is about changing perception, about creating a new understanding of why things are happening the way they happen and shifting the way learning is happening between contexts and through that hopefully how things are done. Warm Data is not to inform and analyse in a way as if you are outside of the system and can make an objective plan. It is about understanding how things are connected and what our part and our role is in these interconnected contexts.

Now I need to head into the next session of the Warm Data course.

Featured photo by Mika Baumeister on Unsplash

tree in a forest

Exploring Warm Data

Last week I started a training course to become a Warm Data Lab host with Nora Bateson and the International Bateson Institute. I want to start a series of blog articles in which I reflect on the different concepts and ideas that I take away from the course. The series is intended as much an opportunity for me to reflect and deepen my understanding of the concepts as it is intended to be for the readers of my blog to get an introduction to Warm Data and connected concepts. The first article in the series is about defining Warm Data and the connected concept of transcontextuality. 

Most fundamentally, Warm Data is about changing how we perceive the world and make sense of it. It is not a process or any other type of thing. It goes far deeper than that. For me, it is a coherent world view that ties in well with other schools of thought I have been following like anthro-complexity. Yet there are also differences between these different schools of thought, which are important interfaces where learning can happen (the idea that learning happens at interfaces between different context is in itself an idea from Warm Data, but more on that later).

In order to be able to explain Warm Data, another concept needs to be explained first: transcontextuality. The idea of transcontextuality is that there are multiple different contexts that are interconnected and interdependent behind any single question, issue or thing we look at. Or as Nora Bateson puts it: “Whatever we are talking about, it is never just that and nothing more.” Let’s take a simple example of a subsistence farmer and the use of more productive seed. A simple linear logic would argue that once the farmer sees that she would get more crop with the new seed, she will adopt it. A transcontextual perspective would first look at the intersection of different contexts that play a role in such farmers’ decision: the history or the farmer and how she learned to farm, the family she is part of, the community she is part of, the financial system, the education system, the culture, the economy around the crop (both in terms of where she buys the seed – or if she can propagate it herself – but also where she sells the produce, linking her to longer value chains and possibly even global supply chains), politics (which includes taxes but also things like corruption or even extortion) and so on. One would also look at traditional knowledge that is available to the farmer and to habitual practice. And of course one would look at the particular ecosystem of which the farm is part of, with its own specificities like other species that are there like pests or beneficial species, the quality of the soil, the amount of rainfall and how that changes, and so on, and so forth. As we can see in this simple example, the discussion cannot be just about the improved seed and nothing more. 

Warm Data is about opening up this transcontextual perspective whenever we approach a complex living system. Nora uses the following definition of Warm Data: 

Warm Data is transcontextual information about the interrelationships that integrate a complex system.

The need for a Warm Data approach has grown out of the realisation that in complex living systems, you cannot fully understand what is happening if you pull something out of its context and study it in isolation. While this seems like a trivial insight, Warm Data is the first approach that genuinely expresses what that means if one thinks it through to the end. 

Of course our scientific approach has made great progress on understanding how things work in isolation. This is the way most science works – take something, isolate it, study it and put it back into the context. And we have gained a massive amount of knowledge like that. But it is static knowledge, it is necessarily limited as it excludes the interrelationships and interdependencies the ‘thing’ exhibits in its natural environment. 

Another important thought – maybe even more important – is that once you isolate a thing and describe it in isolation outside of the multiple contexts it forms part of, you can exploit it. For example, once trees are isolated as independent individual plants and studied and described as such, they become an object in themselves. An object that can be planted, cultivated in an ‘optimal’ way and then cut down. Or better even cut down from natural (rain) forests. However, in Warm Data one would ask: what are the multiple contexts a tree forms part of and how does it interrelate with these contexts? What interdependencies are trees part of? And, eventually, when thinking about this for long enough, one inevitably has to ask the question: where does the individual tree stop and the forest start? Each tree is interwoven with the forest for example through other organisms like mycelia, both while it is alive but also over time as part of a never ending cycle of growth and decay. Now ask the question again: what does it mean to cut down a tree or a whole lot of trees? We now see that this will influence a whole lot of contexts in ways that we cannot predict. And we now know that cutting down large parts of the Amazon rain forest, for example, is influencing global patterns of rain and draught.

There is nothing wrong with a reductionist approach to scientific inquiry. It has given us lots of knowledge and comfort. But we have to understand its limits. Unfortunately, we have elevated the reductionist approach to become our main lens through which we see the world. Because we have extrapolated the reductionist view on the whole world, indeed into every aspect of our culture, education, politics, families, lives, we have also as a human species brought our planet to the brink of collapse. Because we have disconnected trees from forests, animals from ecosystems, crops from the soil, work from family, school from learning for life, mental health from nutrition, etc. we have ignored the interrelations and interdependencies of all of these contexts. It is time now to put this back together again.

Title photo by veeterzy on Unsplash

Chris Corrigan: in our most creative moments, we feel invited into working together

I had the great pleasure to talk to Chris Corrigan for another episode of the Systemic Insight Podcast (Episode 9). Chris is a facilitator and an expert in complexity-sensitive facilitation techniques. He has been using similar methods and frameworks as Mesopartner, such as for example the Cynefin framework, developed by Prof. Dave Snowden. Chris describes himself as a process artist, a teacher and a facilitator of social technologies for face to face conversation in the service of emergence. His business is supporting invitation: the invitation to collaborate, to organise, to find one another and make a difference in our communities, organisations and lives.

Chris and I talked about the difference between being an expert that brings solutions and a facilitator that creates the conditions for emergence. We discussed the importance of invitation and respecting human dignity in collaborative processes and in dialogue. During the discussion, Chris described the Cynefin framework and how he uses it in his practice. Chris presented Cynefin as an incredibly useful generative framework and shared how we can use it to make sense of action.

Chris and I also discussed the current COVID-19 pandemic, how complexity concepts suddenly become very important and useful, and we explored quite deeply the relationship between leadership, moral and ethics, complexity and decision making.

Finally, we talked about the concept of dialogue and dialogic approaches and how they are fundamentally part of how we humans make sense of the world around us, interact and collaborate, and explore different options on how to act.

As the episode is 1.5h long and not everybody might be able to put aside enough time to listen to it, below some extracts of the podcast, partly verbatim quotes by Chris, partly paraphrased by me. Hopefully this is enough of a teaser so you will still go and listen to the full episode, its well worth it!

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Systemic change: A dance between structures and events

In the last post I wrote about systemic change as the conditions or structures that hold a situation in place. I introduced the six conditions for systems change by Kania, Kramer and Senge and showed the little inverted triangle that puts the conditions in neat boxes and a clear hierarchy – the deeper ‘down’ the condition, the more influential over the system. I also introduced again the systems iceberg, which has a similar hierarchy and logic.

These two things – the neat boxes and the implied hierarchy – kept bugging me. I know that in complex systems things are never that neat and never linear causal – there is not one thing in one box that leads to another thing in another box or to an observed behaviour. Reality is messier. I also missed the dynamics in these diagrams – how are these structure created, how do they persist, how do they change? So I want to follow up on this in this post.

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rigid boundary

Systemic change: changing the conditions that hold a situation in place (with a link to Covid-19)

Currently there seems to be a lot going on around systems change, particularly driven by a few organisations that have picked up the topic and drive it forward, including large philanthropic funders that so far have mainly focused on social entrepreneurship and social innovation. They seem to have raised that with simply scaling social enterprises or social innovations, systemic change does not happen and that a systemic view on change is needed. So they have started to put together research projects on figuring out what systemic change is. One thing that emerges from these initiatives is hopefully a wider agreement on what is meant by systems change, which all systems change practitioners could profit from. I’m happy to see that the way I have been thinking about systems change seems to be largely aligned with that emerging consensus.

The definition of what systemic change or systems change (I don’t think there is a difference between the two) is converges around changes in structures that shape the current situation. A very simple but useful definition of systems change comes from the Social Innovation Generation (SIG) in Canada: shifting the conditions that are holding the problem in place (quoted in Kania, Kramer and Senge 2018. This is in line with what I called transformative change – a transformation of the system’s structure. We do not just have to solve the problem but we want to transform the conditions that are holding the problem in place.

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Why outcome-based performance management doesn’t work: podcast with Dr Toby Lowe

Last week we published the 7th episode of the Systemic Insight podcast (get it from Libsyn or Apple Podcast or you can also find it on Spotify). It features Dr Toby Lowe of Northumbria University and his work on why outcome-based performance management doesn’t work – and what to do instead. In this blog post, I’m sharing some quotes from Toby and some insights I took from the conversation.

The discussion in the podcast touches upon why outcome targets distort rather than enhance performance, why they lead to gaming becoming a rational strategy, and what the alternatives are for people who work in complex contexts. As outcome-based performance management is still the prevalent method to manage the performance in many fields, this discussion is highly relevant and pertinent.

This episode is packed with ideas and quite challenging thoughts! In this blog post I am bringing together a number of quotes from Toby and some comments from my side.

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Sensemaking and portfolios – two important concepts when facing wicked and complex problems

In 2013, Richard Hummelbrunner and Harry Jones asked: “How can policy makers, managers and practitioners best plan in the face of complexity?”

Seven years later the search for answers to that question continues through different initiatives and programmes. For example, the Doing Development Differently Manifesto was published at the end of 2014. Matt Andrews and his colleagues at the Building State Capability program launched a successful online course on Problem Driven Iterative Adaptation and published a book in 2017. David Booth has written extensively about the adaptive and iterative approach of the Coalitions For Change programme of The Asia Foundation in The Philippines.

Arnaldo Pellini

So, where are we in this discussion? What are the challenges around transitioning ideas from complexity into projects and programmes? To answer these questions I have reached out to Arnaldo Pellini, founder of Capability, to hear about his experiences working with development initiatives and discuss some of the open questions we are yet to answer.

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Let’s put systemic change to rest

Last week I was facilitating a workshop with a group of very bright and experienced Market Systems Development (MSD) practitioners. As happens so often, at some point we discussed the concept of systemic change. This particular discussion reflected quite well the problem of the wider field of MSD: the group could not agree on how to assess whether a change they instigated has changed the system they are working in. While during most part of the discussion I was in the role of the facilitator and tried to keep my own thoughts out, my passion for the topic made me at some point step out of that role and bring in some of my own thoughts. I’m using this blog post to further clarify my point of view. Indeed, I am making the case that we should finally stop discussing about what systemic change is and move on to focusing on how to measure and communicate about it. In order to be able to do that, I’m suggesting a conceptual understanding of systemic change that I think is quite powerful and that I hope will enable us to put the discussion on what systemic change is to rest.

The discussion we were having last week was around four criteria that the group had decided were essential to assess whether a change was systemic: scale, sustainability, inclusiveness and transformation. Some of the questions the group was discussing on a very high level included whether we need all of these criteria or if we can collapse two into one (transformation into sustainability or the other way around), or whether some are more important than others (scale and sustainability for some, transformation for others). The group was also not clear on their definition of transformation, which is when I stepped out of my role as a facilitator and presented the systems iceberg to define transformative change. For me, change is transformative in a system when it changes the structural level, the constraints that shape the patterns of behaviour (see here for an explanation of the iceberg).

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change

How institutions change – and why nations fail

In my last post, I wrote about why institutions matter for economic development. I also highlighted that the theories of institutional economics and of complex systems actually come to very similar conclusions about how institutional structures, underpinned by basic beliefs or paradigms of how the world works, shape relatively persistent patterns of behaviour, which can be both beneficial for, or holding back development. In this post, I want to share a model that describes the dynamics of institutional change. It is largely based on Douglas North’s book ‘Understanding the Process of Economic Change’ [1], but uses the systems iceberg as a canvas. If you haven’t read my last post, I recommend you head over there and read that one first.

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