Category Archives: my work

The next step in systemic change

Over the course of 2016, Shawn and I worked on a piece of research on systemic change in market systems development, funded by the BEAM Exchange. In this work, we question the utility of the concept of systemic change in market systems development (though this is valid in the wider field of economic development) as it is currently used and suggest a rethink. To do so, we went back to search for a fundamental understanding of economic change. This is what we found.

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An alternative to a Theory of Change approach

I have been blogging quite extensively about the Theory of Change (ToC) approach in recent months. My blog posts reflect a process that I have been going through as part of my different work engagements: adapt ToC approaches to be more sensitive to the complexities development programmes face in their day-to-day work.

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Different phases of Systemic Insight

In parallel, with my colleagues at Mesopartner we keep doing research on understanding complex realities and our human reaction to them based on cognitive science, understanding the process of economic change, making decisions under conditions of uncertainty, and managing highly resilient organisations. In these contexts, ToC has limited applicability and a number of drawbacks. Therefore, we have been working on an alternative approach to the ToC approach which we built from the ground up based on our growing understanding of how complex systems work and how involved actors can lead a process of exploration and change. The approach is called Systemic Insight. Continue reading

Refining the Complexity aware Theory of Change

When I wrote my last post about experimenting with new structures for a complexity aware Theory of Change (ToC) in Myanmar, I had a few elements in place, but still some questions. Going further back to an earlier post, I was clear that differentiating between clear causal links for complicated issues and unpredictable causalities for complex ones is critical. I have been thinking about that a lot and last week I have taught a session on monitoring in complex contexts and I think I have found the final piece of the puzzle. Continue reading

Experimenting with new structures for Theory of Change

Last week I was in Myanmar working with a market
systems development programme. The main task of this trip is to work on the project’s monitoring framework. To set the stage for that, we are working on revising the project’s theory of change (ToC).

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A messy theory of change

Theory of Change is a bit of a contentious beast in my set of tools. As I am thinking and writing a lot on complexity and complex systems, I am aware that causality in complex systems can hardly ever be reduced to a straight line between two boxes and it is even more difficult to predict in advance how change will look like. It is not just that causalities are difficult to disentangle or predict in advance (it’s easier using hindsight), but that because of emergence there are other causalities at work than the linear material – billard-ball like – causality we are used to. But this is the topic of another blog post. So for me, Theory of Change is not an instrument to predict what change will happen but to create a coherent picture that explains why the project is doing what it is doing. Whether it contains any results or effects and lines from interventions to ultimate outcomes or whether it does only show how a set of unrelated safe-to-fail experiments interacts to create change is of less importance to me. Whether I use ToC at all really depends on the team. If I can get the team to grasp concepts of complex systems, attractors, dispositionalities, etc., I will not use a ToC approach but rather stay on the ‘purer’ complexity side of things. When a team is still strongly rooted in traditional development thinking where it is important to clearly define ideal future states, I will take the team from there and try to gently lead them towards recognising uncertainties and give them tools to deal with them, for example a complexity sensitive ToC.

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No clear causal chain but a ‘space of the possible’

I have written about ideas to make ToC ‘complexity sensitive’ by using Cynefin. Here in Myanmar I have not used Cynefin explicitly. I used a simple heuristic to find the complex issues the project is grappling with: problems where there are multiple competing hypothesis about why they exist (and where data/evidence supports multiple competing hypothesis), and opinions on how to solve them diverge, can be categorised as complex. For these problems, instead of drawing clear lines between boxes, I encouraged the project team to create a ‘space of plausible changes’ that they believe could be the result of project interventions. While this does not allow to measure change along a neat causal path, it still creates a sort of anticipatory awareness towards possible signs for change the team can pick up in the field.

This time I also experimented with a new structure for the ToC. Rather than going along the familiar route of ‘intervention-output-outcome-impact’, I changed the ‘layers’ (not sure if this is the right word) of the ToC as follows:

  1. Intervention: This level broadly describes the interventions without going into detailed activities as they might change frequently. Intervention are seen as triggers for stakeholders to react to.
  2. Uptake: This is about the immediate change the project wants to trigger on the level of the partner the team engages with. Essentially, this is the justification of why we work with these partners. What is the change in behaviour that we want to see on that level?
  3. Interaction of interventions/with context: This is where the changes taken up by project partners start to interact with each other and are exposed to the context. For example, how would increased knowledge of farmers on cultivation and production technique play out in the reality of a very difficult market that drives down rather than demands quality.
  4. Systemic change: this is the layer where we look at wider patterns of persistent failure or underperformance and how they could be more beneficial.

Really interesting here is ‘layer’ three. Firstly, this is the place where individual interventions start to interact and where synergies between interventions come into play. But more importantly, here we explicitly take in to account the context and how the interventions interact with the context. This requires us to have a pretty good idea about the context, predominant attitudes, perceptions, belief systems, etc. Questions we asked were: What are farmers going to do with new knowledge? Are they reacting in a conservative way and say they will continue to do things the way they have always done? Or will they embrace it? And if they embrace it, what will be the reaction of the market on the improved product quality and/or quantity? This is where other interventions come into play, for example the work with the traders or other larger down-stream buyers of the product.

This is also the layer where different hypotheses on how something is going to play out can be taken into account. As written above, this is not about the ideal causal pathway we want things to play out. This is about discussing possible favourable patterns that can be stimulated and create anticipatory awareness of them so they can be picked up by monitoring. Also, this is about discussing unfavourable patterns in order to be able to recognise these early and dampen if necessary. A discussion about this uncertainty will hopefully also help the team to pick up totally unanticipated effects or changes.

During the work I realised that I need to rethink layer 4 as well. Systemic changes were actually ‘pulled down’ during the discussion into layer 3 as they are (hopefully) an result of interacting interventions and interactions with context. So layer 4 is developing more into depicting what we assume will happen in the market once these changes take place and in particular what will happen to the programme beneficiaries.

More work is needed and the team in Myanmar will be important to see how the structure needs to be adapted to be useful for them.

Measuring transformations in attitudes

Following up on my last blog post on a new framework for systemic change, I would like to present here the main methodology we used to measure whether there have been transformations in the attitudes of farmers. The approach we used was Cognitive Edge’s SenseMaker®, which allowed us to deeply scan for changes in attitudes and beliefs beyond mere observation of changed behaviours. Continue reading

A new framework for assessing systemic change

Over the last year or so I was hired by a large market systems development programme in Bangladesh to develop a new framework for assessing systemic change for them. We did an initial feasibility study and then a larger pilot study. The report of the pilot study has now been published. Rather than to bore you with the whole report, I would like to share the conceptual thinking behind the framework and the framework itself in this post. In a later post, I will share the methodology. This is not the end of all wisdom and the silver bullet framework everybody has been looking for. For me this is an important step to bring my work and thinking over the last couple of years together into something practically applicable. But this work is not done as I am embarking on a longer research project on systemic change. So there is more learning to come and with it more development of this tool. Please share your thoughts, which would help me to further improve the framework. Continue reading

Mesopartner Summer Academy 2016

Mesopartner Summer Academy 2016 in Berlin

This week we have announced the Mesopartner Summer Academy 2016 on Territorial Economic Development. This year, the academy will have a special focus on green economic development in territories. The academy will take place from 4 to 6 July in Berlin, one of the most exciting capitals in Europe with a rich history of economic transition and development. I will be there an I hope to meet some of my readers as well. Continue reading