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
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).
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.
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:
- 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.
- 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?
- 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.
- 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.
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
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
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
Getting too eager about building the perfect Theory of Change (ToC) for your organisation, programme or project can lead to an over-designed ToC that can be more of a hindrance than a help to manage and learn. It sucks up a lot of time and team resources to build but then gets out-dated extremely quickly. A ToC should be an idea that is alive and dynamic. For me a ToC is more useful if it is a sketch on the back of an envelope after an intense discussion rather than a page in a high-gloss brochure. A ToC in a complex setting is necessarily imperfect. But it can still be extremely useful. Continue reading
Continuing my little emerging series on Theories of Change, there is another issue that I feel is very important in connection with complexity-informed Theories of Change: they do not need to be based on total agreement among the stakeholders. On the contrary, it is important to understand where there is agreement on causalities among the stakeholders and where there is not as this gives us important insight on the complexity of specific links in the logical chain.
When we look at the Theory of Change literature, participation comes up as an important if not central element of a Theory of Change process. And it undeniably is. Bringing in a wide range of stakeholders ensures that we get all or many of the diverse perspectives reflected in the Theory of Change process – and as I have written earlier, understanding diverse perspectives is a corner stone of systemic thinking. Continue reading