Connecting Scenario Planning and Theories of Change

This blog posting was prompted by Tom Aston’s recent comment at the end of an article about theories of change and their difficulties.  There he said “I do think that there are opportunities to combine Theories Of Change with scenario planning. In particular, context monitoring and assumption monitoring are intimately connected. So, there’s an area for further exploration”

Scenario planning, in its various forms, typically generates multiple narratives about what might happen in the future. A Theory Of Change does something similar but in a different way.  It is usually in a more diagrammatic rather than narrative form. Often it is simply about one particular view of how change might happen i.e., particular causal pathway or package thereof.  But in more complex network representations Theories Of Change do implicitly present multiple views of the future, in as much as there are multiple causal pathways that can work through these networks.

ParEvo is a participatory approach to scenario planning which I have developed and which has some relevance to discussion of the relationship between scenario planning and Theories Of Change.  ParEvo is different from many scenario planning methods in that it typically generates a larger number of alternative narratives about the future, and these narratives proceed rather than follow a more abstract analysis of causal processes that might be at work generating those narratives. My notion is that this narrative–first approach involves less cognitive demands on the participants, and is an easier activity to get participants engaged in from the beginning. Another point worth noting about the narratives is that they are collectively constructed, by different self-identified combinations of (anonymised) participants.

At the end of a ParEvo exercise participants are asked to rate all the surviving storylines in terms of their likelihood of happening in real life and their desirability.  These ratings can then be displayed in a scatterplot, of the kind shown in the two examples below.  The numbered points in the scatterplot are IDs for specific storylines generated in the same ParEvo exercise. Each of the two scatterplot represents a different ParEvo exercise.

 

The location of particular storylines in a scatterplot has consequences. I would argue that storylines which are in the likely but undesirable quadrant of the scatterplot deserve the most immediate attention.  They constitute risks which, if at all possible, need to be forfended, or at least responded to appropriately when they do take place. The storylines in the unlikely but desirable quadrant problem justify the next lot of attention.  This is the territory of opportunity. The focus here would be on identifying ways of enabling aspects of those developments to take place.  

Then attention could move to the likely and desirable quadrant.  Here attention could be given to the relationship between what is anticipated in the storylines and any pre-existing Theory Of Change.  The narratives in this quadrant may suggest necessary revisions to the Theory Of Change.  Or, the Theory of Change may highlight what is missing or misconceived in the narratives. The early reflections on the risk and opportunity quadrants might also have implications for revisions to the Theory Of Change.

The fourth quadrant contains those storylines which are seen as unlikely and undesirable.  Perhaps the appropriate response here is simply to periodically to check and update the judgements about likelihood and undesirability.

These four views can be likened to the different views seen from within a car.  There is the front view, which is concerned about likely and desirable events, our expected an intended direction of change.  Then there are two peripheral views, to the right and left, which are concerned with risks and opportunities, present in the desirable but unlikely, and undesirable but likely quadrants. Then there is the rear view, out the back, looking at undesirable and unlikely events.

In this explanation I have talked about storylines in different quadrants, but in the actual scatterplots develop so far the picture is a bit more complex.  Some storylines are way out in the corners of the scatterplot and clearly need attention, but others are more muted and mixed in the position characteristics, so prioritising which of these to give attention to first versus later could be a challenge.

There is also a less visible third dimension to this scatterplot. Some of the participants judgements about likelihood and desirability were not unanimous. These are the red dots in the scatterplot above. In these instances some resolution of differences of opinion about the storylines would need to be the first priority. However it is likely that some of these differences will not be resolvable, so these particular storylines will fall into the category of "Knightian uncertainties", where probabilities are simply unknown. These types of developments can't be planned for in the same way as the others where some judgements about likelihood could be made. This is the territory where bet hedging strategies are appropriate, a strategy seen both in evolutionary biology and in human affairs.  Bet hedging is a response which will be functional in most situations but optimal in none. For example the accumulation of capital reserves in a company, which provides insurance against unexpected shocks, but which is at the cost of efficient use of capital..

There are some other opportunities for connecting thinking about Theories Of Change and the multiple alternative futures that can be identified through a ParEvo process.  These relate to systems type modelling that can be done by extracting keywords from the narratives and mapping their cooccurrence in the paragraphs that make up these narratives, using social network analysis visualisation software.  I will describe these in more detail in the near future, hopefully.