"If you want to think outside of the box, you first need to find the box" - some practical evaluative thinking about Futures Literacy

Over the last two days, I have participated in a Futures Literacy Lab, run by Riel Miller and organised as part of UNESCO's Futures Literacy Summit. Here are some off-the-cuff reflections.

Firstly the definition of futures literacy. I could not find a decent one, but my search was brief so I expect readers of this blog posting will quickly come up with a decent one. Until then this is my provisional interpretation. Futures literacy includes two types of skills, both of which need to be mastered, although some people will be better at one type than the other:

1. The ability to generate many different alternative views of what might happen in the future.

2. The ability to evaluate a diversity of alternative views of the future, using a range of potentially relevant criteria.

There is probably also a third skill, i.e. the ability to extract useful implications for action from the above two activities,  

The process that I took part in highlighted to me (perhaps not surprising because I'm an evaluator) the importance of the second type of skill above - evaluation. There are two reasons I can think of for taking this view:

1. The ability to critically evaluate one's ideas (e.g. multiple different views of the possible future) is a metacognitive skill which is essential. There is no value in being able to to generate many imagined futures if one is then incapable of sorting the "wheat from the chaff" - however that may be defined.

2. The ability to evaluate a diversity of alternative views of the future, can actually have a useful feedback effect, enabling us to improve the way we search for other imagined futures

Here is my argument for the second claim. In the first part of the exercise yesterday each participant was asked to imagine a possible future development in the way that evaluation will be done, and the role of evaluators, in the year 2050. We were asked to place these ideas on Post-It Notes on an online whiteboard, on a linear scale that ranged between Optimistic and Pessimistic. 

Then a second and orthogonal scale was introduced, which ranged from "I can make a difference" to I can't make a difference". When that second axis was introduced we were asked to adjust our Post-It Notes into a new position that represented our view of its possibility and our ability to make a difference to that event.  These two steps can be seen as a form of self-evaluation of our own imagined futures. Here is the result (don't bother try to read the note details).

Later on, as the process proceeded we were encouraged to 'think out of the box" But how do you do that, ...how do you know what is "out of the box"? Unless you deliberately go to extremes, with the associated risk that whatever you come up with be less useful (however defined)

Looking back at that task now, it strikes me that what the above scatterplot does is show you where the box is, so to speak.  And by contrast, where outside the box also is located.  "Inside the box" is the part of scatterplot where the biggest concentration of posts is located.  The emptiest area and thus most "out of the box" area is the top right quadrant.  There is only one Post-it Note there. So, if more out of the box thinking is needed in this particular exercise setting then perhaps we should start brainstorming about "Optimistic future possibilities and of a kind where I think "I can't make a difference"  - now there is a challenge!

The above example can be considered as a kind of toy model, a simple version of a larger and more complex range of possible applications. That is, that any combination of evaluative dimensions will generate a combinatorial space, which will be densely populated with ideas about possible futures in some areas and empty in others.  To explore those kinds of areas we will need to do some imaginative thinking at a higher level of abstraction, i.e. of the different kinds of evaluative dimensions that might be relevant. My impression is that this meta-territory has not yet been explored very much. When you look at the futures/foresight literature the most common evaluative dimensions are those of "possibility" and "desirability" (and ones I have used myself, within the ParEvo app). But there must be others that are also relevant in various circumstances.

Postscript 2020 12 11: This afternoon we had a meeting to review the Futures Literacy Lab experience. In that meeting one of the facilitators produced this definition of Futures Literacy, which I have visibly edited, to improve it :-)

 Lots more to be discussed here, for example:

1. Different search strategies that can be used to find interesting alternate futures. For example, random search, and "the adjacent possible" searches are two that come to mind

2. Ways of getting more value from the alternate futures already identified e.g. by recombination 

3. Ways of mapping the diversity of alternate futures that have already been identified e.g using network maps of kind I discussed earlier on this blog (Evaluating Innovation)

4. The potential worth of getting independent third parties to review/evaluate the (a) contents generated by participants, and (b) participants' self-evaluations of their content

For an earlier discussion of mine that might be of interest, see 

"Evaluating the Future". Podcast and paper prepared with and for the EU Evaluation Support Services Unit, 2020

Has the meaning of impact evaluation been hijacked?

 

This morning I have been reading, with interest, Giel Ton's 2020 paper: Development policy and impact evaluation: Contribution analysis for learning and accountability in private sector development 

 I have one immediate reaction; which I must admit I have been storing up for some time.  It is to do with what I would call the hijacking of the meaning or definition of 'impact evaluation'.  These days impact evaluation seems to be all about causal attribution. But I think this is an overly narrow definition and almost self-serving of the interests of those trying to promote methods specifically dealing with causal attribution e.g., experimental studies, realist evaluation, contribution analysis and process tracing. (PS: This is not something I am accusing Giel of doing!)

 I would like to see impact evaluations widen their perspective in the following way:

1. Description: Spend time describing the many forms of impact a particular intervention is having. I think the technical term here is multifinality. In a private-sector development programme, multifinality is an extremely likely phenomenon.  I think Giel has in effect said so at the beginning of his paper: " Generally, PSD programmes generate outcomes in a wide range of private sector firms in the recipient country (and often also in the donor country), directly or indirectly."

 2. Valuation: Spend time seeking relevant participants’ valuations of the different forms of impact they are experiencing and/or observing. I'm not talking here about narrow economic definitions of value, but the wider moral perspective on how people value things - the interpretations and associated judgements they make. Participatory approaches to development and evaluation in the 1990s gave a lot of attention to people's valuation of their experiences, but this perspective seems to have disappeared into the background in most discussions of impact evaluation these days. In my view, how people value what is happening should be at the heart of evaluation, not an afterthought. Perhaps we need to routinely highlight the stem of the word Evaluation.

 3. Explanation: Yes, do also seek explanations of how different interventions worked and failed to work (aka causal attribution).  Paying attention of course to heterogeneity, both in the forms of equifinality and multifinality Please Note: I am not arguing that causal attribution should be ignored - just placed within a wider perspective! It is part of the picture, not the whole picture.

 4. Prediction: And in the process don't be too dismissive of the value of identifying reliable predictions that may be useful in future programmes, even if the causal mechanisms are not known or perhaps are not even there.  When it comes to future events there are some that we may be able to change or influence, because we have accumulated useful explanatory knowledge.  But there are also many which we acknowledge are beyond our ability to change, but where with good predictive knowledge we still may be able to respond to appropriately.

Two examples, one contemporary, one very old: If someone could give me a predictive model of sharemarket price movements that had even a modest 55% accuracy I would grab it and run, even though the likelihood of finding any associated causal mechanism would probably be very slim.  Because I’m not a billionaire investor, I have no expectation of being able to use an explanatory model to actually change the way markets behave.  But I do think I could respond in a timely way if I had relevant predictive knowledge.

 Similarly, with the movements of the sun, people have had predictive knowledge about the movement of the sun for millennia, and this informed their agricultural practices.  But even now that we have much improved explanatory knowledge about the sun’s movement few feel this would think that this will help us change the way the seasons progress.

 I will now continue reading Giel's paper…

2021 02 19: I have just come across a special issue of the Evaluation journal of Australasia, on the subject of values. Here is the Editorial section.