Evolutionary strategies for complex environments

 [This post is in response to Owen Barder’s blog posting “If development is complex, is the results agenda bunk?”]

Variation, selection and retention is at the core of the evolutionary algorithm. This algorithm has enabled the development incredibly sophisticated organisms able to survive in a diversity of complex environments over vast spans of time. Following the advent of computerisation the same algorithm has been employed by homo sapiens to solve complex design and optimisation problems in many fields of science and technology. It has also informed thinking about the history and philosophy of science (Toulmin, Hull, 1988, Dennett, 1996) and even cosmology (Lee Smolin, 1997). Even advocates of experimental approaches to building knowledge, now much debated by development agencies and workers, have been keen advocates of evolutionary views on the nature of learning (Donald Campbell, 1969)

So, it is good to see these ideas being publicised by the likes of Owen Barder. I would like to support his efforts by pointing out that the application of an evolutionary approach to learning and knowledge may in fact be easier than it seems on first reading of Owen’s blog. I have two propositions for consideration.

1. Re Variation: New types of development projects may not be needed. From 2006 to 2010 I led annual reviews of four different maternal and infant health projects in Indonesia. All of these projects all were being implemented in multiple districts.  In Indonesia district authorities have considerable autonomy. Not surprisingly, the ways the project was being implemented in each district varied, both intentionally and unintentionally. So did the results. But this diversity of contexts, interventions and outcomes was not exploited by the LogFrame based monitoring systems associated with each project. The LogFrames presented a singular view of the “the project”, one where aggregated judgements were needed about the whole set of districts that were involved. Diversity existed but was not being recognised and fully exploited. In my experience this phenomena is widespread. Development projects are frequently implemented in multiple locations in parallel. In practice implementation often varies across locations, by accident and intention. There is often no shortage of variation. There is however a shortage of attention to such variations. The problem is not so much in project design as in M&E approaches that fail to demand attention to variation - to ranges and exceptions as well as central tendencies and aggregate numbers.

2. Re Selection: Fitness tests are not that difficult to set up, once you recognise and make use of internal diversity. Locations within a project can be rank ordered by expected success, then rank ordered by observed success, using participatory and/or other methods. The rank order correlation of these two measures is a measure of fitness, of design to context. Outliers are the important learning opportunities (high expected & low actual success, low expected & high actual success) that warrant detailed case studies. The other extremes (most expected & actual success, least expected & actual success) also need investigation to make sure the internal causal mechanisms are as per the prior Theory of Change that informed the ranking.

It is possible to incorporate evolutionary ideas into the design of M&E systems. Some readers may know some of the background to the Most Significant Changes impact monitoring technique. Its design was informed by evolutionary epistemology. The MSC process deliberately includes an iterated process of exploiting diversity (of perceptions of change), subjecting these to selection processes (via structured choice exercises by stakeholders) and retention (of selected change accounts for further use by the organisation involved). MSC was tried out by a Bangladeshi NGO in 1993, retained and then expended in use over the next ten years. In parallel, it was also tried out by development agencies outside Bangladesh in the following years, and is now widely used by development NGOs. As a technique it has survived and proliferated. Although it is based on evolutionary ideas, I suspect that no more than 1 in 20 users might recognise this. No matter, nor are finches likely to be aware of Darwin’s evolutionary theory. Ideally the same might apply to good applications of complexity theory.

Our current thinking about Theories of Change (ToC) is ripe for some revolutionary thinking, aided by evolutionary perspective on the importance of variation and diversity. Singular theories abound, both in textbooks (Funnell and Rogers, 2011) and in examples developed in practice by organisations I have been in contact with. All simple results chain models are by definition singular theories of change. More complex network-like models with multiple pathways to a given set of expected outcomes are a step in the right direction. I have seen these in some DFID policy area ToCs. But what is really needed are models that consider a diversity of outcomes as well as means of getting there. One possible means of representing these models, which I am currently exploring, is the use of Decision Trees. Another, which I explored many years ago, and which I think deserves more attention, is a scenario planning type tool called Evolving Storylines. Both make use of divergent tree structures, as did Darwin when illustrating his conception of evolutionary process in his Origin of Species.