A guide for planning and strategy development in the face of complexity

By Richard Hummelbrunner and Harry Jones
ODI Background Note, March 2013. Available as pdf

“Many argue that governments, non-governmental organisations and international agencies should spend less time planning in advance and more time adapting programmes to changing scenarios and learning by doing. In the complex context of development, how can policy makers, managers and practitioners best plan in the face of complexity? Does complexity make planning an irrelevant exercise?

This Background Note is a guide, explaining how planning and strategy development can be carried out despite complexity. While it is true that complex situations require a greater focus on learning and adaptation, this does not render plan­ning irrelevant. In fact, there are ways in which the processes and products of planning can respect the realities of the situation and set up interven­tions (policies, programmes and projects) to give them the best chance of success.

The guide builds on academic, policy and programmatic literature related to themes around systems and complexity  and draws on the authors’ experience of advising development agencies and governments in both developed and developing countries.

The note covers three points:

  1. How to recognise a complex situation what challenges it will pose
  2. Principles for planning in the face of complexity
  3. Examples of planning approaches that address complexity”

Rick Davies comment: Over two hundred years ago William Blake exclaimed in verse “Pray God us keep From Single vision & Newton’s sleep”  If he was to read the current literature on complexity, planning and evaluation he might be tempted to repeat his advice, again and again, until it seeped through. Why do I think this? I searched this ODI paper for three magic words: diversity, difference and variation. Their existence in real life is the raw fuel for evolutionary processes, one that has enabled living organisms to survive amidst radically changeable environments over aeons of time on earth. And lo and behold, most of these organisms dont seem to have much in the way of planning units or strategy formulation processes. Evolution is a remarkably effective but non-teleological (i.e. goal driven) process of innovation and adaptation.

While I did not find the words diversity and variation in the ODI text, I was pleased for find one brief reference to the use of evolutionary processes, as follows:

Another option is an ‘evolutionary’ approach, whereby a plan is not seen as single ‘big bet’ but rather as a portfolio of experiments, by setting an over-riding goal and then pursuing a diverse sets of plans simultaneously, each of which has the potential to evolve. We could also adopt a ‘breadth first’ approach with ‘trial and error’ as the central aim of the initial stage of implementation, to encourage parallel testing of a variety of small-scale interventions”

One means of ensuring sufficient diversity in experiments is to decentralise resources and the control over those resources. This can happen in projects which have explicit empowerment objectives and also in other kinds of projects that are large in scale and working in a diversity of environments, where central controls can be loosened, either by accident or intention. In my experience there are already plenty of natural experiments with experimentation underway, the problem is the failure to capitalise on them. One reason being the continued fixation with a single vision, that is, an over-arching Theory of Change, embeded in a LogFrame and/or other planning formats, which end up dominating evaluators’ attention and use of time. This includes my own evaluation practice, mea culpa, notably with four projects in Indonesia between 2005 and 2010.

The alternative is to develop testable models that incorporate mulliple causal pathways. In the past I have emphasised the potential of network models of change, where changes can be affected via multiple influence pathways within complex networks of relationships between different actors. The challenge with this approach is to develop adequate descriptions of those networks and the pathways within them. More recently I have been argueing for the use of a simpler representational device, known as Decision Tree models, which can be constructed, and triangulated, using a variety of means (QCA, data mining algorithms, participatory and ethnographic techniques). The characteristics of a portfolio of diverse activities can be summarised in the form of Decision Tree models, which can then be tested for their  degree of fit with observed differences in outcomes of those activities. The structure of Decision Tree models enables them to represent multiple configurations of different causal conditions, identified before and/or after their implementation. More information on their design and use is provided in this paper “Where there is no single Theory of Change: The uses of Decision Tree models” While I have shared this paper with various writers on evaluation and complexity, none seem to have seen its relevance to complexity issues, possibly because in many writings on complexity, the whole issue of diversity gets much less attention than the issue of unpredictablity. I say this with some hesitation, since Ben Ramalingam’s forthcoming book on complexity does have a whole section on the perils of “Best-practicitis” i.e single vision views of development.

Incidentally, for an interesting but demanding read on the many relationships between diversity and complexity I recommend Scott Page’s “Diversity and Complexity” (2011)


Diversity and Complexity

by Scott Page, 2011. Available on Google Books Princeton University Press, 14/07/2011 – 296 pages

Abstract: This book provides an introduction to the role of diversity in complex adaptive systems. A complex system–such as an economy or a tropical ecosystem–consists of interacting adaptive entities that produce dynamic patterns and structures. Diversity plays a different role in a complex system than it does in an equilibrium system, where it often merely produces variation around the mean for performance measures. In complex adaptive systems, diversity makes fundamental contributions to system performance. Scott Page gives a concise primer on how diversity happens, how it is maintained, and how it affects complex systems. He explains how diversity underpins system level robustness, allowing for multiple responses to external shocks and internal adaptations; how it provides the seeds for large events by creating outliers that fuel tipping points; and how it drives novelty and innovation. Page looks at the different kinds of diversity–variations within and across types, and distinct community compositions and interaction structures–and covers the evolution of diversity within complex systems and the factors that determine the amount of maintained diversity within a system.Provides a concise and accessible introduction. Shows how diversity underpins robustness and fuels tipping points .Covers all types of diversity. The essential primer on diversity in complex adaptive systems.

RD Comment: This book is very useful for thinking about the measurement of diversity. In 2000 I wrote a paper “Does Empowerment Start At Home? And If So, How Will We Recognise It?” in which I argued that…

“At the population level, diversity of behaviour can be seen as a gross indicator of agency (of the ability to make choices), relative to homogenous behaviour by the same set of people. Diversity of behaviour suggests there is a range of possibilities which individuals can pursue. At the other extreme is standardisation of behaviour, which we often associate with limited choice. The most notable example being perhaps that of an army. An army is a highly organised structure where individuality is not encouraged, and where standardised and predictable behaviour is very important. Like the term “NGO” or “non-profit”, diversity is defined by something that it is not –  a condition where there is no common constraint, which would otherwise lead to a homogeneity of response. Homogeneity of behaviour may arise from various sources of constraint. A flood may force all farmers in a large area to move their animals to the high ground. Everybody’s responses are the same, when compared to what they would be doing on normal day. At a certain time of the year all farmers may be planting the same crop. Here homogeneity of practice may reflect common constraints arising from a combination of sources: the nature of the physical environment, and the nature of particular local economies. Constraints on diversity can also arise within the assisting organisation. Credit programs can impose rules on loan use, specific repayment schedules and loan terms, as well as limiting when access to credit is available, or how quickly approval will be give.”

See also…