Emerging from what?

One of the most illuminating insights in Ben Ramalingam’s Aid on the Edge of Chaos (see last week’s review) was on how well suited complexity science is to tackling issues of systemic risk in the global financial system (e.g. too much inter-connectedness amongst banks and other financial institutions). The reason being that there was a wealth of data, millions upon millions of individual transactions, just sitting there waiting to be analysed if only someone could bring the right toolset (and a powerful enough computer). Complexity science can take all that apparent randomness and help us tease out significant emergent patterns and behaviour.

I thought this was particularly illuminating because it perfectly illustrates one of the major challenges of bringing complexity science techniques to bear on development problems: for the most part we do not already have the data, and going out and collecting it is very expensive. Different analytical approaches no doubt differ in their data requirements, but I suspect that in many cases that chaos nerds have an even bigger problem in this respect than randomistas. In short without the huge morass of data there is too little random feedstock from which patterns can emerge.

If we combine that problem with one of the main challenges to RCT’s global domination – limited external validity when context is everything* – I am worried that complexity thinking may sometimes me the equivalent of the proverbial sledgehammer used to crack a nut. It may be that the nut is so hard to crack that nothing short of a sledgehammer will suffice to do the job, but the reality is that we cannot go round deploying chaos science sledgehammers everywhere, not least because I doubt there are enough capable chaosistas.

But there is another emergent pattern out there, of bloggers sounding really stupid when they write about things they don’t understand. So now maybe Ben and co can tell me how badly I am wrong …

* In chaotic systems this is represented in the extreme sensitivity to initial conditions, hence the joke about the butterfly flapping its wings in the rainforest triggering a thunderstorm on the other side of the world.

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The power law and why Aid is SNAFU

According to Ben Ramalingam’s new book, Aid on the Edge of Chaos, emergent characteristics of complex systems (a category that covers most targets of international development aid) often follow a power law in which most results are clustered together but which are offset by a long tail, e.g. lots of mostly poor people and a few incredibly, stinking rich folk. This tail is fatter, and contains much more extreme elements than you would expect from a Normal distribution, so that ignoring it as a few outliers can be incredibly dangerous. Conversely, such as in the case of earthquakes, the tail commands all the attention over swiftly forgotten smaller events. Maybe the same could be said of success rates in Aid projects? Boosters focus on the Green Revolution and the eradication of small-pox ‘fat tail’, sceptics obsess about the vast majority of Aid projects and spending which appears to achieve very little. Both are right, and both are wrong, and both could probably do with reading Ramalingam’s book.

I hope the title does not put people off the book. It suggests to me an anarchic office environment with harassed over-worked managers, when in fact the chaos that results from too many aid projects is rather more slow moving, if no less SNAFU. Ramalingam does have a good justification for his choice of title, but you will have to get to the final few paragraphs to understand it.

The book comes in three parts, and is a mixed read. The first part is a well-written indictment of the many failures and hubris of the international aid system. It treads familiar ground for anyone who has read Ferguson, Easterly and others. I have yet to tire of reading such critiques partly, perhaps, because they fit well with my prior beliefs, but also because such tales of failure are often instructive, useful to remind oneself what not to do!

Some of the targets may be easy, but all the more deserving of criticism. Occasionally it over-reaches, e.g. in condemning the reliance of orthodox economics on the idealised Homo economicus without acknowledging the many useful findings it has produced. Perhaps better editing would have helped, since such lapses are an easy mistake to make when a polemicist’s blood is up, but they do not detract significantly from the argument.

The second part introduces the reader to the power law and other elements of complexity science, and struggles manfully against the reader’s presumed lack of familiarity with this difficult subject. Part of the problem it faces, I think, is that complexity science is as yet a very young discipline. Theory and understanding are still very much in development, hence appropriate analogies and clear explanations are not well established. This presents a barrier to comprehension of a school of thought that is conceptually difficult to grasp.

Given this challenge it is ironic that the book suffers from too much space given over to this part, presumably in some attempt to keep the book balanced between the three parts. Such space has to be used up somehow: Ramalingam has chosen to do so through regular diversions into the history of complexity science. It is laudable that Ramalingam wants to tip his hat to the giants in his field, but such diversions are not fully contextualised (since this is not a history of the development of complexity sciences) and thus not especially illuminating. They are also somewhat repetitive, and distracting from the main argument.

One criticism of the use of complexity theory in development is that it is good for telling us after the fact what went wrong, hence the long list of shame in part one. But it often seems less good at telling us what we should do instead. This is slightly unfair because for the biggest aid agencies with millions of dollars to spend, investing $50,000 (say) in a complexity assessment could save a lot of money from being wasted on a doomed project. (If only aid agency incentives worked that way …) Ramalingam makes this point, but then in part three goes further with a series of examples of where complexity thinking has been used positively to underpin some highly successful development programmes.

How you respond to these examples may depend upon your background. I loved the on-the-ground examples such as the Subak system for irrigation management in Bali, the ecosystem-based approach to tackling endemic malaria in parts of Kenya, and using positive deviance to find ways to reduce child malnutrition in Vietnam, but others left me wondering “So what?” Conversely the talk of chaotic patterns in epidemiology may leave anyone with a decent grounding in ecological population dynamics thinking “Well, duh!” Some of these examples could thus perhaps have done with a better connection back to the critiques of part one to highlight why the use of complexity science is important.

Pressures of time meant that I took much longer to finish reading the book than ideal, and it maybe that a more focused reading would have been easier on the brain, but by the end I found the book frustrating. The overall aims and structure of the book are clear, but in the detail Ramalingam often appears to lose sight of where he is going with too many digressions in what may be an attempt to humanise an extremely abstruse subject. Ultimately I think the book needed more on aid and aid projects, and how they can be improved by the introduction of complexity science, and less on complexity science itself and its practitioners.

All of which is a pity because the ideas contained within the book are incredibly important. Indeed, despite those flaws, I have little hesitation in recommending it to anyone working in development or in developing countries. Unfortunately I suspect the very deliberate (and quite correct) decision not to offer any panaceas will limit the book’s impact on how most aid agencies operate. The world will be poorer as a result.