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.


3 responses to this post.

  1. Posted by am on June 5, 2014 at 6:44 pm

    Sorry, I can’t resist, and comment makers too and I fully reserve my right to write nonsense although I think it makes perfect sense.

    Sledgehammer to crack a nut does sound like a description of development not just chaos theory. Has anyone ever charted the various schools, theories, RCTS and silver bullets over the history of development that have turned out to be not very conducive to development. Why not sub-contract it all to China.


  2. Data is definitely a limitation of complex systems applications in development, but it is a limitation in development research more generally.

    One way around this is to using what is available – the Harvard Centre for International Development work on network analyses of economics is a great example of exactly this. It has been able to map some 150 countries, and is clear where the model cannot work well due to data limitations. This doesn’t make it useless – far from it. Mainstays of development, from the Human Development index to the State of the World Children report, started with exactly this principle.

    These approaches have helped to show why datasets are so important – with the goal of doing better, more evidence-based analyses of development problems, whatever method we use (complexity or otherwise).

    The other, which applied to a number of the case studies in my book, is to apply complexity tools using more qualitative and participatory approaches. Some of these have been very successful as a means of enabling and facilitating bottom-up change. In the short term, this may be where more of the development applications will fit.


    • Thanks for the contribution. The danger of working with what data is there, is that you end up in the every problem looks like a nail to the guy with a hammer. (A problem I am quite sure you would avoid! But others might not.) It would be great to see what problems can be solved with such existing data. Indeed as one of life’s opportunists I think it would be great to encourage active use of such data, just so long as it doesn’t become the latest bandwagon (like m-health) on to which everything has to be loaded however appropriate or not.

      And drawing upon your comment about the importance of data-sets, it would be even better if such ideas could stimulate ever greater openness with the huge amount of household data that is collected by organisations around the world and never shared. We could scrap the enormous waste of resources from repeated efforts, and open up whole new opportunities for big data research. Confidentiality issues could surely be resolved, but what about instinctive secretiveness?


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