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What Kind of Science Do We Need for the Aid and Post-2015 Agenda?

Duncan Green's picture

Spent an intriguing evening last week speaking on a panel at the wonderful Royal Society (Isaac Newton and all that), on the links between the post-2015 agenda and science. The audience was from the government/science interface – people with job titles like ‘Head of Extreme Events’.

I talked (powerpoint here – keep clicking) about how science can help developmentistas by bringing them up to date with what science is actually about. Less Newton more Darwin, in terms of moving from a 19th Century world of linear causal chains, static equilibria and reductionism, to ecological and complexity thinking. I also tried linking some of the stuff I’ve been reading on complexity thinking with the Cynefin framework. It seems to me we need different kinds of science for the different quadrants:

  • Complex: complexity theory, evolutionary/ecological approaches
  • Knowable but complicated: more traditional analytic research methods aimed at nailing down causation
  • Known: Just identify and roll out best practice
  • Chaos: no idea – any suggestions?

The reason I like this is that it helps clarify when we need to bash our brains on complexity theory, and when we can stick with the old fashioned stuff. Convinced?

My other point was to stress that science has to address issues of power and distributive impact – issues like intellectual property rights and the current efforts to restrict poor countries’ access to medicines, but also the impact of new technologies. Geoengineering seemed to resonate as an example: it’s no good thinking about it as a purely technological challenge, you also have to think about winners and losers from its implementation (if they dump a million tonnes of iron filings in the oceans to absorb carbon, it isn’t going to be off the shores of Europe…..).

But enough about me, what did other people say? David Cameron’s post-2015 czar, Michael Anderson, was strikingly interested in complexity and uncertainty – a theme which dominated the evening. He stressed the political obstacles to taking them seriously – politicians don’t want to know; the public switches channel. Correcting that needs an educational effort from scientists, but also finding good ‘proxy indicators.’

Proxy indicators are magical: they take the temperature of a complex system well enough to be useful, and they communicate directly with policy makers and public. According to Michael, the maternal mortality rate is the perfect example – an excellent proxy indicator for the overall state of health systems and a powerful means of communicating with a wider audience. Michael reckons we need such ‘canary in the mine’ indicators to help tackle complex processes such as climate change, conflict or the sustainability of oceans (apparently phytoplankton levels are the best guide to ocean health, but don’t cut it with Joe Public, so they went for fish stocks in the High Level Panel report).

The overall discussion on the role of science was a bit all over the place – I guess ‘Science’ is a very big thing. Perceptions of science are deeply split: policy-makers see it as a source of certainty – ‘what works’, ‘this we know’, ‘facts’ – that they can cling to in their daily swirling clouds of opinion and ideology. But scientists don’t agree – they are much more aware of the limitations of scientific knowledge and the messiness of the world.

Some of the conversation was more about the downstream application of science to implement policies and achieve whatever goals are agreed. For Ban Ki-Moon’s post-2015 special adviser Amina Mohammed, the issues were building scientific capacity in developing countries (entirely missing from the MDGs), linking science-blind parliaments and politicians with nascent scientific communities, and dealing with slow/bad data.

Over dinner (Chatham House rules), multi-disciplinarity got a hostile reception – people reckoned that sometimes you need a single discipline, sometimes a combo – it depends. Better, perhaps to try and adopt a ‘problem driven approach’. Identify the problem, and then see which disciplines jump to the task – shades of Matt Andrews’ ‘problem driven iterative adaptation’ again.

The conversation got heated (appropriately) on climate change, with scientists laying into the civil servants about the necessity of at least discussing the implications for growth (‘growth is exponential; the planet is finite – it doesn’t add up’), and the civil servants wearily explaining the nature of political realities – you can’t question the primacy of growth and keep your job.

And one lovely quote from Isaac Newton himself, nicked from Ben Ramalingam’s forthcoming book Aid on the Edge of Chaos: ‘I can calculate the movements of heavenly bodies, but not the madness of men.’ True that, judging by an evening with the boffins.

This post first appeard on From Poverty to Power
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Comments

Submitted by David Flint on

Science for chaos: I'd use statistics, neural networks, etc., to extract patterns and distributions. Even if I could not find causes I'd have a better characterisation of the phenomena.

I suppose Chaos Theory ought to work but I've no idea whether it ever does.

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