I’ve been bouncing some ideas around with Irene Guijt on how aid agencies can/should work in what we call ‘fragile and conflict-affected settings’ (FCAS). This matters because FCAS are where a lot of the aid business (both donors and INGOs) will end up, as more stable countries grow their way out of aid dependence (and a good thing too).
Let’s start with a simple framework, Cynefin, which divides up situations into four broad types.
- Simple/clear: (known knowns, in Rumsfeld terminology). The situation is stable, and the relationship between cause and effect is clear: if you do X (walk across a motorway), expect Y (sudden death). Response? Find the appropriate rule/toolkit and apply
- Complicated: (known unknowns). The relationship between cause and effect requires analysis or expertise; there are a range of right answers. Example, sending a spaceship to the moon and getting it back again. Response? Combine expertise from different sources. Artificial intelligence copes well here: Deep Blue plays chess as if it were a complicated problem, looking at every possible sequence of moves.
- Complex: (unknown unknowns). Cause and effect can only be deduced in retrospect, and there are no right answers. Examples: battlefields, markets, ecosystems and corporate cultures. Response? Trial and error with quick feedback loops for fast adaptation.
- Chaotic: (yikes!) Response? Staunch the bleeding/fight the fire/stem the panic. Try and get back to islands of complexity.
The aid business (along with academia, businesses, think tanks, government departments etc) mainly sees the world in terms of the right-hand side of Cynefin – problems and tasks that are either simple or complicated, like building a bridge, but soluble. That’s the basis for projects, logframes and all the rest of the guidelines that the aid business takes for granted.
Critics of this ‘business as usual’ approach recognize that aid actually often operates in countries and systems on the left-hand side of Cynefin. Cue lots of discussions and experimentation on ‘adaptive management’, ‘thinking and working politically’ etc etc, all faithfully reflected on this blog.
But I’m worried that the new conversation has blurred the distinction between the ‘complex’ and ‘chaotic’ quadrants, with some concerning consequences. When I was researching ‘adaptive management’ case studies for the Action for Empowerment and Accountability research programme, we wanted to look at how DFID-funded governance programmes operate in FCAS. But in the end we had to settle for countries or sub-regions that weren’t actually that fragile or conflict -Myanmar, Nigeria and Tanzania. These had functioning governments, CSOs, laws etc, at least before Magufuli in Tanzania and Myanmar-before-the-coup. Carrying out research in more chaotic settings were simply too inaccessible and/or dangerous.
The problem is that the lessons we and others drew on how to operate in complex settings, which I think are valid and really interesting, don’t necessarily hold for truly chaotic settings, for example those where no-one knows who’s in charge, and everything changes from one day to the next. So what kind of aid programming might work there? Some initial thoughts, but please add your own.
Firstly, there is humanitarian aid. This is the default, and very necessary, response in situations where lives are disrupted and survival put at risk. Interestingly this is a simple response to sometimes chaotic contexts. It’s like fire fighters – very rule-based responses developed for ‘chaos’.
Beyond fire-fighting, no country or situation is completely chaotic – there will be islands of social and political solidity that donors can identify and support. But these grains may well be different from the normal partners (the state, formal civil society organizations) preferred in the other quadrants. Here are three candidates for promising islands worth exploring, but I’d love to hear about others:
Individuals and Leaders: even in the maelstrom, the same people will crop up, organizing their fellows, finding ways to improve things. In the DRC, Oxfam supported Civilian Protection Committees that organized local people to negotiate the day to day hassles of living in the middle of a war zone, like reducing the amounts demanded by the soldiers (both government and rebels) to get past the roadblocks between them and their fields. When forced to flee because of armed attacks, it was those same committee members who started organizing people in the refugee camps.
Which points towards activities that may seem pretty unfashionable/individualistic – scholarships, leadership programmes, stipends for activists – as being compatible with chaotic contexts in a way that more institutional programmes are not.
Diaspora: Many chaotic settings have large, active and influential Diaspora communities that retain deep connections back home. I’m not quite sure whether they need (or even want) support from the aid sector, or what form that support could take, but as a permanent and stable feature in a chaotic situation, they are definitely worth consulting.
Faith Organizations: People’s personal faith and their involvement with faith organizations is if anything even more important in chaotic settings. The Catholic Church was once described to me as the DRC’s only truly national institution. Faith organizations incubate activists and future leaders (both good and bad). Yet many big donors and even some INGOs are resolutely secular, and see any involvement with faith groups as at best instrumental (one Oxfam staffer once told me ‘sure we work with faith organizations – they distribute our leaflets’).
If donors are serious about working in genuinely chaotic settings, they will need to adapt what they have learned in complex settings to more chaotic ones. One aspect will be getting better at spotting islands of political or social stability that emerge from the swamp and finding ways to support them. Chucking lots of money at them may well be counter-productive.
Any other suggestions? And here’s more from the Cynefin folk on managing chaos and complexity in times of crisis.