The lure of the complicated: systems thinking, data and the need to stay complex

Sometimes messy, frustrating conversations are the most productive – as you wrestle with confusion, small lightbulbs flash on in your head – either insights or the onset of a migraine.

Earlier this week I spent an afternoon at the Gates Foundation in London, discussing what systems diagnostics can offer to groups like the World Bank, DFID and RISE, a big research programme on improving the quality of education worldwide.

We kicked off with an education systems guru, Luis Crouch, describing the 3 things he thinks characterize a system:

  1. The objects/actors in it – wolves, deer and tree saplings
  2. the feedback loops between them – wolves eat deer; deer eat saplings.
  3. The emergent properties that the system produces. Not intentional on part of any one actor.

In his view, most diagnostics of education performance spend a lot of time enumerating and assessing the individual actors (teachers, principals, training institutions etc), but don’t think much about the feedback loops between them. Also, they mainly look at purposive properties – did the programme achieve the intended outcome – not the emergent properties that no-one intended.

We then moved on to Jaime Saavedra, who now leads the Education Global Practice at the World Bank, but from 2013-16 was Peru’s Education Minister. His reflections on his reform efforts there were fascinating.

Firstly (in passing) a ringing endorsement for league tables. He talked of the ‘PISA shock’ from Peru’s poor performance in the global PISA education ranking. ‘‘PISA influences countries: I don’t want to look bad’.

But what really gets him fired up is data. ‘We had a huge obsession with data, throwing information into the system. There were dashboards for absolutely everything.  We tracked the life of the textbook from printshop to student. Ditto with all the inputs.’

On the basis of data analysis and brainstorming, the Ministry identified poor quality teaching, pedagogy and (lack of) school leadership as the main things they needed to tackle.

Lant Pritchett chipped in (very excited that he’s going to be in the UK for a while, after leaving Harvard) and said education reformers need to come up with something like the Growth Diagnostics that he developed with Dani Rodrik, Ricardo Hausman and Andrés Velasco. Education reform is now where growth promotion was, circa 2005: ‘the problem was that every time the World Bank did a country report, it would come up with a checklist of 60 things to fix, and no country could deal with all of them. We need a structured way to identify the binding constraints to learning, and tackle those first’.

Ping! On goes a lightbulb. These two are talking about a complicated system, not a complex one. The distinction is crucial. Complicated is like sending a rocket to the moon – a difficult problem, but one that can be broken down into its component parts, ‘solved’ with data and smarts, and reassembled into a successful solution. That’s what Jaime and Lant were describing.

In contrast, a complex problem is more like raising a child – it’s all about antennae, judgement, guesswork, collaboration, trying stuff out and then realizing quickly when it’s not working. Data is useful, but not as central. ‘Lessons’ from raising one child are likely not to transferable to the next. And if you break a complex system down into its component parts (please don’t try this with your child) you won’t get much insight into how all the different feedback loops produce the ‘emergent properties’ of the whole.

The act of being in charge, of trying to get stuff done, drags you from the complex into the complicated quadrant. The data offers you a handle, you need to set priorities, and before you know it, you are doing the Growth Diagnostics thing and breaking up the system into its parts. That’s probably inevitable, and not such a bad way to proceed compared to, for example, just making stuff up. But it loses touch with those elements that are complex, and likely to mess up your complicated plan.

So what countervailing forces can push decision makers to keep at least one foot in the complex camp? The two obvious ones are politics and MEL: politics – what Jaime knows will fly and won’t – is all about judgement and spotting emergent patterns and opportunities; Monitoring, Evaluation and Learning, done in real time, will tell you when your attempt to disaggregate has backfired, and unintended consequences are springing up like mushrooms in the night.

Any issue is likely to have elements of both complicated and complex, so the question is how to ensure a balance of both.

Cue second light bulb. One of the things I have been failing to do is distinguish clearly between system, problem and solution. On the same topic, they may be in different quadrants – eg health, whether individual or societal, is definitely a complex system, but how to establish healthcare clinics in every corner of the county can be a complicated problem, while vaccinations may be a simple solution.



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6 Responses to “The lure of the complicated: systems thinking, data and the need to stay complex”
  1. Duncan, There is something in the zeitgeist perhaps. Have you seen this piece by Ed Mayo of Coops UK? I like your 2×2 here. We are early in a slow shift from the organization as the unit of thinking and action to the system. Our management tools are still rooted in the organization as unit paradigm. We have developed Constituent Voice as a way to move back and forth between the organization and the system through feedback loops with constituents. As I read your piece I realized that CV also allows you to manage back and forth across the complex and complicated domains. We recently integrated system maps into our toolkit to help everyone in a system to see the full system, and therefore be better able to hold complexity.

  2. Masood Ul Mulk

    In our work in fragile, insecure and uncertain environments we are simultaneously in complex, complicated and simple environment and build our response accordingly. We are constantly moving from best fit to best practise and between the two. As we move into complex environment the foremost role of management is to manage its complexity. This could lie in bureaucratic red tape, security agencies rules, administrators and politicians egos and communities abilities and disabilities; and meeting the results agendas and logframes of the donors.Once we are there we enter the area of complicated and simple quadrant. We have to help the communities build a school, set up a power house, deliver credit etc. There are best practises to be used in addressing these issues. But a neighbours cow walking into a field, a polutician facing election, a bureaucrat who you were insensitive could always send your entire project into a whirlpool and need for controlling an environment that has become unstable. This is only possible where mamagers understand culture and context and you simultaneously deal with atleasr the three quadrants. Emergence beomes your strategy.

  3. Charlotte Ornemark

    Dave Snowden who came up with this brilliant and simple 2×2 in 1999 also uses the analogy of a children’s birthday party. You can plan the event in minute detail, and baking the cake is in itself complicated (rather than complex), but once you let a crowd of little hooligans into your house you have decidedly moved from the ‘complicated’ to the ‘complex’ quadrant. The cake, meticulously created for human consumption, ends up in your curtains and in a smaller sibling’s hair while some unpredicted joke or game get the kids on their feet, collaborating, and roaring with laughter. The uncertainty of complexity does not fit nicely into projectized chunks of manageability which relies on perfectly honed inputs, and therein lies our dilemma as development professionals. Performance (the cake efficiently and expertly made), not to be confused with the overall outcome (kids having fun/ or not). We tend to measure the quality of the cake and whether the kids would like to have a similar cake for their next birthday party. They might well say yes to that. But nobody asks them what made them roar with laughter; the trigger which made the party take off. (Just to say: who defines the problem matters, and when are so-called solutions really ‘real’).

  4. David Grocott

    Interesting read. Some questions and comments:

    – Do complex/complicated challenges exist on a spectrum, or can we draw clear, definable distinctions between them? (Likewise, can we draw clear distinctions between simple/complicated?)
    – Complex challenges are likely to have a series of simple/complicated challenges embedded within them. For example, if a complex challenge is is increasing the number of children sleeping under bed nets, a simple challenge that needs solving is how to distribute bed nets in the most efficient way. But complex challenges are more than just amalgams of simple/complicated sub-challenges. Therefore, solving all the ‘sub-challenges’ will not necessarily solve the overarching complex challenge (probably because you will have fixed the component parts of the system but not managed to get them relating to each other in the right way, and therefore will have been unable to generate the desired emergent property).
    – As Charlotte Ornemark’s post above suggests, simple/complicated challenges tend to manifest at lower levels of the theory of change (i.e. at the input to activity, and activity to output level), while complex challenges tend to manifest at higher levels (i.e. outputs to outcomes, and outcomes to impact). Planning style approaches (e.g. the logframe) tend to be good at dealing with simple/complicated challenges, but if used poorly can create disincentives for the kind of approaches (innovative, reflective, collaborative, adaptive) needed to solve the complex challenges at the top of the theory of change.

    Some interesting thoughts here. I think in general there’s a lot of confusion between what counts as a complicated challenge and what counts as a complex one. Do they exist on a spectrum? Or can we draw clear distinctions between them? (Likewise, how do we draw a clear distinction between simple and complicated?)

    A complicated challenge is likely to have a number of simple challenges embedded within it. Likewise, a complex challenge is likely to have a number of complicated and simple challenges embedded within it. For example, while distributing bed nets in the most efficient way might be a simple challenge, actually getting people to use them appropriately might be a complex one. BUT complex challenges are more than just a series of simple/complicated challenges lumped together, and solving the simple/complicated challenges that make up a complex challenge will not solve the complex challenge itself. That’s probably because the key to solving a complex challenge lies in the relationships between its constituent simple/complicated challenges, and not with the simple/complicated challenges themselves . It is how the various solutions work together – the relationships – that count, and that is too difficult to model.
    but solving the