When the world is complex, how should we think and act?

“...it is not as simple or easy as we would prefer; neither is the world we are trying to manage...”

Warning: more thoughts on complexity. To some extent covering the same ground here (apologies), but more from a philosophy of science perspective, which has been at the same time interesting and thoroughly boring, unless you find the differences between genetically modified potatoes and genetically modified corn to be a significant one.

Complexity books: good covers. Credit: Amazon

Sandra Mitchell, in ‘Unsimple Truths,’ argues for a new approach to science which takes into account the world’s “tractable, understandable, evolved, and dynamic complexity”. Because if the world is complex, then our approach to representing and explaining it should be too. Her expanded, complexity-informed epistemology of science is therefore 1) pluralistic, accepting multiple explanations and models at a variety of levels of analysis, thus allowing for emergent properties when ‘the whole is more than the sum of the parts’; 2) pragmatic, recognising that whenever we represent nature it’s always for some purpose and so can be done in different, equally-valid ways and 3) dynamic, in that our knowledge of the causal structures of the world evolves as that world evolves. The latter of which is a novel thought to me, at least in that form. So the “greedy reductionist strategy” epitomised by the Newtonian physics and universal, exceptionless laws we love to hate, isn’t always wrong, but is when you try to explain everything using it.

Mitchell conveniently pulls out some implications for public policy: she, like Ramalingam and Beinhocker, takes aim at ‘predict-and-act’ models of policymaking which rely, essentially, on predicting the future, or at least assigning vaguely accurate probabilities to different outcomes. The problem is that “uncertainty about the probabilities of outcomes is pervasive, multiplicative, and often non-linear in complex systems” [such as climate change, mental health disorders, the macroeconomy etc.]. In such cases, we need to replace the ‘predict’ with models of “multiple alternative futures” and the ‘act’ part with adaptive management.

The first part of that involves mapping out multiple scenarios of the future, even if the likelihood of one or another is unknown, and comparing policies by how robust they are to the uncertainties in each scenario. This method captures the information we have about the future better than any single estimate. The second part essentially means picking an approach, then monitoring the results of that approach in the short term and modifying it based on the findings. “[A] dynamic, iterative, feedback-rich strategy for decision making that matches the dynamic, feedback-dependent reality of complex systems”.

If not, you end up with the climate change situation as is currently: the inability to come to an agreement on a single quantitative assessment of the probability of various outcomes (2 degrees warming? 10 degrees warming?) undermines any scientific contribution to public policy decisions. The inevitable uncertainty of the issue challenges the credibility of any scientific claim, leaving us to rely on ignorance and intuition: if you’re optimistic you can content yourself with the knowledge that technology will save the day; if you’re pessimistic you can content yourself with the fatalistic observation that we are already screwed.

So more flexibility in our approach to science, more scenarios, more computer modelling, more feedback, more tinkering, more informed actions, more successful policies. Only thus can we manage our “dynamically changing, complicated, complex, and chaotic but understandable universe”.

Emergent Thoughts on Emergence

Just finished ‘Aid on the Edge of Chaos’ by Ben Ramalingam, which applies ideas about complex adaptive systems to aid and development. Though ‘finished’ wouldn’t be quite the right word, as I reckon I’ll need about four more readings to get close to understanding the content. As the title suggests, this will be more a provisional set of conclusions.

Conventional development thinking: 'whoops'
credit www.drsalonen.com

I’ve already written about how Ramalingam critiques the current mode of thinking in development but it’s worth repeating the basic point: we need to stop applying simplistic analyses to complex problems. Ideas derived from Newtonian physics aren’t able to fully describe the totality of our social, environmental, ecological, political and economic reality. Who knew? Actually, the observation that the world is complex is so obvious that I can’t believe we’ve managed to fool ourselves into thinking that you can reduce REALITY to a few squares on a log-frame all this time. 

Upshot: as Abraham Lincoln says, "we must disenthrall ourselves" (one of many, many brilliant quotes in the book).

One part of this disenthralling is to stop looking for panaceas in development. Owen Barder recounts them nicely for us: more capital, more savings, more aid, more technology, better policies, better institutions, better politics. In fact, the desire for panaceas is itself part of the problem. And no, thinking in terms of complex adaptive systems is not the panacea either. This might seem obvious: accepting that there isn’t one solution to the problem of achieving development sounds easy, but actually our brains have a sneaky tendency to slip back into that way of thinking. This came home to me listening to a podcast in preparation for this post (it’s sad but I actually do prepare); Humanosphere editor Tom Paulson, when interviewing Owen Barder, asked him whether complexity thinking means that we need to look at tax havens to achieve development. Barder: that’s exactly the type of panacea thinking we are trying to get away from. Yeah.

So what are complex adaptive systems/systems of organised complexity? Let me throw some words at you that together constitute a vague approximation to an answer. Complex adaptive systems have large numbers of mutually interacting parts, are open to the environment, are self-organising in their internal structure, have ‘emergent’ macroscopic properties over and above the properties of their constitutive parts (think human consciousness as more than putting together feet and white blood cells and nerves and lungs). Complex systems are interconnected and interdependent and dynamic, constantly evolving and adapting. From all that I’m sure it’s apparent that I don’t totally understand either. Looking at the box might be helpful at this point. In fact Ramalingam takes pains to emphasise that complexity thinking is itself a work in progress even for people who research it, so don’t feel bad.

Comparison of conventional aid thinking and complexity-influenced ideas about development

Okay so that’s all well and interesting that clever people have found out why everyone’s been wrong for the last seventy years but what does it mean for development? Ramalingam provides some broad-brush messages on this: complexity thinking can’t tell you what to do on Monday morning, but it can help us understand the world around us better, promote more open debate about the challenges facing us, and help us think up new approaches to problems. It can help us “see through new eyes”. In this sense, development is more of an emergent property of a society/economy rather than a outcome of a process to be engineered. Aid in an ideal world then becomes “an open innovation network,” an “internal catalyst...[to] identify, expand and sustain the space for change,” a “fluid, dynamic, emergent” process. We stop looking for ‘the answer’ because, as the NYU Development Research Institute strapline goes, “there are no answers for global poverty. There are only answer-finding systems.” If nothing else, you can’t say complexity thinking isn’t good at snappy one-liners.

The thrill from this enormous potential is kind of feeding into my disillusionment with NGO work at the moment; the organisations I have experienced thus far are not embedded in the relevant 'system', not looking to foster small adaptive changes whilst accepting the underlying complexity of the problem they are trying to address. The organisations I have worked for have their ‘product’ with its own intellectual framework which is superimposed onto a given situation. They have their own thinking, activities and feedback loops which are only tangentially aligned with the problem at hand with all its complexity, non-linear characteristics and emergent properties. And most dangerous of all, they are self-sustaining, insulated from their own inadequacies by the current aid system.

Ramalingam shows us complex systems thinking in action: it’s all over the place apparently, from emergent leadership in Obama’s election campaign, to holistic range management in Zimbabwe, to conflict resolution in Aceh after the Boxing Day tsunami, to the development of M-Pesa mobile money in Kenya. The book is filled with examples.

After the above whirlwind, I’m still left with a few questions though:

• How do you identify a system? What is the scale of a system?
• How do you know when something is not a problem of organized complexity?
• What would development institutions/donors/NGOs look like if you took them apart and rebuilt them to be equipped to face problems of complexity?
• What are the steps to changing aid in the right direction? Is it just that we read Ramalingam's book, realise we’ve been doing it wrong and change our approach? How do you change the system? (Ramalingam recognises the difficulties of this with the remark that “thousands of careers depend on sustaining certain ways of looking at the world”. That'd be politics again.)

Time to read it again I guess.

Involuntary guest post by Ben Ramalingam

I'm currently reading Aid on the Edge of Chaos by Ben Ramalingam, which attempts to apply complexity theory, i.e. the next big thing about five years ago (hate it when I get there late), to aid policy and practice. You should read it. Just finished Part 1, which dances lightly through the thinking and implicit theories behind the aid industry over the last sixty years and rips them apart, with wit, cartoons, punny chapter titles (Chapter 4 - 'The Goats in the Machine') and occasional classical references. The attack is directed at the linear, simplistic view of reality (cause and effect, rational actors, closed systems) derived from Newtonian physics, which underpins much of aid thinking and is totally inadequate for addressing the complex problems of development. There were some great passages which articulate, much more lucidly, elements of the frustration which underpinned my last post. Sharing is caring.

The problem with the aid industry:

"Despite the grander claims of some recent movements, development and humanitarian work is not a knowledge industry - except in the most idealistic interpretation. It is an export industry, and an exceptionally blunt, supply-oriented one at that. It gathers up poverty, vulnerability, and suffering from the South, packages them for sale in the West, and exports off-the-peg solutions back in relentless waves of best-practicitis"

And the traditional response:

"In the face of widespread institutional inertia, the resilient cookie cutters and travelling orthodoxies of foreign aid, the strategy most commonly found is to simply give up on trying to find a good solution. Just follow orders, do your job, and try not to get in trouble. Let the system do what it will."

The message:

"[A]id agencies are increasingly dealing with a world for which their learning, strategic, performance, and organizational frameworks were not designed"

If you fancy learning what more appropriate frameworks would be, take your pick from the following links on complexity in development, or, better, read the book. If I had to go for one (other than the book) I'd recommend the Owen Barder podcast (not that I've read all of these myself).

Papers/books (for the brave):

A (long) one by Ramalingam and others

A book on systems thinking by Donella Meadows

Podcasts (for the bus):

From Humanosphere with Owen Barder (short)

From Owen Barder himself (longer and better)

Blog posts (for the time-pressed):

From Ramalingam on his blog

Various from Duncan Green on his blog

From the Center for Global Development