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Archive for the ‘Self organisation’ Category

The team behind the Atlas of Economic Complexity (see my post on this here) have come up with a fascinating network-based approach for analysing the global aid system.

As they put it:

International development is a complex global goal that faces massive coordination barriers. The difference in income between rich and poor has expanded over the years from a four to one factor to a hundred to one. Where there once were only a handful of development agencies, thousands have now emerged. The system that connects donor agencies, recipient countries and development challenges is extremely complex and should not be managed with a top-down approach… The Aid Explorer was developed as a tool to facilitate better aid coordination. The Aid Explorer enables users to understand what issues face which countries and which aid organizations are aligned to address these issues.

Some specific pointers:

  • The Aid Explorer’s Profile pages enables us to see which issues face which countries and which organisations are best aligned to address them
  • The Network maps can be used to explore how issues, countries, and organizations relate to each other
  • The Rankings presents the findings and the best alignments of countries, issues and organizations

The process of developing the dataset, and how to use it, is described in more detail in the accompanying paper “The Structure and Dynamics of International Development Assistance“, published earlier this year in the Journal of Globalization and Development.

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Many would argue that standard economic theory enabled us to analyse and understand the economy as it used to be,
with long stable periods punctuated only by occasional crises. However, the recent evolution of the global economy should drive us to pursue ways of expanding economic theory
such that it encompasses the new structures and organization
emerging as we globalize and network our world.
This is from a great new paper by Dirk Helbing and Alan Kirman, which asks and attempts to answer many questions that are of importance to development, and answer them by drawing on ideas from complexity theory. Well worth a read.

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This is the text of an article in the Washington Post by Dominic Basulto about last week’s events in the financial markets. Great stuff.

When news first broke Thursday that JPMorgan’s credit derivatives portfolio had sustained a loss of $2 billion, and potentially as much as $5 billion, on trades gone awry, there was an immediate call for greater regulatory oversight over banks’ high-risk trading activities. The message was clear: “If you’re going to be a bank, then you can’t play at the casino,” as the Post’s Ezra Klein writes. At the same time that the market was lopping off billions of dollars in shareholder value, JPMorgan was purging top executives responsible for the bungled trades and facing awkward questions about its public stance in favor self-regulation. If banks can’t regulate themselves, though, who can?

Inevitably, the answer to that question depends on whether you view the financial markets as complicated or complex. If the financial markets are merely complicated, traditional approaches to regulation can be effective: regulators can turn their attention to individual actors within the market and systematically make the requisite changes to restore the market to equilibrium. In a complex system, however, traditional approaches to regulation can be woefully inadequate — small changes may end up having outsized effects, while big changes may end up having little or no effect. In a complex system, you need to focus on the interactions between each of the participants as much as the condition of individual actors.

The trading screens of Wall Street are, if nothing else, the perfect example of how computers are able to mask the complexity of an underlying system by being able to reduce the real world into 1’s and 0’s. There is no shortage of algorithms, formulas, sophisticated risk management models and quantitative trading models promising to reduce complex financial market interactions to something that can be studied, adjusted and tweaked. In theory, regulators should be able to look at a few numbers, compare them to a few benchmarks, and suggest the necessary adjustments.

But it is rarely that easy.

There is a big difference between complicated and complex. In a classic example, an automobile is complicated, but a transportation system with human drivers is complex. Ultimately, you can fix an automobile by lifting up the hood and checking that everything is working properly, no matter how sophisticated the parts. You can only fix a transportation system, though, by understanding how each of the drivers interacts with each other and understanding the distributions of dynamic traffic patterns.

One of the most innovative areas of public policy, in fact, involves the intersection of complexity theory with regulatory policy. Complexity theory, which has been used to model complex systems ranging from ant colonies to climate change, has also been applied to financial regulation. Practitioners within the financial markets are well-versed with complexity theory and its cousin: chaos theory. One of the entities consistently singled out in the academic literature is the CDC (Center for Disease Control), which is held up as a role model for how to deal with complex systems. For example, using complexity theory, the CDC was able to suggest that — contrary to what you might see in movies like “Contagion”restricting air travel would have little or no impact on stopping the SARS outbreak. As the CDC recognizes, you simply can’t regulate away diseases. You need to deal with them with in real-time as they appear and find the right levers to stop them. Most importantly, you need to be able to spot potential flare-ups before they occur and understand the emergent behaviors that lead to outbreaks.

Certainly, the types of events that we observe in the financial markets, such as “flash crashes” and billion-dollar Black Swan events in derivative markets, are reminiscent of complex systems behaviors where small changes lead to unimaginable consequences. While the Volcker Rule, which would keep banks from engaging in risky trading behavior, could be effective in the short-term in avoiding certain types of adverse market effects, it may not be as effective in dealing with the full range of market fluctuations in the long-term. Implicitly, we recognize the complexity of financial markets by ceding power to computers and algorithms to price financial instruments properly. Now, we need to recognize that this complexity also has important consequences for the way that we think about regulating these markets.

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A continuing theme on this blog has been the issue of leadership. Many reports and studies call for it, reforms are seen as impossible without it, critical challenges will not be met without it, and we are all ready to point out the lack of it (in others, at least).

Despite the fact that leadership is one of the most researched topics in management literature (or perhaps because of this fact) , our understanding of leadership remains vague and ambiguous.

A blog on HBR by Duncan Watts of Yahoo’s Human Social Dynamics research group eloquently explores the concept and ideas with reference to recent social movements.

His argument resonates strongly with ideas of complex adaptive leadership explored in a previous post here.

The Occupy Wall Street movement has both perplexed and frustrated observers and analysts by its persistent refusal to nominate an identifiable leadership who can in turn articulate a coherent agenda. What is the point, these critics wonder, of a movement that can’t figure out where it’s trying to go, and how can it get there without anyone to lead it?

It’s a reasonable question, but it says at least as much about what we want from our social movements as it does about the way movements actually succeed.

Typically, the way we think of social change is some variant of the “great man” theory of history: that remarkable events are driven by correspondingly remarkable individuals whose vision and leadership inspire and coordinate the actions of the many. Sometimes these individuals occupy traditional roles of leadership, like presidents, CEOs, or generals, while at other times they emerge from the rank and file; but regardless of where they come from, their presence is necessary for real social change to begin. As Margaret Meade is supposed to have said: “Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”

It’s an inspiring idea, but over 100 years ago in his early classic of social psychology, “The Crowd,” the French social critic Gustave LeBon, argued that the role of the leader was more subtle and indirect. According to LeBon, it was the crowd, not the princes and generals, that had become the driving force of social change. Leaders still mattered, but it wasn’t because they themselves put their shoulders to the wheel of history; rather it was because they were quick to recognize the forces at work and adept at placing themselves in the forefront.

Even before LeBon, no less an observer of history than Tolstoy presented an even more jaundiced view of the great man theory. In a celebrated essay on Tolstoy’s War and Peace, the philosopher Isaiah Berlin summed up Tolstoy’s central insight this way: “the higher the soldiers or statesmen are in the pyramid of authority, the farther they must be from its base, which consists of those ordinary men and women whose lives are the actual stuff of history; and, consequently, the smaller the effect of the words and acts of such remote personages, despite all their theoretical authority, upon that history.” According to Tolstoy, in other words, the accounts of historians are borderline fabrications, glossing over the vast majority of what actually happens in favor of a convenient storyline focused on the skill and leadership of the great generals.

Thinkers like Le Bon and Tolstoy and Berlin therefore lead us to a radically alternative hypothesis of social change: that successful movements succeed for reasons other than the presence of a great leader, who is as much a consequence of the movement’s success as its cause. Explanations of historically important events that focus on the actions of a special few therefore misunderstand their true causes, which are invariably complex and often depend on the actions of a great many individuals whose names are lost to history.

Interestingly, in the natural world we don’t find this sort of explanation controversial. When we hear that a raging forest fire has consumed millions of acres of California forest, we don’t assume that there was anything special about the initial spark. Quite to the contrary, we understand that in context of the large-scale environmental conditions — prolonged drought, a buildup of flammable undergrowth, strong winds, rugged terrain, and on so — that truly drive fires, the nature of the spark itself is close to irrelevant.

Yet when it comes to the social equivalent of the forest fire, we do in effect insist that there must have been something special about the spark that started it. Because our experience tells us that leadership matters in small groups such as Army platoons or start-up companies, we assume that it matters in the same way for the very largest groups as well. Thus when we witness some successful movement or organization, it seems obvious to us that whoever the leader is, his or her particular combination of personality, vision, and leadership style must have supplied the critical X factor, where the larger and more successful the movement, the more important the leader will appear.

By refusing to name a leader, Occupy Wall Street presents a challenge to this view. With no one figure to credit or blame, with no face to put on a sprawling inchoate movement, and with no hierarchy of power, we simply don’t know how to process what “it” is, and therefore how to think about it. And because this absence of a familiar personality-centric narrative makes us uncomfortable, we are tempted to reject the whole thing as somehow not real. Or instead, we insist that in order to be taken seriously, the movement must first change to reflect what we expect from serious organizations — namely a charismatic leader to whom we can attribute everything.

In the case of Occupy Wall Street, we will probably get our wish, for two reasons. First, if OWS grows large enough to deliver any lasting social change, some hierarchy will become necessary in order to coordinate its increasingly diverse activities; and a hierarchy by nature requires a leader. And second, precisely because the outside world wants a leader — to negotiate with, to hold responsible, and ultimately to lionize — the temptation to be that person will eventually prove irresistible.

Leaders, in other words, are necessary, but not because they are the source of social change. Rather their real function is to occupy the role that allows the rest of us to make sense of what is happening — just as Tolstoy suspected. For better and worse, telling stories is how we make sense of the world, and it’s hard to tell a story without focal actors around which to center the action.

But as we witness a succession of popular movements, from the Arab Spring to Occupy Wall Street, we can at least pause to appreciate the real story, which is the remarkable phenomenon of a great many ordinary individuals coming together to change the world.

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Humanitarian coordination has been described in a new ODI paper as a ‘wicked problem’ which demands new and radical solutions. This post explores the  longstanding incentive issues underlying the lack of effective coordination and suggests possible ways forward.

In a paper published last yearMichael Barnett and I argued that the humanitarian system was stuck in much the same kind of bizarre loop as Bill Murray’s character in the film Groundhog Day: that it was ’condemned to repeat’. Each major disaster highlights pretty much the same problems and flaws, from Rwanda to the Tsunami to Haiti. This is despite a lot of work to try and improve the quality and accountability of aid between these events. Although some remarkable innovations have improved specific aspects of humanitarian aid, system-wide performance remains a major issue.

Drawing on the game theoretical work of Professor Elinor Ostrom and other leading political scientists, we argued that at the heart of this repetition was the issue of incentives.

The humanitarian system has few incentives for collective action, which are seen by many as key to effective coordination. Few of the humanitarian reform initiatives which have been launched over the past 15 years have attempted to understand or address the incentives that underlie and reinforce existing behaviours. This is because few if any agencies have been willing to sacrifice delivering quickly against their individual mandates for the benefit of the wider response.

This  ‘me first’ mentality is apparent throughout the interactions that make up the system - not just between international actors, but also in interactions with national and local actors, with ‘non-traditional’ actors such as the private sector and the military. It is also painfully evident in interactions with disaster-affected populations themselves.

Michael and I argued that without serious effort to examine and strengthen the incentives to cooperate versus those to ‘go it alone’, the coordination of humanitarian aid is likely to continue see a Groundhog Day-style repetition of below-par performance characterised by this ‘me-first’ mentality.

Haiti is the tragic exemplar for our times. Despite the complexity and scale of the post-earthquake response, the mechanisms set up to coordinate international efforts – sector-specific clusters in health, shelter, and so on – had no formal decision-making mechanisms or mandates. Remarkably, given their nominally central role in ensuring the coherence, effectiveness and efficiency of the response, the success of the cluster operations by and large came down to the personality or leadership skills of a single individual.

An ODI roundtable last month on the workings of the humanitarian system concluded that:

The recent humanitarian experience in Haiti is a tale of exclusion. The humanitarian system, with all its resources and coordination and information-sharing mechanisms, succeeded in by-passing many of the spontaneous, ad hoc or informal indigenous humanitarian responses. [agencies] actively excluded others, often without good reason.

None of this is new, of course. A UNOCHA-commissioned study published ten years ago revealed

…a ‘system’ that shows determined resistance to cede authority to anyone or any structure… Despite the urgency of the task, and the potential impact on human lives of poorly coordinated humanitarian responses, [key leaders] at the field level are all denied the ability to direct or manage humanitarian responses. Instead, all have to work on the basis of coordination by consensus. In the face of the obstacles, this is an uphill struggle…”

The Rwanda evaluation, published five years earlier in 1996, noted that the international system had ‘a hollow core’. It would seem that the latest solutions to this problem are also somewhat hollow.

And this is increasingly being recognised by disaster-affected states. Those that can afford to are turning down or strictly limiting international assistance. When Australia’s foreign minister Kevin Rudd was interviewed after the recent floods affecting his country, he argued that that one of the worst things they could have done was to have “a whole lot of uncoordinated delivery of stuff from around the globe plonked on [our] doorstep”. There are numerous ongoing debates in the context of the Japanese earthquake response which are also pertinent to this issue.

How can we deal with the Groundhog Day problem in system-wide humanitarian performance? There are numerous ideas bubbling around at the moment, from strict regulation to nationalisation to partial privatisation. But unless these new efforts tackle the incentives that arise at the point of disaster, they are unlikely to lead to significant and lasting change.

An important focus for anyone reflecting on how to improve system-wide performance is to explore ways to change the pay-offs, so that the longer-term incentives for mutual cooperation in the interests of disaster-affected people outweigh the short-term incentives for going it alone.

Without this issue being put front and centre so as to address the ‘hollow core’, any new reform to improve system-wide performance will sit on top of these issues instead of resolving them. Agencies will continue to deliver against their narrow, short-term organisationally-defined objectives, to the detriment of their own longer-term benefits, overall system-wide performance, and most importantly of all, the communities they purportedly seek to help.

Of course, such self-examination is not an easy thing to do, nor is it easy act upon. As the write-up of the ODI event notes, humanitarian coordination is a good example of a “wicked problem“: “difficult and fluid problems with no clear solutions, to which any response typically creates additional problems… the more closely they are examined or the better they are understood, the more complex and insoluble they may seem.” It continues:

Collective strategic action in the face of these problems may be continually frustrated; more effective responses may depend on developing and strengthening decentralised, diverse, non-linear and non-hierarchical approaches to problem-solving across the humanitarian sector.”

One radical suggestion for changing the pay-offs by along these line scomes from Yaneer Bar-Yam, president of the New England Complex Systems Institute. He suggests that the shortcomings of the humanitarian-response system in Haiti have a lot to do with a principle of systems thinking known as requisite variety. This states that an effective system has to have as many different states of response as conditions that are presented by its environment – or more simply, that internal diversity has to match external diversity.

This implies that we need to be seeing a lot more creativity and innovation in coordination efforts, in ways that are appropriate and tailored to different emergency contexts. The ‘one-size-fits-all’ model of the clusters, together with their lack of coordination ‘teeth’, may well be hindering more than helping performance.

There are also serious questions to address about who gets to judge the performance of agencies. According to a Slade interview with Bar-Yam, one example of such an improved model would be:

…one that understands that duplication and competition among NGOs is not a bad thing, so long as organizations are rewarded with donor money for delivering effective solutions.  And those solutions can only be determined by Haitians themselves…”  (emphasis added)

This idea seems to have some resonance with “cash on delivery” – an innovation from the development side of the system. Of course any attempt to apply this principle to humanitarian aid needs a lot more thought – and it is just one suggestion – but it is certainly an intriguing one. There are no doubt more such ideas to consider. What does seem clear is that this kind of incentives-focused thinking needs to play a central role in the current round of efforts on humanitarian performance and reform, if we are serious about change.

In Groundhog Day, Bill Murray’s character was only able to end his continuous loop of the same day when he started putting his own interests to one side. He ended the cycle when he stopped being arrogant and duplicitous, stopped trying to ‘win’, and started acting in the interests of the greater good in a simple and honest way.

Groundhog Day is a fable, of course, and to many it may appear a rather sentimental one. But it also contains a lesson that international humanitarian actors would do well to heed.

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Last year I wrote a paper called Paradigms, Poverty and Adaptive Pluralism. In it I explored how technological advances and complexity sciences were together helping to reframe a longstanding divide between two opposing paradigms in international development.

Because of the relevance of this to current debates on complexity and aid, I welcome this opportunity to share these ideas here. I warmly invite feedback from the across the aid blogosphere, which (as I explain in my next post) I see as a new and vitally important feature of the international development ‘system’.

Exploring Paradigms

‘Paradigms’ is one of the most over and mis-used terms in the social or physical sciences. This is arguably because it is so potentially useful. For Thomas Kuhn, who did so much to bring the term to popular attention, a paradigm was a strong network of commitments – conceptual, theoretical, instrumental and methodological. Of course, he was writing about the physical sciences. In comparison, I define a paradigm as a coherent and mutually supporting pattern of:

- concepts and ontological assumptions;
- values and principles;
- methods, procedures and processes;
- roles and behaviours;
- relationships; and
- mindsets, orientations and predispositions.

I find the above useful because it explicitly includes social, values-based and personal dimensions of paradigms – all things which matter a great deal to social scientists.

In my 1997 book Whose Reality Counts I presented two alternative paradigms - as I then understood them – which contrasted things with people, as shown in the table below.

The ‘things–people’ distinction is useful for identifying and understanding relationships between many phenomena and for diagnosing problems. It points up the contrasts between disciplinary and professional orientations: the things paradigm is more associated with engineering and economics, the people paradigm more with anthropology and sociology. And the contrasts in the two  columns indicate differences which are evident in much practice. At the same time, there are many cross-overs and cross-applications.

One key difference is that the things paradigm works in contexts (including human contexts) in which inputs and receiving environments are relatively uniform and controlled, and there is clear causality leading to desired outcomes.

Because of this narrow applicability, many of the errors and failures of development policy and practice have stemmed from the dominance of the things paradigm. This dominance goes back at least to the Marshall Plan, to the creation of the International  Bank for Reconstruction and Development, to development projects in the 1950s  and 1960s devoted to infrastructure such as harbours, railways, roads,  communications, dams and irrigation projects, and to the idea that Third World  countries had to catch up with capital investment in ‘infant industries’. These  all gave primacy to things over people.

Engineers and economists were in charge. It was they who set norms and procedures. For the infrastructure projects of the time, these largely made sense. But the things paradigm was then embedded in the values, culture, hierarchies and staffing of the World Bank and of bilateral and other organisations.

Non-economist social scientists were few, of low status, and regarded at best as useful to call in to deal with any ‘people problem’ in implementation once the planning had been done. So top-down, standardised approaches and methods came to be imposed on diverse, uncontrollable and unpredictable people and conditions, often with bad results.

There followed a long and continuing struggle for a better balance that put people first, with their participation from the start and throughout in projects and programmes. There were calls for a new professionalism to shift the balance, effectively from things more towards people. There was progress. For many reasons the balance did indeed shift.

Some attempts to introduce top down routinized procedures were abandoned. Participation and empowerment became part of the rhetoric even if less often of the reality of development. Local people were much less regarded as a residual. People living in poverty, women, children, those who were vulnerable, marginalised and socially subordinate, were given more priority. Though there remained far to go, their knowledge, aspirations, capabilities and priorities were better recognised and brought more into development processes. Especially in the 1990s, the centre of gravity of the balance between things and people began to shift towards people.

But the 2000s brought reversals. ‘Things’-related procedures were increasingly imposed on processes and people. In much development practice, problems were aggravated by the way linear logic, assumptions of predictability, objectively verifiable indicators, impact assessments, logframes and results-based management were more and more required by donors and lenders. More and more the assumption took hold that ‘we know what to do’ and all development required was more money. Good practice and performance, so often dependent on intangible personal and inter-personal unmeasurables like commitment, honesty, energy and trust, were undermined and sapped by the spreading culture in much development of targets, indicators and measurement, and the implicit and even explicit orientation of ‘If it can’t be measured, it won’t happen’.

‘Rigorous’ impact assessment was increasingly demanded. The so-called gold standard for this became randomised control trials (RCTs). These can make sense for medical research where there are many highly standardised units (people and their bodies) and inputs (immunisations, medicines, treatments) but misfit the realities of the complexity of social and much other change, with their uncontrolled conditions, multiple treatments, multiple and indeterminate causation, and unpredictable emergence .

In such contexts, RCTs are liable to postpone and limit learning, and to be costly, slow and inconclusive. Another contested manifestation of this control orientation has been the logframe. Thought by many in the late 1990s to fit realities and programme and project needs so badly and to have so many defects that it would die a natural death, the logframe has to the contrary flourished and spread to become a methodological monoculture in donor requirements.

So in the name of rigour and accountability what fits and works better in the controllable, predictable, standardised and measurable conditions of the things and procedures paradigm has been increasingly applied to the uncontrollable, unpredictable, diverse and less measurable paradigm of people and processes.

The misfit is little perceived by those furthest from field realities and with most power. But then all power deceives. Aid recipients do not tell donors what they experience. They think about future funding. Because funds and power are involved, these tightening and constraining shifts pass largely unremarked and unchallenged.

And what can be called ‘things procedures’ like the logframe are convenient for understaffed donors: they transfer transaction costs and any blame to those whom they fund. Recipients of aid funds are like frogs in the proverbial slowly heating pot and they adapt; but more than the frogs, they increasingly feel the pain. They do less and do it less well. They would like to jump out but fear for their survival if they did.

In my next post, subtitled Expanding Paradigms, I examine the limitations of this simple binary opposition of things and people. Shifts in technology and advances in the complexity sciences are starting to transform these paradigms, helping bring nuance to and even transcend these longstanding divides.

In the meantime, I do welcome readers to share your thoughts and ideas on the above.

Robert Chambers 10/02/2011 Institute of Development Studies, Sussex

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Positive deviance (PD) is a fascinating approach, a decade and a half old, and the focus of growing interest in health, education and numerous other sectors in domestic public policy. Interestingly, given PD saw first widespread application in an aid programme, it is still less well known than it should be across the international community.

This post compiles information from a variety of sources into Q&A form to explore the ideas and assumptions of positive deviance. Rather randomly, it is posted on the same day as another PD-related post, by Duncan Green.

What is Positive Deviance?

According to Richard Pascale, co-author of The Power of Positive Deviance:

It’s an oxymoronic term, in a way, but it’s a very simple observation that in many situations—both in companies and in societies—there are problems that seem absolutely intractable,  where you tried everything and you can’t get anything to work, and you just end up accepting things by saying, “that’s just the way it is.” Yet in almost every one of those situations there usually are a few people with the same resources as everyone else, who, against all odds, are succeeding when everyone else is not. So the simple idea is to look at those people who are deviant in a positive direction, and who are prevailing when the conventional wisdom says you can’t.

Where did Positive Deviance come from?

From the PD website:

In 1991, the Jerry and Monica Sternin faced what seemed like an insurmountable challenge in Vietnam. As new Director of Save the Children in Vietnam, Jerry was asked by government officials to create an effective, large-scale program to combat child malnutrition and to show results within six months. More than 65 percent of all children living in Vietnamese villages were malnourished at the time. The Vietnamese government realized that the results achieved by traditional supplemental feeding programs were rarely maintained after the programs ended. The Sternins were mandated by the government to come up with an approach that would enable the community to improve and sustain their young children’s health status…and quickly!

Building on Marian Zeitlin’s ideas of positive deviance, working with four communities and a population of 2,000 children under the age of three, the Sternins invited the community to identify poor families who had managed to avoid malnutrition despite all odds, facing the same challenges and obstacles as their neighbors and without access to any special resources. These families were the positive deviants. They were “positive” because they were doing things right, and “deviants” because they engaged in behaviors that most others did not. The Sternins and the community discovered together that caregivers in the PD families collected tiny shrimps and crabs from paddy fields, and added those, along with sweet potato greens, to their children’s meals. These foods were accessible to everyone, but most community members believed they were inappropriate for young children. The PD families were also feeding their children three to four times a day, rather than twice a day, which was customary.

The communities developed an activity which enabled all of the families with malnourished children to rehabilitate their children and to learn how to sustain their children at home on their own, by inviting them to practice the demonstrably successful but uncommon behaviors which they had discovered in their communities. The pilot project resulted in the sustained rehabilitation of several hundred malnourished children and the promotion of social change in their communities. (PD website)

So it’s basically a way of identifying best practices? Great, just what we need!

Steady on there, not so fast.

Of course the initial reaction of any hierarchical organization is, “this is a best practice, let’s do it everywhere.” The strong counsel of both the villagers and Jerry and Monique Sternin was, “Absolutely not. Every community has to be curious about the question. They have to accept the invitation, if you will, to do something about a problem they regard as really essential. They’ve got to be at the front end to figure out what’s going on, what’s working.

Then they’ve got to find the other positive deviants and learn what they’re doing. Then they have to figure out to disperse this information within their community. But if you go out and say to people in another village, ‘Gather and eat shrimps, crabs, and greens,’ they’re going to say something like, ‘You know, we’re different. Those guys eat weird things.’ You’ve got to get the community to own it.”

So what differentiates this from best practices in most applications is that there is always this invitation, an authentic invitation, that has to be accepted by the target group. (Richard Pascale in interview)

So what are the principles behind Positive Deviance?

It’s pretty simple:

…The standard model… is this hierarchical principle of experts and people in authority having the answers to our problems. That notion seems to be baked into the human psyche. It’s just generally true throughout the world. You always begin, and not wrongly, with looking for a technical solution. So [vaccines are] a great answer, and you don’t really need a lot of community mobilization if you’ve come up with a silver bullet and can prove that it works—that’s not a particularly hard sell.

But really hard problems—what we call adaptive problems—are imbedded in a  complex social system. They require behavioral change, and they’re rife with unintended consequences. These kinds of problems, like the ones that we’re facing all around the world: developed societies with governments that can no longer afford the social safety net, the healthcare problems in the United States, and any number of issues like that, are not just technical problems. They’re really adaptive problems.

You try the technical solutions. For reasons that are imbedded in the complex social system and behavior, they don’t work. When you impose them in a top-down fashion, you don’t get closer to anticipating all the unintended consequences. So they don’t work.

More philosophically, this is a quote from the PD website, from Lao Tzu
Learn from the people
Plan with the people
Begin with what they have
Build on what they know
Of the best leaders
When the task is accomplished
The people all remark
We have done it ourselves.”
~Lao-Tzu Tao Te Ching

That all sounds very good. But what impact has it actually had on the ground?

Here’s the great bit. The successful application of the PD approach has been documented in more than 41 countries in nutrition and a variety of other sectors from public health to education to business. The following is an illustrative sample of PD-informed program impacts over the 15 years:

  • Sustained 65 to 80% reduction in childhood malnutrition in Vietnamese communities, reaching a population of 2.2 million people.
  • Significant reduction in childhood malnutrition in communities in over 40 countries around the world.
  • Reduction in neo-natal mortality & morbidity in Pashtun communities in Pakistan and minority communities in Vietnam with near universal adoption of protective behaviors and social change.
  • Estimated  50% increase in primary school student retention in 10 participating schools in Missiones, Argentina.
  • Documented reduction in girl trafficking in impoverished communities in East Java, Indonesia.
  • Thousands of documented female circumcisions averted in Egypt and the formation of 12 “FGM free” communities.
  • In the Spring of 2005 Merck Mexico began a project to address the issue that only 13 out of 21 districts at Merck Mexico had a sales coverage of 100% or greater. Within  8 months  all districts (100%) were covering their sales target.

Wow. So how exactly does PD relate to traditional approaches to innovation?

According to the classic diffusion-based approach, innovation is based on the following principles:

  • it comes from the ‘outside’
  • it is pushed and promoted by a change agency anf through expert and  knowledgeable change agents
  • these agents use persuasive communication strategies to plug existing knowledge-attitude -practice (KAP)
    gaps among the client / user audience
  • they harnessing the influence of charismatic opinion-leaders, who serve as visible  role models of adoption for the non-adopters.

PD turns this model on its head. As one account puts it:

We are not suggesting the PD approach substitute for the classical diffusion of innovations paradigm. Rather, we argue that the PD approach provides additional options. We believe that often the wisdom to solve intractable social problems lies within the community. Diffusion in the PD approach is an “inside-out” process, in contrast to the classical dominant framework of “outside-in” diffusion.

As Pascale and Sternin put in a 2005 HBR article:

When identification of a superior method is imposed, not self-discovered, cries of “We’re not them” or “It just won’t work here” predictably limit acceptance. By contrast, a design that allows a community to learn from its own hidden wisdom is, among other things, respectful. Innovator and adopter share the same DNA. Community members invest sweat, and, in the process, they become partners to change.

Where has it been applied so far?

Hundreds of places. Here is a list of applications by organisation. Don’t be surprised if you see your own organisation on the list.

Can PD be applied to every problem?

Of course not. When there are proven remedies to technical problems—the Salk vaccine to polio, supply chain management practices, hardware and software solutions—companies can use them to work harder, faster, or smarter. And problems that rely on brainpower but that don’t require major behavioural adjustments are unsuitable for the positive deviance approach.

The method works best when behavioral and attitudinal changes are called for—that is when there is no apparent off-the-shelf remedy and successful coping strategies remain isolated and concealed. In such cases, change from within, discovered, celebrated, and implemented by the people who need to do the work. People are much more likely to act their way into a new way of thinking than to think their way into a new way of acting.

Sounds a bit familiar. So what’s the connection to complexity and evolutionary sciences?

The Power of Positive Deviance, the 2010 book, suggests that complexity science is a key part of the rationale for Postive Deviance. Specifically:

the [PD] process excels over most alternatives when addressing problems that (1) are enmeshed in a complex social system, (2) require social and behavioral change, and (3) entail solutions that are rife with unforeseeable or unintended consequences. It provides a fresh alternative when problems are viewed as intractable (i.e., other solutions haven’t worked).

And later on:

PD works like nature works… this isn’t an analogy; it is the way it is… nature tinkers with a different shaped bird beak or a slightly larger brian… natural selection does the rest, favouring variations that improve access to food and reproduction… In nature, this all plays out in evolutionary timescales of centuries or millennia. Emploring identical principles, the PD process achieves change within months or a few years…

How can I find out more?

Check out the September 2010 field guide and the PD website. Also take a look at the excellent book ‘The Power of Positive Deviance‘ – an excerpt is available here.

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A piece in yesterday’s New Scientist titled ‘Can Complexity Theory Explain Egypt’s Crisis?’ explores ideas of complexity in the context of the ongoing events in Egypt. It draws on the insights of two noted complexity thinkers – Yaneer Bar-Yam and Thomas Homer-Dixon. Excerpts are reproduced below with permission:

Egyptians are the world’s biggest wheat importers and consumers, and most are poor. As a result, the government maintains order with heavy subsidies for bread. It also runs the ports where imported wheat arrives, the trucks that haul it, the flour mills and bakeries…

[Such systems] are fine so long as the top of the hierarchy is in place, and can recover quickly. But take the top away – as is happening in Egypt – and the entire system risks collapse.

The early signs of this are showing. Bread is getting scarce in Egypt’s capital, Cairo. Bakeries are closing for lack of flour… Imported wheat is sitting in ports as cranes and lorries stand idle. The interlocking dependencies that tie modern economies together spread dislocation further. Even where there is food,  Egyptians have little money to buy it, as businesses and banks close, cash machines empty and wages dry up…

…The stresses of decades of dictatorship might have turned the entire Middle East into a “self-organised critical system”… The build-up of stresses makes such systems vulnerable to cascades of change triggered by relatively small disruptions…

The key argument of the article is that a hierarchical system (like the Egyptian government) facing a dynamic and interconnected problem is - in the extreme - prone to catastrophic collapse.

Regular Aid on the Edge of Chaos readers will know that this resonates strongly with previous reflections on this blog. The growing interconnectedness between finance, fuel and food systems was the focus of a recent piece exploring the ‘Globalisation of Vulnerability’. The maladaptive nature of organisational and governance systems in the face of change have also been covered on numerous occasions, including in a piece on ‘History on the Edge of Chaos’.

However, there is another vital dimension to complex adaptive systems that does not get sufficient coverage in the New Scientist piece. The author does briefly acknowledge that there are two sides to complex interdependencies: as well as collapse, they can also generate cascading change. (For an example, see the lessons from the Obama Presidential Campaign as recounted by veteran civil rights activist Marshall Ganz.) But the article misses out on the opportunity to reflect on the remarkable efforts of the anti-government protestors across Egypt through a complexity lens.

Without a doubt the most astonishing feature of the unfolding events in Egypt has been the leaderless, self-organised, networked movement that emerged and managed to maintain a peaceful and resilient presence – despite the efforts of the pro-Mubarak contingents.

As well as insights into collapse, complexity science can tell us something about how such movements happen, and give insights into the dynamic social processes that play out. It can tell us something about resilience in the face of oppression. It gives insights into the information and communication networks that feed and shape a movement. The ideas of complex adaptive systems can help us learn more about emergent collective action, and – through this – about how beliefs are reinforced, about how passion is shared and about how courage builds.

And – as we have seen repeatedly since January 25th - cascading, unpredictable change can have a profoundly human face.


Complexity science does more just than provide new ways to theorise descent, freefall and collapse. It can also help further our understanding of what human beings are capable of achieving. As Thomas Homer-Dixon, mentioned above, put it in the title of his book: there is an Upside to Down.

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When does crowdsourcing work best? New research from the Institute for Human Development provides answers which may be of relevance for aid projects and programmes.

There has been a lot written, spoken and blogged about the power of crowds in making decisions. In James Surowiecki‘s bestselling Wisdom of Crowds, published in 2004, the central thesis was that diverse groups are likely to make certain types of decisions and predictions better than individuals – even those with specialist expertise. As Surowiecki noted:

…under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them”.

The six years since the Wisdom of Crowds was published have seen the rise and rise of online social networking and related technologies. Social media and the power of the crowd have been at the heart of everything from political resistance movements to presidential elections (and indeed, resistance movements following presidential elections). The term crowdsourcing was coined in 2006 to describe an organisational approach that harnesses the creative solutions of a distributed network of individuals. As one of the originators put it:

Simply defined, crowdsourcing represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call. This can take the form of peer-production (when the job is per formed collaboratively), but is also often undertaken by sole individuals. The crucial prerequisite is the use of the open call format and the large network of potential laborers.”

There is a growing – some would say evangelistic – enthusiasm for crowdsourcing as the answer to a whole range of problems. Just a few initiatives off the top of my head: fundraising for socially responsible films, the development of transit planning in urban areas, combating corruption, creating markets for innovations, expanding scientific peer review processes. A quick Google illustrates just how expansive this agenda is.

The potential for crowdsourcing to contribute to international aid has also attracted a lot of attention, with perhaps the most prominent example being the role of new innovative technologies in the aftermath of disasters. The following is a typical example of the arguments made by the ‘pro-crowd’ camp:

The rapid proliferation of broadband, wireless and cell phones, coupled with new crowdsourcing technology, is completely changing the face of disaster relief. Everyone with a computer can provide crucial assistance, sifting through satellite photos, translating messages or updating maps, and most people are happy to do this free of charge — contributing to life-saving relief efforts is a powerful motivator… At a fraction of the cost of most relief budgets, crowdsourcing can solve coordination problems on the ground.

As many readers will be aware, crowdsourcing in disaster responses has been the focus of a passionate, sometimes vehement, and at times rather distracting debate.

My intention isn’t to retread ground that has already been well covered – and occasionally angrily stamped on – elsewhere. Instead, I want to explore evidence that tries to explain – following Surowiecki – the specific conditions under which a crowd is effective. Does recent research on decision-making yield any lessons or ideas worth a closer look?

Certainly, some of the crowdsourcing argument is borne out by the evidence. Numerous disciplines – from anthropology, cognitive psychology and evolutionary biology – suggest that collective decision making can help group members cope more effectively with unfamiliar contexts, and it is almost a cliche to say that humanitarian disasters are the archetypal unfamiliar context. However, reviews of this literature suggest many of these studies lack testable, well-structured concepts and hypotheses to explain exactly what collective decision making involves when compared to other kinds of decision making. They also often fail to examine the implications of different kinds of decision-making processes for the accuracy of decisions. These issues echo the challenges that have been put to the crowdsourcing community.

One recent exception to the above is simulation-based research that has been undertaken by analysts at the Institute for Human Development in Berlin. This work looks at a range of decision making processes, and suggests that there are two distinct ways in which groups can work to provide solutions to a problem.

First, individuals can follow specific ‘leaders’ in the crowd. This usually means drawing on those experts with information particularly relevant to the decision at hand. This is comparable to the typical aid decision-making process.

Second, crowds can work to aggregate information from the members, which is then made available to the crowd itself or to a third party. This enables decision making to be enhanced through ‘collective cognition’, a concept that underpins many of the arguments for crowdsourcing. This collective cognition can be unconscious emergent property, or it might be facilitated consciously through network interactions within the crowd.

The work by the HDI suggests a number of findings which are pertinent for the aid crowdsourcing debates:

  • a number of conditions influence when groups use ‘follow an expert’ or ‘wisdom of the crowd’ strategies. Specifically, the researchers found that the diversity of the group, the quality of individual information and group size all had a bearing on which approach is chosen.
  • in so-called single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions – where individuals should be able to consider the success of previous decision outcomes – the collective’s aggregated information is almost always superior
  • regardless of the decision-making approach taken, groups must have the potential to acquire information through social interaction, respond positively to those who possess pertinent information, and update their approaches based on the success of the previous decisions
  • In ephemeral and unstable social groups that make collective decisions only occasionally, individuals tend to follow the most informed individual. Stable social groups that encounter repeated decision points would do well to use some information aggregating process.

At the risk of over-generalising, the above suggests an emerging hypothesis – that for many simple or complicated issues where only one attempt is needed – ‘puzzles’ or ‘problems’, as a previous Aid on the Edge post put it – there is potential for experts to outperform crowds. The best illustration is to point out all those problems Malcolm Gladwell covered in Blink – detecting if a work of art was a fake, whether a teenager was carrying a gun, whether a fire would lead to a building collapsing, and so on.

In complex problems that require ‘multiple shots’, crowds can help augment expert perspectives by developing emergent solutions to evolving problems. The processes of information aggregation, transparent decision-making and effective feedback loops are essential here – all concepts which will be familiar to those interested in complex systems thinking.

Although the research is narrow, preliminary and based on mostly on theoretical simulations, the HDI work does point towards a more structured way of understanding the limits and possibilities of crowdsourcing. As such, it could be a constructive way to start to navigate some of the entrenched debates we have seen to date. Ultimately the research suggests that we shouldn’t be asking ‘does crowdsourcing work or not?’, but rather ‘when does it work, why, how, and with what benefits?’

This is not to say the answers will always be clear-cut or unambiguous, but asking the right questions will surely get us closer.

Now all we need is for some aid researchers to pick these concepts and questions up and run with them.

Or maybe an aid crowd would be better?

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Complexity scientists have long argued for the use of concepts such as nonlinearity and interconnectednesss to better understand economic phenomena, including growth, market failures and crashes. Ongoing research at the Harvard Center for International Development is taking this area of work forward in very promising ways.

In some ways, as Tim Harford has argued, the notion of complexity resonates closely with the classical roots of economic thinking. Adam Smith emphasised the importance of specialisation as a source of the wealth of nations, and “specialisation and complexity are closely linked: an economy with more specialists is one that requires more teamwork and more distinct interactions between individual activities.”

However, this is not how most economists – development or otherwise – have traditionally thought about growth. This may be about to change following ground-breaking work by researchers at the Harvard Center for International Development. Ricardo Hausmann and Cesar Hidalgo have been looking at economic complexity in a rigorous fashion in order to develop testable hypotheses about products, networks and self-organising processes of economic wealth creation.

The best introduction to this work is a thought-provoking TEDx talk Hidalgo gave in August 2010:

As Harford noted in his piece:

Development economists may find themselves paying more attention to such issues in future. We know very little about how to encourage an economy to become more complex and acquire new product capabilities. That may explain why we still have so much to learn about how to make poor countries rich.

There are of course downsides to complexity and interconnectedness, as the credit crunch and resulting global crisis has shown. Andy Haldane, director of the Bank of England,  put the case forward compellingly in a 2009 speech covered in a previous Aid on the Edge of Chaos post:

…interconnected networks exhibit a knife-edge, or tipping  point, property.  Within a certain range, connections serve as a shock-absorber.  The system acts as a mutual insurance device with disturbances dispersed and dissipated.  Connectivity engenders robustness. Risk-sharing – diversification – prevails. But beyond a certain range, the system can flip the wrong side of the knife-edge.  Interconnections serve as shock-amplifiers, not dampeners, as losses cascade.  The system acts not as a mutual insurance device but as a mutual incendiary device. Risk-spreading – fragility – prevails. The extent of the systemic dislocation is often disproportionate to the size of the initial shock…” (emphasis added)

The key may be to find the balancing point between the two: the point of ideal  trade-off between creativity and resilience – or what some theorists describe as the ‘edge of chaos’. This would seem to carry implications for economic development strategies used by international agencies. A follow-up post will look at how these ideas are being taken up in the IMF’s work on economic recovery in the wake of the crisis.

NB Interested readers can see previous Aid on the Edge of Chaos posts on the topic of complexity and economics here.

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