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Is this the ‘Next Big One’?

Posted in Agriculture, Biology, Communications, Evolution, Healthcare, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Research, Resilience, Strategy, Technology on April 23, 2013 | Leave a Comment »

As concern grows about H7N9 in China, this post explores the importance of managing such pandemic risks through collaboration, innovation and systemic thinking

In the month of World Health Day (April 8th), the latest outbreak of bird flu in Asia provided a sobering sense of the challenges the international community still faces. To date, H7N9 has killed 21 people, infected 104, shut down poultry markets across Asia, and has led to Chinese shares tumbling.

The WHO originally announced that the likelihood of this latest pathogen is transmittable between people is low, and that the world should not ‘get into a flap’ – as one observer memorably put it. The potential of bird flu to be the source of the “Next Big One” means however that we cannot be complacent. In terms of global catastrophic risks, it is hard to think of one more serious than the 1918 avian influenza epidemic which killed 50-100 million people worldwide – 3-5% of the global population. This was so devastating in part because the virus had acquired mutations that allowed it to cross from birds to humans, and then to ‘go pandemic’. Based on analysis of the mutations in H7N9, scientists fear that this latest variant may have the same potential.

But there is still a lot we don’t know about H7N9. Where has it come from, why, and how? What is its relationship to earlier variants? How might it mutate? What impact might it have in the future? What does it mean for our ongoing, historically loaded, battle with avian flu?

To answer such questions, we need to draw on a variety of disciplines: epidemiology, molecular biology, virology, all of which fit nicely with the current models of public health. The problem is that many of these models set us, humans, apart from nature. Diseases, the standard narrative goes, encroach on our territory and we need to fend them off. The reality is in fact the exact opposite.

It is now widely acknowledged that many – the majority, in fact – diseases are born in the intersection between society, environment and economy. More than 2/3 of all human infectious diseases are zoonotic in origin, meaning that they somehow crossed species boundaries. The terminology likely to be adopted in future Hollywood blockbusters on the topic is simple but evocative: ‘spillover’. Primates, birds, bats, pigs, rats, mice, dogs, insects – any creature we co-exist with can act as sources or carriers of pathogenic lifeforms.

Spillover is driven by a pattern of activity which is becoming all too familiar. Deforestation: 4% increase can lead to 50% increase in malaria rates. Hunting has led to HIV-AIDS, Ebola, all crossing the species boundary. And, to bring it back to bird flu, livestock. Around 70% of the rural poor and 10% of the urban poor are dependent on livestock. Livestock conditions are increasingly creating tremendous opportunities for pathogens to cross from wild birds to caged birds, and onto humans. And the demand for animal-based protein is expected to grow 50% by 2020, much of it in the developing world. This problem is not going to go away any time soon.

Leapfrogging on the success of the human race, trade and transport linkages provide a morbid global transmission network. The rate at which new diseases are emerging and spreading is nothing short of shocking.  Zoonotic diseases have increased in the past 40 years, with at least 43 identified outbreaks since 2004. ILRI estimates that 1.7 million people die each year thanks to spillover diseases. By way of comparison, the highly respected CRED crunch on disaster epidemiology found that the 2001-2010 average annual deaths from natural disasters was 107k  per year.

Ecological and evolutionary principles are vital in understanding these complex system effects on a more solid scientific basis. Experts at the University of Florida made the point in pithy fashion a couple of weeks ago, “If we don’t understand the reservoir and the ecology of the virus, it’s hard to design interventions to protect humans.” But such understanding is – with a few exceptions – still under-utilised in public health.

Of course, every disease is different, every context is unique. But the process by which spillover happens is similar. We can point to ecologies under stress. Life forms under duress. As an excellent briefing by colleagues in the Consortium on Disease Dynamics (CDD) puts it, “The health of people and animals are… interconnected and inextricably linked to the environments both inhabit. Given the complex pathways that lead to spillovers, it is important that prevention and control measures are undertaken with a strategic approach and an understanding of the many interdependencies.”

What does this challenge add up to for the global risk management community? The work by the CDD gives some very useful pointers.

First, multi-disciplinary approaches are vital. The WEF Global Risks report has for some years now been calling for better disciplinary collaboration in order to think about emerging risks. With avian influenza there is a clear need for better collaboration between public health specialists, disease ecologists and evolutionary biologists. Some important work is already happening, under the auspices of entities such as the global One Health initiative, organisations like the EcoHealth Alliance, and initiatives like the USAID-funded Predict, and this work needs to move firmly to the centre of the debate.

Second, anticipation and warning systems – new investments in surveillance are urgently needed to establish and maintain necessary systems at multiple levels – community, national, regional and global. We need multi-stakeholder information platforms, bringing together government, civil society and the private sector in new kinds of networks, in order to establish ‘systemic  surveillance approaches’. This needs to move beyond a focus on specific disease to looking at the whole system, looking at the intersection between disease drivers, disease incidence, and socio-economic factors.

Third, new approaches – especially in the realm of complex adaptive systems – have a lot of relevance for how we think about such outbreaks in the future. Methods such as systems thinking, network analysis, agent-based simulations, and dynamical systems theories can help develop a more precise and accurate understanding of the complex dynamics of disease. Together with the rise in ‘big data’ approaches, there is scope to develop new models and theories of how pandemics unfold which are more appropriate to our ‘hyperconnected world’. We need to be careful however to ground this science in local, community understanding – to support affected communities to become the frontline of defence: adaptive managers of emerging risks.

Fourth, we need changed funding models – funding prevention, not just response, and linking pandemic risks to high-risk development activities, and ensuring that we don’t forget history too quickly. There needs to be attention even when the threats may not be imminent. The private sector, with interests in business continuity, can be key actors here. Done right, such investments can engender what might be seen as positive spillovers. As the CDD work suggests, investments in prevention of avian influenza can provide the basis for such work on other potential pandemics.

In closing, if we want to take a wide-angle lens on the problem of disease outbreaks like HN79, to understand why these diseases are occurring at an increasing rate, we could do worse than taking a lead from Nathan Wolfe. A globally renowned virologist, a couple of weeks ago Wolfe wrote a tub-thumping piece on the WEF blog about the continued risks of unregulated hunting, especially bushmeat, which gave birth to human immunodeficiency virus (HIV). His basic argument was by ignoring the implications of our food production systems, we are running an unacceptably high risk of terrifying global scourges in the future.

Clearly, we all need to start pay much more attention to the intersection of economy, society and the environment if we are serious about proofing ourselves against the Next Big One.

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Beyond Linear Development Trajectories: What if there were 5 clusters of quite different developing countries?

Posted in Biology, Economics, Evaluation, Evolution, MDGs, Public Policy, Reports and Studies, Research on October 2, 2012 | 7 Comments »

This guest post by Andy Sumner and Sergio Tezanos Vázquez explores new approaches to classifying developing countries, based on a new IDS Working Paper published last week. In the paper, they develop a more precise and accurate classification system for low and middle income countries, and suggest that this can support a more complex, non-linear  appreciation of development trajectories. This post concludes with the intriguing notion of ‘development landscapes’ (building on a recent CGD blog post written by Owen Barder and myself). 

Sergio

Andy

In 1963 Dudley Seers wrote –in The Limitations of the Special Case– of developing countries:

[t]he typical case is a largely unindustrialised economy, the foreign trade of which consists essentially in selling primary products for manufactures. There are about 100 identifiable economies of this sort, covering the great majority of the world’s population (1963, p. 80).

 And perhaps most famously, Seers wrote in The Meaning of Development:

 The questions to ask about a country’s development are therefore: What has been happening to poverty? What has been happening to unemployment? What has been happening to inequality? If all of these three have become less severe, then beyond doubt this has been a period of development for the country concerned […] If one or two of these central problems have been growing worse, especially if all three have, it would be strange to call the result ‘development’, even if per capita income has soared (1969:24).

Since then many have challenged the use of income per capita as the primary proxy for development. Of course, in addition to low and middle income countries there are many classifications – notably those by Human Development and the Least Developed Countries classifications and the new pioneering work of the Oxford Poverty and Human Development Initiative.

Our new paper continues this tradition with what might be described as a complex systems twist. The paper challenges the continuing use of income per capita to classify developing countries as low income countries (LICs) or middle income countries (MICs), given that most of the world’s poor live in the later group (see here,here, here, here).

Further, it highlights the ambiguity over the usefulness of the MIC classification given the diversity in the group of over 100 countries that includes Ghana and Zambia, as well as India, China and Brazil.

Finally, it points to a new way of framing development, which moves beyond the linear league table approach which tends to imply a certain shared pathway (cf critiques of HDI as ranking countries on a scale of 0 to Denmark). In closing, we touch on one example of what this might  look like below: there are no doubt others.

We used a cluster analysis to identify five types of developing country using a set of indicators for 2005-2010 covering definitions of development based on the history of thinking about ‘development‘ over the last 50 years from four conceptual frames:

  • development as structural transformation;
  • development as human development;
  • development as democratic participation and good governance;
  • development as sustainability.

This is what we found:

Our development taxonomy differs notably from the usual income classification of GNI per capita (Atlas method) used to classify LICs and MICs. Notably many countries commonly labelled “emerging economies” are not in the emerging economies clusters because they retain characteristics of poorer countries.

Our analysis generated 5 clusters as follows:

Cluster 1: High poverty rate countries with largely traditional economies. Those countries with the highest poverty and malnutrition headcounts, who are also countries with low productivity and innovation and mainly agricultural economies, with severely constrained political freedoms. This cluster includes 31 countries, some of them might be surprising: Pakistan, Zambia, Nigeria, and India.

Cluster 2: Natural resource dependent countries with little political freedom. Those countries with high dependency on natural resources, who are also countries with severely constrained political freedom and moderate inequality (relative to the average for all developing countries). This cluster includes 9 countries, such as Mauritania, Vietnam, Yemen, Cameroon, Congo, Swaziland and Angola.

Cluster 3: External flow dependent countries with high inequality. Those countries with high dependency on external flows, who are also countries with high inequality, and moderate poverty incidence (relative to the average for all developing countries). This cluster has 32 countries, such as Senegal, Ghana, Indonesia, Thailand, Peru, Colombia, and Panama.

Cluster 4: Economically egalitarian emerging economies with serious challenges of environmental sustainability and limited political freedoms. Those countries with most equal societies, who are also countries with moderate poverty and malnutrition but serious challenges of environmental sustainability and –perhaps surprisingly– limited political freedoms. This cluster has 15 countries, including China, Azerbaijan, Belarus, and Kazakhstan.

Cluster 5: Unequal emerging economies with low dependence on external finance. Those countries with the lowest dependency on external finance and who are also countries with the highest inequality. This cluster includes 14 countries, such as South Africa, Botswana, Costa Rica, Malaysia, Argentina, Mexico, Brazil, Turkey, Chile and Uruguay.

Two-thirds of the world’s poor – not surprisingly given the characteristics noted above – live in Cluster 1 countries though this is largely due to the inclusion of four populous countries (Bangladesh, India, Pakistan and Nigeria –and one should remember a third of world poverty is accounted for by India). About a quarter of world poverty is situated in Cluster 3 and Cluster 4 countries and the remaining 5% live in Cluster 2 and Cluster 5.

What is most striking – and of particular relevance for readers of this blog – is that we find that there is no simple “linear” representation of development levels (from low to high development countries), as found in the Human Development Index or its variants.

We find instead that each development cluster has its own and characteristic development issues. Building a development classification is not a simple task: once we overcome the over-simplistic income-based classification of the developing world, we find that there is no group of countries with the best (or worst) indicators in all development dimensions.

It thus would be more appropriate to build “complex” development taxonomies on a five-year basis than ranking and grouping countries in terms of per capita incomes or other indicators, as this will offer a more nuanced image of the diversity of challenges of the developing world and policy responses appropriate to different kinds of countries.

In keeping with a growing consensus in development thinking, this way of thinking points to an understanding which is less about linear progression, and where change is path dependent, and development policy is best seen as an evolutionary process of learning.

What might such a complex taxonomy look like? A fruitful avenue to explore (following the recent post by Ben Ramalingam and Owen Barder on complexity and results) might be the notion of fitness landscapes from evolutionary biology, which illustrate how different species attain biological fitness through processes of variation, selection and amplification.

Erik Beinhocker and Tim Harford have argued that growth and innovation can be likened to a evolving search of a dynamic fitness landscape. “Development Landscapes” might provide an analogy and a model for explaining the distinct but related positions of countries in different development clusters.

This would enable analysts and policy makers to examine the different pathways the cluster members have been on, and to explore the space of possibilities for future change.

About the guest post authors:

Sergio Tezanos Vázquez is an associate professor at the Economics Department and a research fellow at the Iberoamerican Research Office on International Development & Co-operation at the University of Cantabria.

Andy Sumner is Co-Director of the newly established, King’s International Development Institute, King’s College London.

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Conversations on Complexity: A Tribute to Elinor Ostrom

Posted in Biology, Evolution, Influence, Institutions, Knowledge and learning, Leadership, Organisations, Public Policy, Resilience, Strategy on August 16, 2012 | 12 Comments »

Many people around the world were deeply saddened to hear of the death of Elinor Ostrom in June this year. By way of a tribute, this extended piece brings together some of her ideas on the implications of complexity science for development aid. It draws on material from a series of interviews I conducted with Professor Ostrom between 2009-2012 for use in my forthcoming book, and has been approved for publication by her colleagues at The Workshop, Indiana University.     

When Elinor Ostrom won the Nobel Prize in 2009, the Swedish Royal Academy of Sciences made the following statement:

[she] has challenged the conventional wisdom that common property is poorly managed and should be either regulated by central authorities or privatized. Based on numerous studies of user-managed fish stocks, pastures, woods, lakes, and groundwater basins, Ostrom concluded that the outcomes are, more often than not, better than predicted by standard theories.”

Challenging standard theories was a running theme Professor Ostrom’s work. Ideas of the commons and how they really worked were central to this, as was the analysis of institutions and the sustainability of social-ecological systems. Complexity was a particular interest: ideas of systems, self-organisation, the evolution of rules, institutions as emergent phenomena and resilience are all repeated motifs in her papers, books and speeches. Indeed, her 2009 Nobel Prize Lecture – she was the first and, to date, only woman to win the Economics Prize – builds on the distinction between simple and complex human systems, and closes with the following words:

We should continue to use simple models where they capture enough of the core underlying structure and incentives that they usefully predict outcomes. When the world we are trying to explain and improve, however, is not well described by a simple model, we must continue to improve our frameworks and theories so as to be able to understand complexity and not simply reject it.

She was on the Santa Fe Institute (SFI) Science Board for five years. Corresponding in 2011 while I was visiting the Institute, she wrote to tell me that it was one of her favourite places in the world. It is easy to imagine how the SFI approach naturally appealed to someone who had spent her life’s work breaking down disciplinary boundaries.

My small-scale engagement with Professor Ostrom started in 2009 following the publication of a report on complexity science and aid I led on while at ODI. She used it as reading material for her students in Autumn 2010 and kindly wrote back to tell me how useful she had found it. We subsequently had telephone and face-to-face interviews on the topic of complex systems, development and aid. These discussions, and of course her rich body of work,  helped to shape the ideas in my forthcoming book, which she warmly encouraged from the outset. I have used material from these interviews to write this post.

*

What is your view on complexity and the complexity sciences? What is the value of this approach?

I get so upset when people use complexity as a reason not to do things – complexity and context are essential for operating in many different situations. In order to make sure decisions are relevant, we have to understand the context of decisions, and the complexity of the situation. My take on complexity is that it is a key set of concepts which are essential for understanding how the world works. There are many situations where simple models don’t work – when there are 10, 15, 20 variables. For example, think about situations where problems are nested within each other or situations where there are many actors capable of actions, conflicting information about transmissions and payoffs and diverse outcomes. Here, the ideas of complexity can lend a hand by providing a means of analysis and understanding the reality of these action situations.

How would you apply these ideas to international aid agencies?

The last thing aid agencies want to do is analyse things as a complex system! (laughs) But how do you unpack systems without such analyses? In biology and ecology, there is a necessity of using complexity science and related ideas as a model – although it is not always acknowledged, they do have to use it. For example, in a situation with 10, 15, 20 species, how do you understand the potential impact of the elimination of one species, when one unit being eradicated would cause disaster rather than simply being important. We can’t address these questions without drawing on complexity theory in some way.

The lack of long timeframes and a lack of supporting cultures means that aid agencies don’t help people learn how to think about and change the structure of the situations they are facing. In many situations, this is because of colonial roots of aid, which did not respect local institutions – they didn’t understand them so they were treated as non-existent.

The difference between this approach and that of Darwin is stark – the care and diligence that was given to studying animal species in the 19th century is so evident, and it from this that we have evolutionary theory. But these countries also had people, but there was no attempt to understand their knowledge systems, the rules they had developed to manage various kinds of socio-ecological systems… Colonial powers assumed we have the answers, and destroyed social capital. Aid agencies, unfortunately, do much the same thing.

What are the biases of development aid that you see inhibiting the take-up of a more complex, realistic way of doing things?

Development aid asks the question: where can we pour money in to make the most difference in the most visible way (laughs). This is not especially amenable to complex ways of understanding the world. Most projects are 2-3 years, some are 7, but these are big engineering projects, and then they disappear.

‘Fitting’ is all important in this context. Many agencies today have blue prints for situation A, but they are so ingrained they can’t deal with B, C, D and E. Some employ very inspiring young people, but they are not keen to stay long in their organisation – 4-6 years max, they tend not to be sanguine about the future. This is understandable, but it also goes against what we know about bringing about social change.

Take the Sida work. We said, we want to understand the role you play in sustainable development – tell us what your best projects are. And we found that their best projects were relative failures because of exactly these issues.

The most fundamental change is to change the social science curriculum to change the way that development is taught. We need to get away from treating governance as top-down. The presumption of almost all work is that a hierarchy will work effectively, gather information from variety of sources and develop tactics of behaviour. In complex systems, there are many different areas, all moving in different directions and at different speeds, doing localised things which are relevant. The idea that a central processing unit that can gather up all of this information and make decisions about the whole system… the theories fall down.

I developed a framework to understand complex social-ecological systems, which builds on my work on governing the commons. This sets out the key design principles for complex systems which sit on the interface of society and ecology: watersheds, fisheries, increasingly the whole planet. Some of the reaction has been very enthusiastic – some people, the biologists, the ecologists, the complexity scientists, love it. Others hate it, they say it’s not science, it’s too complex.

What examples do you see of good development practice, which do take account of complexity?

I was at a conference recently in the north of Sweden, for the Childbirth Foundation, 1000 young people from 100 countries. They were trying to answer the question – how do you change the way the world works to develop more opportunities in developing countries. There were lots of ideas, using the market, ideas like cooking stoves and many others, all aimed at the broader goal of dealing with climate change, bringing about development. Some of these ideas have already been applied; some are still being tried out. But the key is that they are doing development in a way that has a chance of working.

And there were no international development agencies present. They should have been there, just to see what was being discussed. The key difference was that while international development agency way of thinking has seen a lot of failure, they haven’t picked up yet on the answer, which is that we must have multiple approaches, small and experimental and larger and more concrete. But apparently the taxpayer doesn’t like to see experimentation with their money!

Aid agencies tend to not involve staff in anything other than a project, and sometimes only for part of a project. And when the project ends, they leave. Mr Shivakumar, a colleague who worked on the Samaritan’s Dilemma, has done work with Action Aid in their Ethiopia programme. They will go to a site where they are trying to help farmers build up their capacity, say for public services. They are there for some time, but they try to do something 5 years before they are going to leave. They will call a meeting and say ‘we will fund 1 year for 100%, after that, we will drop to 80%, and you need to support 20%, then down to 60%, and so on… If we are doing something good, then you should want to carry it on. If not, that’s fine, the project closes. That is an example of an aid  philosophy that takes account of complexity.

Look at Grameen Bank, that started off slowly, and if it was cut down after 2 years it would never have turned into the institution it is today. But it worked because it was a system within a system within a system. It didn’t have public official waiting for a report on a Friday afternoon before they could go home. It had lots of people in localised situations who presented and developed rules for how things would work, providing some basic structure for example, you have to meet every week, we have to put money on the table, we have to be forgiving at times… These small-scale units proved to be very innovative and creative. Small-scale units can be very adaptive in changing – look at family units when a child arrives, or a job changes. They can deal with the complex, but they are guided by a different philosophy to development agencies. They don’t have to come up with winning solutions, they can learn from their own successes and diversity of other approaches, they can change things if they are not working.

There is a growing interest in resilience in development circles. Do you see this a promising line of enquiry?

Resilience and sustainability are very similar – these systems have similar properties. It is another attempt to get this kind of thinking into institutions, and just the latest one. The work of the Stockholm Resilience Centre is very important here, and they have been successful in influencing a number of research agendas. But I think a lot of the time policy people are just using the language without knowing what they are talking about (waves hand in mock exasperation). When I did my work on social-ecological systems I was very careful to build on the work of ecologists and social scientists, so it was a truly integrated framework. The trouble with much of the development policy work around resilience I see is that so much of it doesn’t really try to engage with the science of resilience, but instead uses as a catch-all to further particular institutional interests. We need some real serious thinking on this issue if it is going to make a meaningful contribution.

What do you think are the implications of the science of complexity for big conferences like Planet Under Pressure and Rio+20?

We are lucky that there is growing interest in this area, from academics and researchers across the world. The work you have been leading on your book – I think this is work of immense value and importance for the development sector. The crucial question is whether international agencies are ready to hear the message and willing to act on the lessons. I know there are others leading similar work in other closely related sectors like environmental sustainability, community development and social entrepreneurship – about the complexity inherent in different resource systems and under different rules.

We need a broader approach to these systems. What we are lacking at the moment is a shared framework. Without this, how are we to ensure our knowledge accumulates? A shared framework of complex systems can help us ensure knowledge in these different fields is not isolated. There are more people working on this, and they are working together more, but not enough is being done yet.

What do you see as the key to the success of Rio+20?

There needs to be more connection around this challenge through a shared lens of complex social-ecological systems, and these large-scale events provide an opportunity to do this. The key principle informing all of this has to be taking a more evolutionary, polycentric approach to policy making.

This is the key challenge we face, and it is only going to grow with time. I am hopeful though. If there is one thing I think I have learned it is that just because we have a certain emphasis in our institutions today, it doesn’t mean we are stuck with it forever.

 What three things would you change among aid agencies to get them to take more account of complex realities?

The number one thing is the ‘spend it or lose it’ mentality – it is common to most bureaucracies, but getting it changes is essential if aid is going to be tailored to the complex realities of development. This institutional change will allow many others to come about, and so it is a very important one.

Number two is getting aid agencies to be more of a learning enterprise and less of a doing enterprise. This means feedback, training, reflection. This means not assuming we have the answer. We need to create an environment where discussion and debate are openly welcomed, and where redundancy is not always seen as bad, just excessive redundancy.

Third, we need to reward people for developing imaginative ideas that draw on the complexity of the real world, that leave people in developing countries more autonomous, less dependent, and more capable of crafting their own future.

*

There are of course many tributes to Professor Ostrom, by people far more qualified than me to write them. For my own small part, I feel grateful to simply to have known her: for her time and support; for how generous she was with the benefits of her towering intellect; and for her gentle, playful sense of humour. The fact that someone of her stature could take the time and care to engage with and mentor me and so many others like me around the world speaks volumes about the extraordinary person she was.

She will be deeply missed – as the Indiana University president said in his statement, we have “lost an irreplaceable and magnificent treasure”.

“We have to think through how to choose a meaningful life
where we’re helping one another in ways that really help the Earth.”

Elinor Ostrom, 1933-2012

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The Limitations of Imitation

Posted in Biology, Evolution, Influence, Innovation, Knowledge and learning, Leadership, Reports and Studies, Strategy, Technology, Trade on May 18, 2012 | 1 Comment »

Innovation is getting a lot of attention at the moment in development and humanitarian work. Many, including myself, see this as long overdue. But, according to an article in this  weeks Economist, this attention may be misplaced. The piece makes a strong argument for the importance of imitation  in business, and its advantages over innovation. In this post I want to take a look at these arguments for imitation. I also want to see what complex systems research tells us about the limits and possibilities of such an approach.  

I: The Virtues of Copying?

Innovation is essential. Countless speeches, articles and books attest to its central importance – in economic growth, business success, and organisational effectiveness. As a result, imitation is a “heretical idea”.  But the uncomfortable truth, according to the piece in this weeks Economist at any rate, is that in the real world, firms that copy others are more successful.

Examples include:

  • The iPod, the iPhone and the iPad were not the first of their kind: “Apple imitated others’ products but made them far more appealing.”
  • Pharmaceutical firms can be divided into inventors and imitators, and some inventors have joined the copycats, selling generic drugs
  • Supermarket own-label products copy well-known brands, making for a multi-billion dollar product category
  • High street fashion firms consistently copy innovations from the catwalk.

Such imitators often proved to be the winners in business:

copying is not only far commoner than innovation in business… but a surer route to growth and profits…. studies show that imitators do at least as well and often better from any new product than innovators do. Followers have lower research-and-development costs, and less risk of failure because the product has already been market-tested…”

So why isn’t imitation lauded? The key issue is that the incentives for copying are weak – whether legal, individual, organisational or cultural. Set aside the obvious issue around patents, and we learn that “praise and promotion do not go to employees who borrow from other firms.” A study of how new product development firms go about their work found that none had a formal or informal policy for responding to other firms’ innovations, making them slow to learn from others successes. And there may well be cultural factors at play here, although the piece made what for me was a rather lazy and out-dated comparison: US firms tend to be obsessed with innovation, whereas Asian firms are far better at legal imitation.

II: Imitation or Exploring Adjacent Possibilities?

I finished the piece feeling that there was more to this issue than the author acknowledged. At best, the analysis was incomplete, at worst, very simplistic.

First, many of the successful “copying” efforts highlighted in the article highlights were far from easy or straightforward. The process of imitation involves numerous adaptations and innovations – some of them significant and certainly not cheap.

Take the iPhone, which is held up as a kind of archetype of copying. Clearly it wasn’t the first smartphone, but compare it to what was around at the time and it is clear that Apple weren’t simply copying and pasting ideas. The idea that Apple somehow saved on the R&D costs of the iPhone because of the advances made in prior products seems risible.

To borrow an idea from evolutionary biology, I would argue that the key to the most of the successful imitations that the article presents is really that many of the so-called copycat firms explored the “adjacent possibilities” – the diverse and emergent possibilities that spring up around a new idea, product or process.

Stuart Kauffman uses this concept to explain how such powerful biological innovations as sight and flight came into being. More recently, Steven Johnson showed that it’s also applicable to science, culture, and technology.

The core of the idea is that people arrive at the best new ideas when they combine prior, adjacent, ideas in new ways. Most combinations fail; a few succeed spectacularly. So, copying might succeed – but there are all kinds of examples where it doesn’t.

III: Innovation in the App Ecosystem

Second, and perhaps even more importantly – you need innovators to copy from in the first place. No innovators means nothing to copy which means nothing to sell. To really get to grips with the ‘imitation vs innovation’ debate, the Economist piece needed to pay more attention to the overall system of firms, consumers and products – and the dynamics and interactions in that system. Fortunately, recent and excellent work by two researchers at University College London addresses exactly this issue in the context of mobile applications, and presents some very pertinent conclusions.

The “app economy” of producers and users that has sprung up around the applications that run on mobile phones and devices is nothing short of startling. It has been referred to by industry experts as “one of the biggest economic and technological phenomena today”.

Soo Ling Lim and Peter Bentley of UCL were interested in the dynamics of the app economy, in particular wanted to understanding the role of innovation.

Lim and Bentley – one is a prize-winning software engineer, the other a successful “app entrepreneur” – started with the insight that will be familiar to Aid on the Edge readers: that the app economy was best seen as a “co-evolving system of apps, developers, and users [who] form complex relationships, filling niches, competing and cooperating, similar to species in a biological ecosystem.”

Apple releases very little data on its stores and so the researchers decided that the best way to get around this would be to build an ecosystem model to simulate the dynamics they observed. They programmed agents in their model – appropriately named AppEco – to mimic the behaviour of developers and consumers.

Developers build and upload apps to the app store; while consumers browse the store and download the apps. They also programmed apps – passive artefacts in the ecosystem which are the key means by which the agents interact.

They then programmed their developers with different characteristics. They identified five broad types of developers in the real app economy, and built these characteristics into their model agents. They are innovators, optimisers, milkers and copycats, and flexibles. Although specific to the app economy, there are clear parallels in most other sectors and contexts.

  • Innovators are those developers who come up with groundbreaking apps – like AroundMe or TuneIn Radio. In te model, innovators were developers who produced different apps in a variety of categories – including social networking, business, utilities, and productivity.
  • Optimisers take a hit formula, such as the Angry Birds franchise, and try to adapt and continually improve it. In the model, these developers were the learners – they took their own best app and made variations on it.
  • Milkers have one specific idea and use it repeatedly. They might, for example, create apps for each of hundreds of town maps, rather than building one app that can call up many maps. In the model, they used their most recent idea and varied it repeatedly.
  • Copycats build knock-offs of top-selling apps – see Angry Chickens or Angry Dogs – and work by appealing to or confusing users who end up buying the facsimiles.
  • Flexibles begin with one of the strategies above, but change their strategy based on the strategy of the top developers.

Lim and Bentley then calibrated the model to match the behaviour of a real app store, in this case, the Apple iOS App Store, which is the oldest and best established store. They used three years worth of publicly available data from the store and primed the model until it closely resembled the behaviour of the real store.

The next stage was to run some “what if” experiments. The specific questions that inspired these experiments have direct relevance to the Economist piece. For example, with so many developers trying out different strategies to increase their downloads, Lim and Bentley wanted to know if an innovative developer would receive more downloads compared to a copycat developer.

At the start of each simulation of the App economy, all five categories of developers contributed an equal number of apps, but different constraints were placed on the system. A whole range of different scenarios then evolved, including the following:

  • If the proportion of apps from each group was kept constant, copycats made the most money initially.
  • Over time, however, the overall ecosystem suffered from a lack of novel products. Dissatisfied users moved onto better platforms
  • Copycats rely on good apps created by other strategies; it is extremely difficult for an ecosystem to support a large proportion of copycats. (This result mirrors the app stores in the real world – copycat developers regularly appear and take advantage of the success of others, but nevertheless their strategy remains in the minority.)
  • When consumer choices dictated which apps were successful, it was the optimisers who sold the most apps, followed by innovators, milkers and finally the copycats.

The general conclusions in the authors’ summary paper seem clear:

In a complex ecosystem no strategy can be a guaranteed winner, but our results indicate that some strategies should be chosen more frequently than others. Innovators produce diverse apps, but they are hit or miss – some apps will be popular, some will not. Milkers may dwell on average or bad apps as they churn out new variations of the same idea. Optimisers produce diverse apps and tailor their development towards users’ needs. Finally, Copycats may seem like the best strategy to guarantee downloads in an app ecosystem, but the strategy can only work when there are enough other strategies to copy from. In addition, this strategy can only exist in a minority, otherwise app diversity will decrease (many duplicated apps result in a scarcity of some features desired by users) and the fitness of the ecosystem will suffer.” (emphasis added)

IV: Conclusions: Mix it Up

Both of these ideas – “adjacent possibilities” and the ecosystem model – suggest that the Economist article downplayed the difficulties and limitations of imitation. While it briefly acknowledges Schumpeter’s concerns about imitation dominating industry, the overall tone is bullish, suggesting that: “copying is here to stay; businesses may as well get good at it.”

The reality, however, is that copying is seldom a straightforward matter, and can take as much creativity and resources as innovation.

And there should be serious concerns about the overall health of a ‘ecosystem’ dominated by copycats. Copying is a strategy that can only work for a minority of players, and then only for limited periods.

Any lessons here for the aid system, I wonder?

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JP Morgan and the Price of Complexity

Posted in Accountability, Biology, Chaos, Economics, Evolution, Financial crisis, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Self organisation, Strategy, Technology, Trade on May 15, 2012 | 1 Comment »

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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New Scientist Briefing: Can ecosystems show how to fix the euro?

Posted in Biology, Economics, Financial crisis, Innovation, Institutions, Knowledge and learning, Networks, Public Policy, Technology, Trade on November 10, 2011 | 6 Comments »

The eurozone, like the rest of the world economy, is a complex networked system. That gives it properties economists rarely consider but which could help us understand the current crisis. This New Scientist ‘Science in Society’ Briefing examines the issues.

What is a complex network?

Complex networks have many interconnected components which influence each other’s behaviour. These changes then feed back on each other. A famous example is the numbers of predators and prey in a given environment, which vary in a complex interdependent way. The eurozone – the 17 countries that share a common currency, the euro – is similarly interdependent, with similar feedback mechanisms.

All complex networks are governed by a balance between negative feedback, such as interest rates, which is stabilising, and positive feedback, such as the self-reinforcing erosion of trust in markets, which is destabilising, says physicist Len Fisher at the University of Bristol, author of Crashes, Crises and Calamities: How we can use science to read the early-warning signs.

How does that help us understand economic crises?

In certain circumstances, one type of feedback can end up dominating the system, causing it to change so dramatically that it flips to another state. Examples include the way animal populations can suddenly collapse or the way economies can slip into recession.

These tipping points tend to be highly unpredictable. Even so, Fisher says computer models of the system can still show how the system can change. Yet leading economics journals, he says, do not accept computer-modelling studies. “Mainstream economists have not considered these non-linear effects,” agrees Oonsie Biggs of Stockholm University’s Stockholm Resilience Center in Sweden.

Can we understand complex systems well enough to control them?

Maybe. The diversity of a network’s components and the density and strength of its connections – called its connectivity – affect the system’s resilience, or resistance to change. More connections make a system more resilient: if one component fails others can fill in. But only up to a point. Go past a certain threshold and more connectivity makes the system less resilient because a single failure can cascade to every other component.

The trick is to get the balance right. “Cascades of failure may be controlled by changing the nature and strength of the links between various parts of the networks,” says Fisher. Much current research in complex systems focuses on assessing connectivity correctly to enable that. Other work aims to detect behaviour that indicates an imminent collapse.

So turning 17 separate currencies into one eurozone was a cascading failure waiting to happen?

Yes. That is why Greek debt is a crisis, even though Greece accounts for only 2.5 per cent of the eurozone’s GDP. News of its debts caused the trust that markets placed in Greek government bonds to plummet. Its creditors are mainly in the eurozone, so a Greek default is causing markets to lose confidence in other members, such as Italy – which is too big to bail out.

Could the crisis have been avoided?

Complexity theory shows what went wrong. Yaneer Bar Yam of the New England Complex Systems Institute in Cambridge, Massachusetts, says his still-unpublished studies show that investors profited by driving down the value of Greek government bonds, triggering the crisis. If instead of national bonds issued by sometimes weak economies, the eurozone had one common bond backed by powerhouses such as Germany, such an attack could not have happened.

Germany rejects eurobonds. But, says Bar Yam, complex systems such as multicellular organisms show that “if you are going to accept common risk, you have to invest in defences that extend to the weakest member”. Either that or make sure an attack on a weak member cannot spread, a technique that ant colonies have perfected: the death of a single ant has little effect on the colony as a whole. “Biology has solved this problem several ways,” says Bar Yam.

If connectivity is a risk, why create the euro?

Connectivity is also profitable as it makes economic production much more efficient. And it can adapt to problems: connectivity allows other eurozone countries to help Greece, and to build better common defences.

Trade-offs between efficiency and resilience may mean we need to develop ways to make systems more stable, such as pruning connectivity or paying for defence measures. “We now have the quantitative, analytical tools to do that,” says Bar Yam. Such models may also show when short-term costs that reduce a system’s efficiency may be warranted because of the long-term benefits of increased system resilience.

Some connectivity problems could be hard to prune, though. Biggs says close coupling between major global hubs, such as the eurozone and the US, is a big source of instability that which may threaten strong contributors in future, like France and Germany.

Why don’t economists know this?

They are starting to. Some economic theorists have drawn parallels between financial networks where bank failures are prevented, and forests where small fires are always put out. Such forests accumulate deadwood fuel and lose patchiness, increasing connectivity. When a fire eventually breaks out, it becomes huge. That’s why forest managers now encourage regular, small burns. Similarly, banking networks may need low-level failures to prune connectivity and risk.

Systems-based thinking has even reached the Bank for International Settlements in Basel, Switzerland, which sets global rules for the capital a bank must hold to back loans. It announced this month that the risks posed by banks depend on their “size, interconnectedness, complexity and global scope”. So from 2016, “global systemically important banks” – initially 29 of them – will be required to keep more capital on hand per dollar loaned than less vital banks. This is partly to “discourage banks from becoming even more systemically important” – in other worlds, too big to fail.

This recognition of the importance of complexity has been largely confined to banking, however. The eurozone is a network of governments. It is not clear how eagerly they will adopt a way of thinking that truly puts collective interests first.

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The Humpty-Dumpty Problem

Posted in Biology, Evolution, Innovation, Knowledge and learning, Networks, Reports and Studies on June 23, 2011 | 1 Comment »

The latest issue of American Scientist features some superb reflections by Robert L Dorit on the limitations of reductionist thinking in the biological sciences. They have clear parallels for social sciences and, by extension, for social policy. Selected extracts are below.

Despite Descartes’ contention that we could not distinguish a well-made automaton of an ape from an actual ape (“were there such machines exactly resembling organs and outward form an ape or any other irrational animal, we could have no means of knowing that they were in any respect of a different nature from these animals”), the relations of parts to wholes in living systems is entirely different from that in machines—and most unclocklike. If anything, living systems consistently violate all of the criteria for reducibility. The number of elements that compose any living system—an ecosystem, an organism, an organ or a cell—is enormous. In living systems, the specific identities of these component parts matter. Unlike chemistry, for instance, in which an electron in a lithium atom is identical to an electron in a gold atom, all proteins in a cell are not equivalent or interchangeable. Each protein is the result of its own evolutionary trajectory. We understand and exploit their similarities, but their differences matter to us just as much. Perhaps most importantly, the relations between the components of living systems are complex, context-dependent and weak. In mechanical machines, the conversation taking place between the parts involves clear and unambiguous interactions. These interactions result in simple causes and effects: They are instructions barked down a simple chain of command.

In living systems, by contrast, virtually every interesting bit of biological machinery is embedded in a very large web of weak interactions. And this network of interactions gives rise to a discussion among the parts that is less like a chain of command and more like a complex court intrigue: ambiguous whispers against a noisy and distracting background.

As a result, the same interaction between a regulatory protein and a segment of DNA can lead to different (and sometimes opposite) outcomes depending on which other proteins are present in the vicinity. The firing of a neuron can act to amplify the signal coming from other neurons or act instead to suppress it, based solely on the network in which the neuron is embedded. The disappearance of a single species can stabilize an ecosystem or send it spinning into chaos, depending on (you guessed it) the network of interactions that surrounds that species. This extensive and subtle connectivity, which gives meaning to the behavior of the underlying components, turns out to be a consistent feature of living systems.

The recurrent evolution of these networks of weak interactions suggests that they may allow biological systems to incorporate information from the environment while also maintaining stability in the face of constant perturbation. This general feature of living systems also has clear methodological consequences for modern biology. Once this gossamer web is taken apart in search of the smallest components we can study, the process of putting it back together bears no resemblance at all to reconstructing a clock. Thus we find ourselves, early in the 21st century, with extraordinarily detailed descriptions of the components of many biological systems. But reconstructing those systems is proving to be a monumental and consistently surprising enterprise.

The promise of reductionism rested on the belief that an intelligent dissection of complex phenomena would not only yield progress, but would eventually reduce any problem to its component parts. Complexity, we naively hoped, was simply a by-product of incomplete understanding, an illusion that would fall away once the parts were fully understood. But this is the dirty little secret of contemporary biology: Despite our reductionist successes, the central conceptual problems of biology have not yielded to study. We have revealed the elegant workings of neurons in exquisite detail, but the material understanding of consciousness remains elusive. We have sequenced human genomes in their entirety, but the process that leads from a genome to an organism is still poorly understood. We have captured the intricacies of photosynthesis, and yet the consequences of rising carbon-dioxide levels for the future of the rain forests remain frustratingly hazy. We are, in short, the king’s horses and the king’s men: We stare at the pieces, knowing what Humpty should look like, but unable to put him together again.

How does one deal with such complexity? In living systems, Dorit argues, the component parts are always embedded in an intricate web of interactions. Such complexity appears at multiple levels of organisation as shown in the three illustrations below: a network of interactions among 1,700 different human proteins (top), a food web in a Puerto Rican rain forest (middle) and a neuronal network in a mouse brain (bottom).

Dorit continues:

the days when we could have blind faith in the power of reductionist deconstruction are over. Humpty lies in fragments. Fortunately for us, a new approach is taking shape to replace the seductive appeal of reductionism. We biologists may have bought a little too much of what Descartes had to sell, but as the limits of naive reductionism become more obvious, additional methods emerge for understanding the complexity of life. This new, interactionist perspective on living systems, with its emphasis on the interplay of parts, has benefited from new computational tools and experimental approaches. Whole new subfields in the life sciences, as well as productive interactions among existing disciplines, have emerged. Systems biologists, complexity theorists and newly minted biologists now attend as carefully to the ways in which parts come together as they do to the parts themselves. In the process, features of living systems that we once carelessly overlooked (or destroyed) in our haste to deconstruct now snap into focus. We are, for instance, beginning to understand that modularity and redundancy are inherent features of all levels of biological organization. These features characterize systems that are simultaneously resilient and capable of evolving.

Read the full article here.

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How do you solve a problem like Malaria?

Posted in Biology, Campaigns, Evaluation, Evolution, Healthcare, Innovation, Institutions, Knowledge and learning, Malaria, Public Policy, Strategy, Uncategorized on January 27, 2011 | 9 Comments »

Big thanks to Alanna Shaikh and Bill Brieger for feedback and comments.

Debates about malaria eradication in the aid blogosphere, along with recent scientific evidence, highlight the urgent need to improve our understanding of the complex dynamics of this terrible affliction and to use it to adapt ongoing eradication programmes.

A nearly hopeless case?

According to the WHO, one in every five childhood deaths in Africa is due to the effects of the disease and an African child has on average between 1.6 and 5.4 episodes of malaria fever each year. A child dies every 30 seconds of malaria. The latest estimate from the 2010 World Malaria Report is that in 2009 the disease killed almost 800,000 people and afflicted 225 million others. And while a 2009 global malaria risk map suggests that while risks are worst in Africa, there are  clear indications of dangers in many other countries too.

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Last week Chris Blattman posted a justifiably scathing response to an article in Guernica, which had suggested that attempts to eradicate malaria are ‘nearly hopeless‘, and that current global attempts to do so are doing more harm than good.

Chris put forward an eloquent and moving counter-argument which included the following points (a) disease eradication is one of the few successes of big aid (b) we can’t simply let malaria take its toll and do nothing (c) presenting malaria as a symbol of African honour – as the Guernica article does – is at best inaccurate and misleading and (d) development is the key to successful eradication.

Many (myself included) would agree wholeheartedly. Evidence suggests that the cessation of malaria control programmes can lead to severe epidemics, as in Swaziland in 1984-85, or Madagascar in 1987-88. Shortfalls in ongoing responses have also led to resurgences in Zambia and Rwanda. Much of what is presented in the Guernica article can be dismissed as bizarre, confused or just plain wrong. However, one point made is worth looking at in more detail.

Premature pronouncements and cheap mainstays

The author suggests that current approaches to malaria tend to be narrowly focused on a limited number of technical solutions, or the search for such solutions. Think bednets, drugs or the investment in the development of vaccines.

In fact, this narrowness in the focus of malaria programmes appears to have been a relatively constant feature over the last 40-50 years. In 1969, the Pearson Commission (the source of the ubiquitous 0.7% of GDP aid target for donor countries) pronounced the disease ‘virtually eliminated’. Today it is hard not to see this declaration alongside Chamberlain’s ‘peace for our time’ as one of the most premature statements ever (as well as a little disingenuous from the standpoint of developing countries).

A 2008 Lancet review cited in Malaria Matters tells us more about the failures of the first eradication effort:

[the 1960s eradication campaign] was far too monodimensional, relied too much on DDT [insecticide] spraying, and  neglected the palpable problem that the delivery infrastructure was not  in place in too many parts of the malarious world.”

It goes on:

The emergence of widespread mosquito resistance to DDT, and parasite resistance to the cheap mainstay of therapy compounded the difficulties.” (emphasis added)

In short, narrowness of responses allowed evolutionary dynamics to play out at various levels, changing the efficacy of those responses. This problem has not gone away. As clinical microbiologists Richard Carter and Kamini Mendis see it, for the most part, the types of tools that are available and are used for malaria control today are the same as those which were available during the ‘virtual elimination’ era.

This point does need some nuancing. There was one approved insecticide during the first eradication effort, whereas there are now a dozen. The use of treated nets – which weren’t around in the 1960s – has been responsible for large drops in some countries. But even in those countries there is growing acknowledgement of the need for a better combination of responses to make further progress. ‘Cheap mainstays’ will not do the trick. As noted on Malaria Matters:

The malaria lifecycle is complex, and health systems designed to deliver malaria interventions [are] equally complex (and challenging), which means we cannot and should not expect a magic bullet in the near future.

If we want to better understand the complexity of malaria, a good place to start would be to understand the evolutionary dynamics at play.

Exploring evolutionary dynamics

Resistance to responses – whether among mosquitoes or the parasite itself – has been identified as an evolutionary phenomenon. Biology 101 tells us that all populations of organisms display genetic variation across members which enable some to handle particular environmental stresses and opportunities better than others. Natural selection has been shown to favour the evolution of pathogen populations that can resist the drugs and insecticides in their environments.

As the Lancet article cited above notes, resistance has evolved at two distinct levels. The malaria parasite evolves, developing drug resistance. One team of researchers has found that “Drug development programs exhibit a high attrition rate and parasite resistance to… drugs exacerbate the problem. Strategies that limit the development of resistance and minimize host side-effects are therefore of major importance.”

Specific parasites also adapt at the molecular level, according to the antibodies encountered in the host’s immune system. There is also the prospect of inter-species infections, whereby – for example – parasites mainly responsible for malaria among chimpanzees find ways to adapt to new human hosts, facilitated by greater human penetration of forest environments.

Mosquitoes also evolve to adapt to changing physical environments, human behaviour and pesticides. As described by Bill Brieger on his excellent Malaria Matters blog:

…Resistance to insecticides in [a mosquito sub-species] is receiving increasing attention because it threatens the sustainability of malaria vector control programs in sub-Saharan Africa. An understanding of the molecular mechanisms conferring… resistance gives insight into the processes of evolution of adaptive traits and facilitates the development of simple monitoring tools and novel strategies to restore the efficacy of insecticides…”

There have been numerous calls for more studies into how insects exposed to pesticides undergo strong natural selection and develop various adaptive mechanisms to survive.

Of course, human populations have also have co-evolved with malaria, and developed different kinds of resistance. The protective effects of the sickle cell trait is certainly the best known example, but there are others that have been identified, including genetic variations in the populations of Thailand and New Guinea which prevent against malaria-induced miscarriages. However, humans adapt genetically less quickly than the malaria parasite or the mosquito – waiting for or relying human evolution of resistance (as the Guernica piece seems to imply) is clearly not an adequate fall-back option.

Professor Karen Day, who has studied the historical evolution of malaria, is clear about the importance of this line of inquiry:

…From Ronald Ross’s discovery that malaria is transmitted by mosquitoes came the idea that we could control malaria by impacting the life span of the mosquito. If we can better understand the evolution and diversity of malaria, we may find an Achilles heel in the parasite or new ways to thinking about control….”

Slow take-up, slow scale-up?

However, while there is some basic research attempting to bring an understanding of evolutionary dynamics to the design of better drugs, pesticides, and even vaccines, there are still questions as to whether this knowledge is ready to be applied in programmes and at the necessary scale. The overall global malaria response may still be relatively limited in terms of its repertoire of responses.

For example, a 2009 study notes that the Global Malaria Action Plan (GMAP) of the Roll Back Malaria initiative sought to spray 172 million houses annually, and distribute 730 million insecticide-impregnated bed nets. The study concluded that if this was implemented with existing insecticides, with no acknowledgement of the scope for evolutionary response, the program would create unprecedented opportunities for the development of resistance among mosquitoes, and may also create new variants of mosquitoes.

The World Malaria Report 2010 shows that global efforts to prevent malaria through bednets and sprays reduced cases from 233m in 2000 to 225m in 2009 and 985k deaths in 2000 compared to 781k deaths in 2009. However, tellingly, the statistics also show that several African countries saw a resurgence of the disease – in part because of resistance and changing contextual factors.

Researchers at Maastricht University have argued that a fundamental issue is that much malaria modelling does not take into account evolutionary dynamics. By modelling global malaria as a complex adaptive system, the researchers have been able to review the efficacy of malaria strategies, and were also able to assess the potential implications of climate change.

Overall, their conclusion was that continued changes in human behaviour (such as in agricultural methods or urbanisation, which presents its own set of challenges), as well as human impact on the environment, will mean malaria will continue to evolve and confound current interventions in areas of high prevalence. They also make a complementary point to Professor Karen Day’s – eradication and control strategies that do not take account of these complex evolutionary dynamics may well make things worse, and could ‘substantially exacerbate the significance of malaria in coming decades’.

Some of these fears may be becoming reality. An article published in Science magazine in October 2010 suggested that the mosquito strain that is responsible for most disease transmission is in the process of rapidly evolving into two genetically distinct species. The hypothesis is that the two species are evolving in different directions in reaction to differences in environment and the challenges they face. The Imperial College researchers confirmed fears that this development is likely undermine efforts to control and treat malaria – conventional strategies are unlikely to be effective against both strains.

So what?

Forty years ago, malaria eradication failed at least in part because of a lack of diversity in the mechanisms employed, and the related evolution of resistance. Although global responses are broader than before, there are still questions about whether they are diverse enough, and whether the full breadth of approaches and knowledge are being applied at scale. Narrowness in responses may, in the worst case scenarios, be making human populations more vulnerable to malaria.

This means supporters of eradication and control programmes must continue to fund research that advances an evolutionary understanding and use it to keep ahead of the disease. This makes the levelling off of aid commitments reported in World Malaria Report 2010 all the more worrying, because much hope now lies in more funding for innovative basic and applied research. At least some of this research should start with the premise that the dynamics of malaria requires a rethinking of global efforts, with a special focus on capacity of existing health systems to deliver a broader range of treatments.

In this area, like in so many other aspects of international aid, silver bullets may well be red herrings. But history and recent research suggests that this is not a battle that should be conceded easily. Rather, as Chris Blattman notes, we can take some heart and some lessons from previous eradication programmes.

Smallpox was famously wiped out in the 1970s, with the last case being in Merca, Somalia in 1977. When the eradication was announced in 1980, the campaign was described “a triumph of management, not medicine”. This was an especially unusual pronouncement given it was made by the-then Director-General of the WHO.

But what exactly did this mean? According to one major account, the DG was referring to the emergent process of adaptation and learning – the evolutionary process within the programme itself – which

…more than any other element in the campaign, [was] the key explanatory factor of the ultimate success of the program… ”

What eventually eliminated smallpox was the combined approach of top-down problem-solving—mass vaccination to reduce disease incidence to certain levels —and bottom-up emergent experimental innovations in early detection, isolation and control - to push towards complete eradication.

Of course, smallpox is a very different disease, and may have been a better candidate for eradication than malaria – exactly because of the evolutionary nature of malaria.

But there is an interesting message here: if we want to deal with the evolving problem of malaria, we also need the global response to adapt and evolve, for organisations involved to think and act ‘outside the box’.

It is not clear what this would look like yet, of course – but it is worth noting that the eventual strategy for dealing with smallpox eradication was not originally employed or even envisaged by the implementing organisations.

Whether current efforts are able and willing to take on such an adaptive management mentality remains to be seen.

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An interview on complexity and aid

Posted in Agriculture, Biology, Chaos, Innovation, Organisations, Public Policy on January 6, 2011 | 1 Comment »

…Because of our urgency to end poverty, we act as if development is a construction, a matter of planning and engineering, rather the complex and often opaque set of interactions that we know it to be…

This is a excerpt from a recent interview I gave to Dennis Whittle (former CEO of Global Giving).

Click here to read the interview in full.

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When Can Crowds Outperform Aid Experts?

Posted in Biology, Campaigns, Evaluation, Innovation, Knowledge and learning, Natural disasters, Networks, Organisations, Self organisation, Technology on January 3, 2011 | 5 Comments »

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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