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Why Managers Haven’t Embraced Complexity

Posted in Economics, Facilitation, Innovation, Institutions, Knowledge and learning, Leadership, Research, Strategy, Technology on May 10, 2013 | 3 Comments »

This is a cross-post from the HBR written by Richard Straub, and is one of a series of perspectives that will be published leading up to the fifth annual Global Drucker Forum in November 2013 on the theme of Managing Complexity.

Nobody would deny that the world has become more complex during the past decades. With digitization, the interconnectivity between people and things has jumped by leaps and bounds. Dense networks now define the technical, social, and economic landscape.

I remember well when the idea of applying complexity science to management was first being eagerly discussed in the 1990s. By then, for example, scholars at the University of St. Gallen had developed a management model based on systems thinking. Popular literature propagated the ideas of complexity theory — in particular, the notion of the “butterfly effect” by which a small event in a remote part of the world (like the flap of a butterfly’s wings) could trigger a chain of events that would add up to a disruptive change in the larger system (such as a hurricane). Managers’ eyes were opened to the reality that organizations are not just complicated but complex.

Why did this interest and work in complexity not lead to major changes in management practices? There are, I think, a few major reasons that it didn’t — and that also suggest that the overdue change might now finally take place.

Complexity wasn’t a convenient reality given managers’ desire for control.
The promise of applying complexity science to business has undoubtedly been held up by managers’ reluctance to see the world as it is. Where complexity exists, managers have always created models and mechanisms that wish it away. It is much easier to make decisions with fewer variables and a straightforward understanding of cause-and-effect. Here, the shareholder value philosophy, which determines so much of how our corporations operate these days, is the perfect example. Placing a rigid priority on maximizing shareholder returns makes things clear for decision-makers and relieves them of considering difficult tradeoffs. Of course we know that constantly dialing down expenses and investments to boost short-term margins inevitably damages the long-term health of the company. It takes a complexity approach to keep competing values and priorities and the effects of decisions on all of them in view — and not just for management, but equally for investors, analysts, and regulators.

Technology was not yet powerful enough to capture much complexity.
When systems thinkers and theorists turned their attention to economies and organizations in the 1980s and 90s, the tools simply did not exist to model their workings at a level that would yield practical insight. Now, the exponential increase in computing power and the progress in mathematics and statistics have propelled us into a new era. With the ability to draw on data bases and map networks at scales that were unthinkable before, we can hope to understand communication flows through large organizations, and the impact of disturbances and managerial interventions on these flows.

The prospect of non-human decision-making is unnerving.
More recently, with the surge of computer processing power, another nagging concern has formed in some people’s minds. Does the fact that massive computing power is required for systems-level comprehension mean that the interpretation of information, sense-making, and learning will become “extra-human” activities? Will the computer take over the role of the knowledge worker? Will we soon reach a tipping point when human brainpower is obsolete? Some technophiles (many of them inspired by Ray Kurzweil’s ideas) respond to questions like this with a resounding yes. Yet for most of us it is a disturbing thought, because we have seen so many of the models designed to predict the future state of complex systems (from economies to climates) fall short of accuracy, to say the least.

The eager futurists talking about machines taking over evaluation of situations and decision-making have set back their own cause, as others see them ignoring an essential fact: sense-making is always informed by values. The idea that we might look for value judgments from algorithms is just badly flawed. But fortunately, the recognition is growing that, while computers can provide us with enormous extensions of our storage and processing capacity, they must and will remain only inputs to human brains, where the ultimate evaluation and deliberation must continue to take place. Think of the brain as our own “complexity processor” and itself our most complex organ: It helps us to address complex issues and yet come up with seemingly simple solutions. Those are made possible when we unconsciously see through the myriad of information elements that are stored in our brain as raw material to build meaningful patterns, or the famous “big picture” that humans can develop best.

The recognition of complexity is at its core a view of the world that that makes us more humble and more open. It is the awareness that too often our interventions will not achieve what we wanted and we will be shocked by unintended consequences. (The fact that, following the creation of the Cap-and-Trade Carbon Emission Scheme as a clever new artificial market, more coal is being burned in Europe than before is a mind-boggling example.) At the same time, it is the acknowledgement that simplistic “can do” thinking and linear approaches in organizations and markets, which are by definition complex, won’t be sufficient. And it is the prod to us to better understand why.

There has been no watershed event to make it true that managers will apply complexity science to their work today, whereas they could not, or would not, yesterday. Rather, there has been a gradual change in mindset, pushed along by the increasingly evident damage of narrow, simplistic thinking. The toolkit that allows us to understand the dynamics of large systems has continued to evolve. And the reassuring truth has been reasserted that, on top of the logic of algorithms, human values and judgment are essential.

Managers, I think, should now get ready to face the full complexity of their organizations and economic environments and, if not control them, learn how to intervene with deliberate, positive effect. Embracing complexity will not make their jobs easier, but it is a recognition of reality, and an idea whose time has come.

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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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Questioning innovation

Posted in Accountability, Economics, Evaluation, Evolution, Influence, Innovation, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Reports and Studies, Research, Technology on January 30, 2013 | 5 Comments »

Innovation is popular in aid at the moment, so much so that there is a steady spate of articles which range from trashing its potential contribution to development through to challenging Western, donor, countries’ assumed roles as the ‘providers’ of innovation.

In this post I want to argue that there is a middle  ground between the unthinking mantras that are increasingly peddled by agencies and the growing number of entirely justifiable critiques.

An Economist article made the point succinctly over a decade ago, ‘what precisely constitutes innovation is hard to say, let alone measure.’  Some concluded, as a result, that innovation was a ‘new theology’.

innovation-prayer

However, with a growing chunk of economic growth being driven by industries and products that in fact didn’t exist ten years previously, such dismissals seem increasingly Luddite.

While clarity and precision in thinking about innovation is all-important, it is far from easy. We are not helped by the fact that many innovation stories are in fact apocryphal – retrospectively woven to lend the star protagonists much more agency and awareness than in fact they possessed. This is true of even the best known innovation stories. Take Alexander Fleming’s infamous and much-lauded discovery of penicillin – Fleming himself used to describe the conventional account of his contribution as the ‘Fleming Myth’.

Typically in business the market is the ultimate arbiter of innovation, and as we know, most products fail. In aid, however, the market does not provide an adequate indication of what is successful and what is not. This is largely done by the aid system itself. Bill Easterly made much of the fact that the market could get Harry Potter books anywhere there was a demand, which he compared unfavourably to the inability of the aid system to get simple treatments like vaccines to where they were most needed:

There was no Marshall Plan for Harry Potter, no International financing Facility for books about underage wizards. It is heartbreaking that global society has evolved a highly efficient way to get entertainment to rich adults and children, while it can’t get twelve-cent medicine to dying poor children.”

But as Amartya Sen subsequently argued:

the disparity in the results is indeed heartbreaking… [but] there is a radical difference… between the enterprise of supplying “what is in demand” — which is integrally linked to the buyers’ ability to pay — and that of supplying needed goods and services to people whose income and wealth do not allow a need to be converted into a market demand.

While Sen’s point applies more broadly to aid delivery, it is also relevant to new ideas and innovations within aid.

In any case, whether because of market failure or the wilful self-interest of aid agencies, innovation – which is an ambiguous enough concept in the business realm – becomes very murky territory indeed in development. It is hard to say  what innovation actually is, what it generates, and for whom. Like the famous ruling about pornography, many are of the view that ‘I know it when I see it.’

Such vagueness is the ideal seeding ground for development fads, and indeed, innovation is fast becoming the latest ‘fuzz-word’. Everything is being labelled ‘innovation’: as one blogger memorably put it, we seem to be suffering from Innovation Tourette’s.

Problem

Little wonder that growing numbers of thinkers and writers see the need to beat innovation with a big snarky stick. These criticisms play a vital role in highlighting the risks and downsides of  all shiny new aid agendas – and innovation is no exception.

Having observed such trends in the past, I think there is a danger that between the rise of the fad and the indignant reaction to it, we lose sight and sense of why the issue is question is actually important. Specifically, we risk learning the wrong lessons about what innovation actually is, and the potential it has to add to our work.

What we need is a more precise and accurate way by which to separate the innovation wheat from the faddish chaff. This was in fact one of the key motivations of a study I co-authored with Kim Scriven and Conor Foley while at ALNAP back in 2008-9.

So what did our study suggest in terms of getting more precision in innovation? We found it useful to ask some key questions to identify whether a particular idea or approach was in fact innovative.

  • Q1. Is the idea being proposed a new Product, a new Process or service, a new way of Positioning aid, or a new Paradigm or mental model? Or is it some combination of the four? (see more here)
  • Q2. What are the origins of the idea, and what does it aim to do differently to what is already out there? Where, exactly, is the novelty – is it a whole new thing, or is it new combinations of existing things? What, in partciular, are the implications for relationships with aid recpients? Did the idea involve re-thinking that age-old and much-critiqued relationship?
  • Q3. How disruptive is the innovation? Is it transactional, in that it enables existing efforts to work; incremental in that improves these efforts, or transformational in that it radically changes these efforts?
  • Q4. What precisely are the expected benefits the idea should confer? Can these benefits be framed in terms of existing evaluation criteria of enhanced relevance, efficiency, effectiveness, impact or sustainability of aid? Or are there other, newer, criteria that matter? How can the benefits be measured – qualitatively, quantitatively, or some blend thereof?
  • Q5. What are the potential risks and downsides of the idea for all parties – especially aid recipients – and how will these be mitigated against?
  • Q6. Where can the idea be located in an overarching innovation process? Is it at the early stages of recognition and invention, is it in need of development and implementation, or has it been tested and is now ready for wider diffusion?  (see more here)
  • Q7. What are the networks and relationships that will support and facilitate the innovation process? What capacities and competencies are necessary? Are these in place? How can they be built?
  • Q8. What is the potential scope of the innovation in terms of wider diffusion? Who might benefit, and in what ways? What is the route to scale, and who needs to be engaged to get there?

There are no doubt many more questions that could be asked, but the above provide a good starting point for what might be termed ‘innovation due diligence’. The key, in my opinion, is to use these and others questions to develop more honest, rigorous stories about ongoing and historical  innovations: about how they came about, why, and with what benefits. Such questions are useful because they help us look at innovation from a more systemic perspective: looking not just a idea, but the overall social, technological and institutional context from which it has emerged.

These questions should be relevant whether you are a donor bombarded with new proposals and ideas, an operational aid worker seeking to get funding for your exciting new idea, a blogger wanting to shine a light on the depressing excesses of innovation-speak, or a researcher wanting to investigate an supposed innovation in a systematic fashion (in fact I think we need far more of the last category, but that’s another story).

We will need to keep wielding the big stick as necessary, to curb against such excesses of aid ‘innovation-speak’. But we may also at times need a magnifying glass and ruler – metaphorically speaking – and asking these kinds of questions could help with this. My $0.02 is that if a would-be innovator can’t take a reasonable stab at these questions, they aren’t working hard enough, or they are over-selling something. A lot is spoken about creativity in innovation, but recent work suggests – in echo of the old 1% inspiration, 99% perspiration line – that the larger part of innovation lies in the proper execution of the idea.

I think there is a special role for the aid blogging community in asking such questions and demanding answers. We have seen in the past few years how bloggers have mobilised in a largely self-organised fashion to push back against various poorly considered ideas.

I know there are many bloggers who want to engage with innovation in a serious fashion, and who are dismayed by the current hype surrounding it. We should be able to highlight the good and bad of what we see emerging from the aid innovation agenda. And aid agencies should be willing to open their ideas up to the views and scrutiny of this emerging, globally networked, community of thinkers and analysts. This kind of effort has, in other distributed sectors, developed into new crowd-sourced marketplaces for innovation such as Innocentive. There’s no reason why the same couldn’t happen in our sector.

Our ultimate goal, I’d argue, should be to work to bringing the perspectives of aid recipients into the mix as part of our standard operating procedures. Now that in itself could be seen as a real innovation.

The need for such engagement goes beyond mere niceties. The most effective ideas we uncovered in our 2009 study were precisely those that re-thought and re-formulated this core aid relationship: cash, community approaches to malnutrition, transitional shelter. Put simply, these were the innovations that we found to be most worthy of the term.

Gamechanger

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Technophobia, Technojoy

Posted in Evolution, Innovation, Knowledge and learning, Technology on September 26, 2012 | Leave a Comment »

I’ve come up with a set of rules that describe our reactions to technologies:

1.  Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.

2.  Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.

3.  Anything invented after you’re thirty-five is against the natural order of things.”

Douglas Adams, 2002

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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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What Does The Atlas of Economic Complexity Mean for Development?

Posted in Climate change, Economics, Evolution, Financial crisis, Innovation, Networks, Public Policy, Reports and Studies, Resilience, Technology, Trade on November 3, 2011 | 11 Comments »

Ricardo Hausmann of Harvard and Cesar Hidalgo of MIT (whose work I have blogged about previously here) have just published the deeply impressive Atlas of Economic Complexity. It is built around an innovative, network-based methodology for understanding economies and their potential for  growth. It represents perhaps the most systematic and in-depth application of the ideas and methods of complexity research to issues of development to date. Readers can download the Atlas and experiment with a powerful interactive visualiser here.

Following an interview with Cesar Hidalgo last week, this extended post explores the implications of this important new work.

I. What is the premise of the Atlas?
The basic idea underpinning the Atlas of Economic Complexity is straightforward. As Hausmann notes:

The fundamental proposition… is that the wealth of nations is driven by productive knowledge. Individuals are limited in the things they can effectively know and use in production so the only way a society can hold more knowledge is by distributing different chunks of knowledge to different people. To use the knowledge, these chunks need to be re-aggregated by connecting people through organizations and markets. The complex web of products and markets is the other side of the coin of the accumulating productive knowledge. [emphasis added]

The secret to modernity is that we collectively use large volumes of knowledge, while each one of us holds only a few bits of it. Society functions because its members form webs that allow them to specialize and share their knowledge with others.”

At the heart of the Atlas is the attempt to measure the amount of productive knowledge that each country holds by applying  network analysis techniques to this complex web.

Much standard development – and economic – thinking doesn’t engage very well with the idea of webs and networks. As Hidalgo told me, such ideas run counter to much standard thinking, which seeks to identify differences between individuals and groups based on their inherent qualities – demographic criteria and suchlike. Experts then puzzle over why, for example, communities with the same criteria, or countries with very similar starting points end up with very different development pathways and social and wealth outcomes. It turns out that in many cases, their relationships and networks prove to be a key differentiating factor. If the data is available, it is possible to develop very precise and rigorous analysis of these differences.

II.                   How does the Atlas work?
So how does the Atlas make these ideas relevant to development economics? Well, for starters, it acknowledges that accumulating productive knowledge is difficult: “For the most part, it is not available in books or on the Internet. It is embedded in brains and human networks. It is tacit and hard to transmit and acquire. It comes from years of experience more than from years of schooling. Productive knowledge, therefore, cannot be learned easily like a song or a poem. It requires structural changes. Just like learning a language requires changes in the structure of the brain, developing a new industry requires changes in the patterns of interaction inside an organization or society.”

As readers will be well aware, the social accumulation of productive knowledge has not been universal: “The enormous income gaps between rich and poor nations are an expression of the vast differences in productive knowledge amassed by different nations.”

These differences are expressed in the diversity and sophistication of the things that each nation makes. In order to put knowledge into productive use, societies need to reassemble these distributed products through teams, organisations and markets. These issues are explored in detail in the Atlas, through the concept of the ‘product space’. This is a map which captures the products made by different countries in terms of their knowledge requirements. This maps provide a way of understanding how productive knowledge is accumulated.

As Hidalgo said in interview:

Knowledge doesn’t add up like capital. There is a lot of redundancy in knowledge. Some countries may have diverse knowledge but small populations. Product space is an expression of different kinds of knowledge – and its much better than other indicators.

Cesar’s TED talk from August 2010 gives more information about this idea and how it works.

The underlying notion of this analysis is that the complexity of an economy is related to the range of useful knowledge embedded in it:

For a complex society to exist, and to sustain itself, people who know about design, marketing, finance, technology, human resource management, operations and trade law must be able to interact and combine their knowledge to make products. These same products cannot be made in societies that are missing parts of this capability set. Economic complexity, therefore, is expressed in the composition of a country’s productive output and reflects the structures that emerge to hold and combine knowledge… Increased economic complexity is necessary for a society to be able to hold and use a larger amount of productive knowledge, and we can measure it from the mix of products that countries are able to make.”

III: What does it all mean?

So what does this give us in practical terms? As a starter, representing such a huge amount of data – covering 128 countries, 99% of world trade, 97% of the world GDP and 95% of the world population – in visual form is in itself a remarkable feat. As people like Hans Rosling have powerfully demonstrated, innovations in how we visualise data can yield tremendous new insights and ideas.

Here’s an example of a product space diagram, this one for the United States. To learn more about the diagrams and how to interpret them, I would strongly recommend having a play with the visualiser, then scanning the report, then having another play.

Hausmann, Hidalgo and their team have also developed an Index of Economic Complexity to represent their data systematically. This Index tells us about the richness of the product space of a given country, and by extension, is one useful indicator of the potential to grow. It can also be used to compare economic complexity across countries, as shown in this chart showing the ranking of different countries (from 1 to 128, highest is most red).

The authors acknowledge that these ideas are not always easy to grasp, and provide a useful thought-experiment to help readers get their heads around the implications of the Index.

Think of a particular country and consider a random product. Now, ask yourself the following question: If this country cannot make this product, in how many other countries can this product be made? If the answer is many countries, then this country probably does not have a complex economy. On the other hand, if few other countries are able to make a product that this country cannot make, this would suggest that this is a complex economy.

So for example, Japan and Germany are the two countries with the highest levels of economic complexity and if a good cannot be produced there, the list of other potential countries is likely to be very short. Conversely if a product cannot be made in Mauritania or Sudan, the list of other potential countries is likely to be a long one.

One useful way of understanding the benefits of the Atlas is to think about what the analysis adds to some of the key questions in growth economics. One of the classic comparisons made in the growth literature is between African and East Asian countries – which were at comparable levels of development in the 1950s-1970s, but which are now literally worlds apart.

Hausmann and Hidalgo give their  take on this by comparing the Economic Complexity Index for Ghana and Thailand. The lessons are resonant for aid agencies. Both countries had similar levels of schooling in 1970, and Ghana expanded education more vigorously than Thailand in the subsequent 40 years, supported of course by external assistance and policy recommendations.

Despite this, “Ghana’s economic complexity and income stagnated as it remained an exporter of cocoa, aluminium, fish and forest products. By contrast, between 1970 and 1985 Thailand underwent a massive increase in economic complexity, equivalent to a change of one standard deviation in the Economic Complexity Index. This caused a sustained economic boom in Thailand after 1985. As a consequence, the level of income per capita between Ghana and Thailand has since diverged dramatically.”

The Economic Complexity Index has been shown to be a better predictor of economic growth than a number of other existing development indicators. For example, as reported in the Economist last week, it outstrips the WEF index of competitiveness by a factor of 10 in terms of the accuracy of its predictions. It also outperforms the World Governance Indicators and the standard variable used to measure human capital as predictors of growth.

There are many other rich and varied insights from the work which cannot be covered in detail here. There is also tremendous potential to build on and extend this data and analysis in the future. One of the areas I have been working on recently is on resilience, both as a means of reducing the impact of future crises and disasters, and as a means of securing development gains. This issue is understandably at the forefront of many policymakers’ minds at the moment. The network analysis underpinning the Atlas could be used as a very useful comparative indicator of economic resilience, comparing the sustainability of growth in different countries, and help us think through growth scenarios which might enhance or diminish resilience.

There may also be scope to use this kind of thinking to bring more rigour and realism to problems of industrial reform. Take for example the ubiquitous issue of how we move to low-carbon industrial strategies. It would need more data and analysis, but the product space is clearly a powerful way to start to think about the key issues in a systematic and data driven fashion. There are numerous climate change benchmarks out there but none – as far as I know – employ the kind of network analysis used in the Atlas, and so a key aspect of how industrial economies work is missed out. By understanding better the carbon reliance of a particular countries product space, it is possible to think through the implications – the likely successes and failures – of existing adaptation policies.

IV: In conclusion
Perhaps the most important contribution of the Atlas is the analytical rigour that it brings to the complex and dynamic nature of economic growth, and the ability it gives us to ask new and challenging questions more precisely. Cesar summed it up for me as follows:

What we really want to do is to inspire a new kind of conversation. Our traditional approach to economics has retained measures developed in 1930s and 1940s to deal with the situations and crises we faced back then. We think there should be a new breed of measures – that bring much more precision and resolution, and that mean we don’t continue to build our analysis on the over-simplification of a complex system.

Let’s hope we see more of this way of thinking in development debates. While there will inevitably be a degree of resistance from the old guard, it seems to me that the underlying premise of the report is something that no one could disagree with:

Ultimately, this Atlas views economic development as a social learning process, but one that is rife with pitfalls and dangers. Countries accumulate productive knowledge by developing the capacity to make a larger variety of products of increasing complexity. This process involves trial and error. It is a risky journey in search of the possible.”

Such lessons clearly need to play a much more central role in development policy and practice. Haussman, Hidalgo and their team have done us a real service with this work.

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Whose Paradigm Counts? Guest Post 2 of 2 By Robert Chambers

Posted in Accountability, Evolution, Facilitation, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Strategy, Technology on February 15, 2011 | 10 Comments »

Expanding Paradigms

In my first post in this two part guest series, I presented an account of the contrast between ‘things’ and ‘people’ as it was framed in my 1997 book Whose Reality Counts? and as many people in the development sector still perceive it. As numerous responses – both here and on other fora – have noted, things aren’t as simple as all that.

The binary contrasts of things and people as I saw them back in have some very obvious limitations. In comparing the two columns in the table in my earlier post, there can be the temptation of the Animal Farm syndrome: ‘four legs good, two legs bad’.

Reductionism, on the things side, is for instance often painted as bad, and inclusive systems as good. However, this fundamentally depends on context and purpose. There are examples where the opposite holds true. There are also many useful cross-overs between the paradigms, applying things approaches to people, and vice versa. The binary is blinkered and misses much. It lacks subtlety and nuance. And there are many things that do not fit.  It disguises as much as it reveals.

And yet on so many levels it is appealing precisely because of these shortcomings. It is liable to set up a ‘them and us’. It is liable to place responsibility for failures on the ‘other’. Binary oppositions can harm when they polarise the way we see the world and how we determine what is important.

The emergence and evolution of complexity science – and its multifarious insights and analogies – span the physical world, digital computer technology, biology, ecology, economics and social domains. Besides edge of chaos (the idea which gives its name to this blog) other ideas and ways of thinking and seeing things from complexity science take us into a new realm. These ideas collectively make it easier to appreciate, legitimate and accommodate uncertainty and unpredictability – regardless of whether we are talking about things or people.

Because it finds patterns in many diverse phenomena, complexity science provides us with a means by which to read across and change our understanding of these different paradigms. Nobel laureates have argued that complexity thinking presents us with a new opportunity to bridge the divide between physical and social sciences – the world of things and the world of people. I suspect they might be right.

As a 2008 ODI working paper on complexity sciences (Ramalingam et al, 2008), notes:

scientists and thinkers [have been faced] with opposing paradigms since classical times right up to the present day, e.g. the contrast between the hard rock of Aristotle and the swirling mysticism of Plato, echoed in the differences between the approach of neoclassical economics compared with that of cultural anthropology.”

That paper concludes that complexity theory provides a means of ‘steering a middle ground’ between such polarities.

Among the more striking and relevant of the concepts are non-linearity, adaptive agents, co-evolution, and sensitivity to small differences in starting conditions. The paper cited above is a comprehensive introduction to many of these ideas. These concepts have illuminated and validated the creativity, diversity and unpredictability of the  paradigm of people and processes in combination with things. The world is not just things, nor is it just people. The world is not just physical, biological, social, behavioural, psychological, cognitive – it is all of these at the same time.

Complexity science has thus opened up new ways of seeing and understanding phenomena from an interdisciplinary perspective. It offers a lens to re-think the nature, utility and relevance of our work, wherever on the scientific spectrum we sit.

Bridging Paradigms

Revisiting the paradigms of things and people which I set out in the earlier post, we can now see that our conceptualisation needs to be adapted and allowed to evolve. There are new worlds between the certainties of the ‘things’ paradigm and the ambiguities of the ‘people’ paradigm. In fact, there is some astonishing common ground.

For example, characteristics of new technologies, deriving from the ‘things’ paradigm, are now to be found on the people side. Modern social media and communications technologies differ from the more fixed and more mechanical technologies of the past. Separately, new movements have emerged in participatory methodologies, for example, towards the use of numbers and measurement, leading to quantification of experiences that are normally considered qualitative, including empowerment and social change. Poor people can correct, validate and themselves generate statistics, and these can empower them in their relations with organisations and government. They can bring a rigour to evaluations that is all their own, if we or they are able and willing to facilitate good participatory processes and to be open to the outcomes.

Such paradigm-bridging approaches have a growing legitimacy in scientific endeavours, and need to be brought into the mainstream of development thinking. Participation underlies both the examples above, and this – to my mind – is no coincidence. Participatory methods are increasingly being seen as key to effectively navigate so-called wicked problems and as such play a central role in achieving such paradigmatic win-wins. But participation too needs to evolve in order to accommodate this dynamic new world. The methods we have long facilitated with poor people need to be brought into organisations to improve the quality of dialogue and collective problem solving. Facilitators and facilitation open up a win-win future. And the methods also need to adapt and transform to keep up with, and maximise the benefits of, new technologies.

Importantly, complexity can also contribute to reformulating paradigms themselves – giving us a different way of understanding what a paradigm is. From a complexity perspective, each paradigm is an interconnected and interdependent pattern that coheres through mutually reinforcing elements.

In the light of these reflections – the things versus people paradigms need to be redefined, expanded and re-presented.

Re-thinking paradigms

Today, then, we can see two broad paradigms at work in international development. On the one side are Neo-Newtonian practices – those processes, procedures, roles and behaviour which emphasise standardisation, routines and regularities in response to or assuming predictabilities. On the other side, we can see what I call adaptive pluralism, which demands creativity, invention, improvisation and originality in adapting to and exploiting change.

How these paradigms are presented also needs to shift in the light of understanding of complexity.  The diagrams below build on the definition of paradigms I set out in my first post. They suggest some of the ways in which  different elements of each paradigm are mutually reinforcing.

Elements in a Paradigm of Neo-Newtonian Practice

alt

Elements in a Paradigm of Adaptive Pluralism

alt

A whole book could be written in elaboration, justification and criticism of these representations. The details and contrasts are, to mix metaphors, flying kites, going out on a limb, and provoking bulls with red rags.

Readers can judge whether the kites fly, the limb breaks, or any bulls are provoked; and will I hope accept my challenge and invitation to do better. Through a plurality of ideas we may get closer to what will make sense of our rapidly changing world.

There are two points to make now. First, it is not an either-or. These ways of thinking about the world need to co-exist in a much healthier manner than they do currently. Rosalind Eyben has written about how the formal, reductionist side of the aid system often overlays the adaptive side of the system, resulting in cognitive dissonance. It must be possible to get a better, more honest, and realistic, balance between the two.

Second, and to build on this, establishing a better balance needs to be grounded in the challenges we face right now, otherwise it is likely to be abstract and meaningless. Let me ask for suggestions of approaches, things we know that can be done better, where we might attempt paradigmatic win-wins. Maybe it is about furthering the results agenda through participation and local ownership. Perhaps it is developing more socially grounded alternatives to the logical framework. Maybe it is about how large databases and social networks can be developed in tandem in order to enhance aid transparency. Perhaps it is about how uncertainty and context can be better addressed within planning frameworks of aid bureaucracies. In the wider world, areas come to mind where bridging the paradigms may be increasingly essential: climate change, urbanisation, HIV-AIDS, and the link between farming and animal health are some immediate thoughts. I am sure you will have your own examples.

However, here my own prejudices have to come to the fore. There is little doubt in my mind that the neo-Newtonian paradigm has become more and more dominant in development action, if not development thinking. It exerts a powerful influence – for better or for worse – on the way much of the system works. For balance, we need a countervailing pull. For the paradigmatic win-wins which I touched upon earlier to be recognised and acted upon, we need to understand better how adaptive pluralism can add value to development efforts, and how it can be accorded the status it deserves.

In general terms, we must each start by looking to our own paradigms and see what scope there is for challenging how we personally think about, approach and deal with a given problem. Do we feel our own paradigmatic preferences imposing themselves onto a situation before we have even fully understood it? Do we react strongly to a certain way in which development problems are framed and communicated? Is there a ‘them’ in our own organisation to whom we should be speaking but don’t because ‘they don’t see things the way we do?’ If we can answer yes to any of these things, we each have a good place to start.

Conclusion

I hope you have enjoyed this two part series as much as I enjoyed writing it and engaging with Ben in discussions around it (the extent of which really make us co-constructors and co-authors). I hope that at the very least, it will have triggered thoughts and provoked disagreements. I would appreciate reactions and reflections – not least on whether the concept of these paradigms works, whether their treatment makes sense to others, and suggestions for refinements or other articulations.

Let me conclude with a point which relates directly to the readers of this and other blogs. Email, internet, search engines like Google, mobile phones, Web 2.0, blogging, YouTube, Facebook, Twitter….these have already created a culture and practice of continuous adopting, de-adopting, adapting, learning and changing - demanding alertness, nimbleness and creativity. It has even led to adapting and revising this Guest Post series on the fly.

In this emergent aid blogosphere, we as people are all engaged in adaptive pluralism, using physical technologies that are built on neo-Newtonian principles. As such we are reminded daily that the world is things and people, all at the same time.

Let me hope that we in the development community can use the potential of this new space to explore new possibilities, to challenge ourselves and to push beyond our existing mindsets and attitudes, and to do so in ways which create more paradigmatic win-wins.

Our guiding philosophy can be a simple one – in words of the migrant worker, social philosopher and writer Eric Hoffer (pers. comm. Ruth Meinzen-Dick 2004):

In times of change, learners will inherit the earth, while the learned will find themselves well-equipped to deal with a world that no longer exists.

And one more, from Tom Stoppard:

It is the very best time to be alive, when almost everything you believed is wrong.

I am grateful to Ben Ramalingam for the idea of this blog, for co-constructing it, and for contributions to the adaptive revisions of an earlier draft.

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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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