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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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The Aid Explorer: Mapping the Hyperspace of International Aid

Posted in Accountability, Evaluation, Facilitation, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Reports and Studies, Research, Self organisation on May 2, 2013 | 1 Comment »

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

As they put it:

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

Some specific pointers:

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

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

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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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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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Taming fragility?

Posted in Conflict and peace building, Evolution, Influence, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Strategy on March 12, 2012 | 6 Comments »

This is a guest post by Frauke de Weijer (pictured), policy and fragile states specialist at the excellent ECDPM think tank. 

In a previous post on this blog, Ben explored the potential of complex systems research for thinking about statebuilding and fragility.

In this guest post, I would like to take this discussion one step further by asking what the specific implications are for development policy and practice if we start treating fragility as a wicked problem.

Since I came across the term ‘wicked problems’ a few years ago, I have been convinced that state fragility can indeed be described as a wicked problem. The trick with wicked problems is that they are actually a set of problems (or messes, as Russell Ackoff would describe them), some of which are more technical (or tame) in nature and others are wicked again.

Our tendency, in the development world, is to treat them all as technical; i.e. as problems to which the solutions are already known and simply need to be applied. This is what contributes to the consistent failure in addressing state fragility.

This is not to say that applying a different approach, i.e. a ‘complexity theory approach’, will fix the problem. Wicked problems are not particularly ‘fixable’, which is exactly why they are wicked in the first place! What it means is that we have to start from the premise that we do not know the solutions and that we have to discover those solutions as we go along. This is also what Ben speaks about when he says to ‘avoid silver bullet strategies and attempt multiple parallel experiments’.

How to apply these ideas in practice? Fragile states should not be seen as playing grounds for experimentation, especially not for the international community. Yet, in many instances it is possible to test out different ideas; create the conditions for different endogenous solutions to come about; to allow for learning to flow and for strategies to be continuously adapted to the emerging insights of what it would take for a complex social system to change. The key lies in creating feedback loops and learning systems, something the international development community is notoriously bad at.

In a separate article on ECDPM’s Talking Points blog, I have made a further attempt to translate some of the principles stemming from complexity theory into actual practice in fragile states. In my mind, a number of starting points can be described:

1) We have to start from the premise that we do not understand the complexity and interconnectedness within a social system and that we do not know what the solutions are.

2) New ways forward need to be found through ‘wrestling the problems to the ground’; i.e. by enabling local actors to identify potential solutions, test these, and learn from these.

3) Societal change is painful, takes time, is unpredictable and does not follow well-established paths. For external actors engaging in such settings, conflict-sensitivity is key, but the principle of doing no harm is naïve. It is a matter of mitigating these risks to the best of our ability.

4) In rare cases does the national development strategy reflect a genuine consensus of the people, and ownership is often limited to a small group. This raises questions on whether the principle of alignment with national government strategies can be maintained as a self-evident choice.

5) Long-term engagement and having an over-the-horizon strategic vision is essential in fragile states. However, as long as international development continues to work on the basis of current management models, its impact on fragile states will remain limited.

6) For a new approach to fragility to emerge, the policy making and operational systems in use in development cooperation need to undergo fundamental change. It means going beyond a mentality in which experts know the solutions, and putting ‘learning systems’ at the center of development policy.

I elaborate on these principles in a new article on ECDPM’s Talking Points blog website. Do take a look and share any thoughts there.

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Official Views, Doing the Wrong Thing ‘Righter’, and the World Bank Presidency

Posted in Accountability, Evaluation, Influence, Innovation, Knowledge and learning, Leadership, Public Policy, Strategy on March 5, 2012 | 14 Comments »

There’s been a lot of interest in the imminent vacancy of World Bank President, with numerous suggestions of qualified individuals who should be on the list. This post looks at one particular aspect of the role which seems to be missing from most of this debate, and which should be high on the list of criteria for a successful future leader of the Bank.

I: The Official Views of ‘Development Churches’

David Ellerman, currently a visiting scholar at the University of California in Riverside, and World Bank Staffer for over a decade (where his roles including being senior advisor to the Chief Economist Joseph Stiglitz), is one of the most original and innovative thinkers in development. In 2000 he published a paper entitled ‘Must the World Bank have Official Views?’ in which he argued that the Bank spent a lot of time and effort determining its Official Views on particular development issues, and that this practice undermined in efforts in a number of ways.

Specifically:

  • it impedes the open contesting of adverse opinions that is so crucial to the advancement of knowledge
  • it impedes the Bank as a learning organization since the overturning of an older view is all the more difficult if it has been branded and enshrined as an Official View
  • it impedes client countries being intellectually in the driver’s seat as they will inevitably be encouraged in a multitude of ways to accept an opinion because it is an Official View

(The fact that Ellerman wrote and published this while still at the Bank gives some indication of his intellectual courage – I don’t know the backstory so cannot say what impact this had on him personally or professionally.)

Ellerman expanded on this in a subsequent paper for Development in Practice Journal. Through its focus on Official Views, the World Bank and other aid agencies become, in effect, ‘Development Churches’:

“…giving definitive ex cathedra ‘official views’ on the substantive and controversial questions of development. As with the dogmas of a Church, the brand name of the organisation is invested with its views….”

Ellerman argues that in the face of these Official Views, adverse opinions and critical reasoning tend to give way to authority, rules and bureaucratic reasoning shaped by the hierarchies within the organisation. Moreover, these Official Views “short-circuit” and bypass the active learning capability of national and local actors, and substitute the authority of external agencies in its place.

…Once an ‘Official View’ has been adopted, then to question it is to attack the agency itself and the value of its franchise. As a result, new learning at the expense of established Official Views is not encouraged…”

II: Moving Away From Doing the Wrong Thing Righter

The conclusions of a recent, still draft study on the World Bank’s efforts in participatory development indicates that the issues Ellerman highlighted are still an issue within the agency:

Project structures need to change to allow for flexible, long-term engagement. Projects need to be informed more seriously by carefully done political and social analyses, in addition to the usual economic analysis, so that both project design and expected outcomes can be adapted to deal with the specific challenges posed by country or regional context… Most importantly, there needs to be a tolerance for honest feedback to facilitate learning, instead of a tendency to rush to judgment coupled with a pervasive fear of failure. The complexity of development requires, if anything, a higher tolerance for failure. This requires a change in the mindset of management and clear incentives for project team leaders to investigate what does and does not work in their projects and to report on it (emphasis added)

This general phenomena is not unique to aid agencies, of course. The late great Russell Ackoff, a systems thinking pioneer, used to argue that almost every problem confronting our society is a result of the fact that our public policy makers are doing the wrong things and are trying to do them ‘righter’.

The righter we do the wrong thing, the wronger we become. When we make a mistake doing the wrong thing and correct it, we become wronger. When we make a mistake doing the right thing and correct it, we become righter. Therefore, it is better to do the right thing wrong than the wrong thing right.

Back in 2000, David Ellerman suggested a way of overcoming the addiction to Official Views, which involves presenting the following message to client countries, and then acting upon it:

“…To the best of our accumulated experience (which we deem to call “knowledge”), here is what works best in countries like yours. Why don’t you study these principles together with their corroboration to date, take a look at these case studies, contact these people who designed those reforms, set up horizontal learning programs with those best practice cases, and try some experiments to see what
works in your own country? After carrying out this learning process on your own, you might call us back if you feel we could help…”

III: Implications for World Bank Presidency Candidates: A Simple Questionnaire

Building on all of this, we might view the current candidates for the World Bank Presidency in a different, and hopefully useful, light. We need someone who can take Ellerman’s message and Ackoff’s philosophy make them part and parcel of the way the organisation works.

To test candidates suitability in this regard, we might sensibly ask them, and those who know of them, the following yes / no questions (which should take no more than fifteen minutes of their time).

  1. Does the candidates track record indicate they have the ability to be a leader who facilitates as well as one who directs?
  2. Is the candidate able to let go of the notion of selling to, or controlling, others using a set of predefined strategies and results? (Can they effectively manage the uncertainty and ambiguity that ensues?)
  3. Does the candidate instinctively seek out challenges to their institution’s  ideas and policies, and see their leadership role as catalysing ‘mutual learning’? (Does the candidate routinely present their viewpoints as ‘permanently provisional’ and ‘up for debate’?)
  4. Can they respectfully but purposefully elicit the insights, creativity, and wisdom from others? (Can they do this even when others disagree with them?)
  5. Can they encourage multi-stakeholder dialogue and debate as a route to experimentation and innovation?
  6. Are they courageous enough to say ‘I was wrong’, and enable their learning process to be public, to allow others in their organisation and more widely to follow suit? (Are they willing to hear about, and learn from, failure, even in high-profile programmes?)
  7. Can their leadership help diverse groups and constituencies accomplish the results that they want? (Are they willing to share the credit for successes with others?)

I would tentatively suggest that if we have ‘yes’ responses against most of these questions for a given candidate, we could be looking at a genuinely interesting appointment.

If we have mostly ‘no’s, then we should all get ready for another term or two of Official Views, and all that goes with them.

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Facebook, social media and the complexity of influence

Posted in Campaigns, Communications, Evaluation, Influence, Knowledge and learning, Leadership, Networks, Strategy on February 20, 2012 | 5 Comments »

Influence is a complex process in the development sector. We have known this for some time – the work of the RAPID programme at ODI on understanding how evidence influences policy is very clear on this. But the wider socio-economic system within which development cooperation is embedded is no less difficult to influence.  Many corporations, especially in new media, are turning to complexity and evolutionary sciences as a means of measuring influence. But there is considerable potential for misuse and abuse, as illustrated by a new report on Facebook’s contribution to the European economy, and a recent critique of the latest wave of social media analysis firms.

I: Facebook’s Impact on Europe

In a report published in January 2012, Facebook asked the global business advisory firm, Deloitte, to estimate the benefits it generated for the European economy. The report findings make intriguing reading for anyone with an interest in performance, accountability and transparency.

Deloitte’s analysis looked at the direct economic impact of Facebook – such as paying tax, profits and wages. These are the so-called ‘narrow economic effects’ of on-site activities. These impacts are seen as Facebook’s value added, and are described as ‘analogous to contribution to GDP’.  Facebook also has a series of what Deloitte calls ‘ecosystem effects’ – namely, how Facebook enables other businesses to ‘reach customers, make sales, create and monetise apps and even boost demand for products such as broadband and smartphones.’ These ‘broad economic effects’ which result from the Facebook ecosystem give us a measure of Facebook’s influence. The diagram below sets the model out in more detail.

Facebook’s ‘narrow effects’ suggest that it has a narrow impact of €214m and supports 3,200 jobs. But its broad effects are considerably more. For Europe as a whole, the economic impact of Facebook was estimated as just over €15bn in revenues, supporting 229,000 jobs.

By far the largest part of this broader impact was seen as ‘the impact on business participation, where Facebook enables other businesses to advertise, promote their brand, raise awareness and therefore generate new sales… much of this effect is associated with the brand value created for organisations through the social links prevalent on Facebook and the new ways of engendering loyalty and interest that Facebook provides.’ [emphasis added]

Facebook Chief Operating Officer Sheryl Sandberg had this to say when launching the report:

Today’s report shows that Facebook is about a lot more than sharing pictures or keeping up with friends. Increasingly, social media means growth and jobs. Social media is proving particularly valuable for small- and medium-sized businesses, which form the backbone of the European economy.”

These are strong statements, and certainly in keeping with the ‘Facebook boom’ narrative that is so prominent in the media at the moment. In a time of economic gloom, Facebook seems to be one of the few glimmers of hope.

II: Spurious reasoning, dodgy numbers

However, Deloitte seems rather more measured than Sandberg. While clearly happy to put their name to and launch the report, the preamble to the report qualifies this in the following passage:

As set out in the contract, the scope of our work has been limited by the time, information and explanations made available to us. The information contained in this report has been obtained from Facebook Inc and third party sources… Deloitte has neither sought to corroborate this information nor to review its overall reasonableness… no responsibility or liability is or will be accepted by or on behalf of Deloitte… or any other person as to the accuracy, completeness or correctness of the information in this document or any oral information made available… [emphases added]

How about that for an object lesson in distancing oneself from ones work? But if we look at the detail of the analysis, we can start to see that the ‘ecosystem valuation’ is based on some very sketchy assumptions.

For example, Deloitte attributes €6.6bn of the €15.3bn European-wide economic impact of Facebook to “brand value”. This is based on the attribution of a fixed cash value of a Facebook fan of a particular product (€4.69), taken together with the total number of fans (4.2 billion), with some downward adjustments. While all of the numbers used are based on other studies, the overall calculation and final figures still seem fantastically overblown.

The report also suggests that Facebook contributes €5.5 billion to the European economy by generating technology sales. €0.4 billion of this is down to additional device sales, and the rest is seen as broadband. In effect, the report is saying that large numbers of Europeans are buying devices and signing up for broadband just to – or mostly to – use Facebook. Again, this is a claim that would be very hard to substantiate. These two figures alone make up €12.1 billion of the stated impact €15.3bn of Facebook.

III: Social media hyperbole

This is a particular form of social media spin, and is part of a wider movement described by Philip Sheldrake in the Guardian last week. Sheldrake argues that a whole spate of social media organisations are using and abusing the tools and ideas of complexity science in order to demonstrate their influence, all with an air of scientific credibility.

The rest of this post draws extensively from Sheldrake’s critique. He begins by describing what influence is:

You have been influenced when you think something you wouldn’t otherwise have thought, or do something you wouldn’t otherwise have done. …ultimately no one wants to communicate without influence; that wouldn’t be a good use of the communicator’s time and energy, or indeed that of those on the receiving end. The focus on making sure you’re influenced back is vital too… Individuals (and organisations) that best absorb the zeitgeist are heuristically more able to respond in ways their audiences (stakeholders) might well appreciate…

But things aren’t all that straightforward, and he turns to complexity science to show why:

Complexity is the phenomena that emerge from a collection of interacting objects. The interacting objects could be molecules of air and the phenomenon the weather. It could be vehicles and the phenomenon the traffic. Human objects could be the population of Cairo, the 99%, sports fans in a sports stadium, people who like photos of cats, your customers, or your employees; in fact, any collection of people interacting with each other, influencing each other. A characteristic of complexity is that studying the individual rarely betrays anything about the phenomena. You can’t learn much about the termite mound by studying the individual termite or the traffic jam by studying the car.

Sheldrake then relates the ideas of complexity science to the phenomenon of influence:

Take almost any of your recent thoughts or actions and try and decipher how in fact that thought or action came to be; what did you take into account, consciously and unconsciously, over what timescale? You soon begin to appreciate that your thoughts and actions are outputs of a complex system. You are reconciling multiple inputs, multiple influences.

The article points out that companies such as Klout, PeerIndex and PeopleBrowsr all claim to provide systematic insights into individual influence, using ideas of complex systems (specifically social network analysis). This is problematic, however:

In my opinion, complexity and network science will continue to unearth insights of important commercial and societal value, but I’m considerably less enamoured about seeming to translate today’s analytical capabilities into some kind of a score of an individual’s influence. Right now, we have no scalable facility to ascertain or infer who or what caused someone to change their mind or behaviour, without falling into some kind of last-click attribution trap, so how then can we pretend to score an individual’s likelihood to exert that influence, and as if they did so with apparent Newtonian simplicity? We’ve barely even attempted to correlate proxies for influence, assuming that universal correlates even exist. Today, these scores are apportioned in such naive fashion that your so-called influence changes following a fortnight offline.

IV: Navigate complexity, don’t ignore it

This seems to be precisely the kind of thinking that can be seen as underpinning the ‘Facebook ecosystem’. On this basis, we might say that the Deloitte analysis was weak not merely because they did not seek ‘to corroborate this information nor to review its overall reasonableness’. It also falls into the trap of attributing benefits to Facebook in far too simplistic and straightforward a manner, through over-use of the metaphor of  ‘ecosystem’. To cite Sheldrake again:

Perhaps these companies attempt a measure at online popularity, or perhaps online authority, or more exactly the likelihood to have one’s online output shared/forwarded, but not one’s influence. Nor indeed one’s trustworthiness.

Sheldrake also cites Duncan Watts, noted network expert, who has argued against such applications of network and complexity science:

Influentials don’t govern person-to-person communication. We all do. If society is ready to embrace a trend, almost anyone can start one – and if it isn’t, then almost no one can.

This is not great news for Facebook and other social marketeers: ‘many [of whom] have claimed to be able to identify the influentials, get to know them, and influence them. They are effectively claiming to be the influencer of influencers, a sort of influencer-in-chief if you like.’

Sheldrake closes with a message for marketing and PR consultants that is equally pertinent for development and humanitarian agencies seeking to demonstrate their influence:

However, successful [organisations] of the 21st-century will avoid such simplistic thinking, such hyperbole, and recognise complexity and navigate it appropriately.’ (emphasis added)

Facebook and other firms who are well advanced in their use of complexity science ideas should be paying careful attention to Sheldrakes’ assessment. Those of us in the development sector – despite being at much earlier stages in both our efforts to use complexity science and analyse influence – would perhaps also do well to take note.

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