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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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Can we rely on our current understanding of the economy?

Posted in Economics, Evolution, Financial crisis, Innovation, Knowledge and learning, Networks, Public Policy, Resilience, Self organisation, Traffic on April 25, 2013 | Leave a Comment »

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

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Perspectives on a Hyperconnected World

Posted in Communications, Conflict and peace building, Economics, Evolution, Financial crisis, Innovation, Networks, Public Policy, Reports and Studies, Research on February 6, 2013 | Leave a Comment »

Many of the grand challenges that confront humanity—problems as diverse as climate change, the stability of markets, the availability of energy and resources, poverty and conflict—often seem to entail impenetrable webs of cause and effect.But these problems are not necessarily impenetrable. Powerful new tools have given scientists a better understanding of complexity. Instead of looking at a system in isolation, complexity scientists step back and look at how the many parts interact to form a coherent whole.

Rather than looking at a particular species of fish, for example, they look at how fish interact with other species in its ecosystem. Rather than looking at a financial instrument, they look at how the instrument interacts in the larger scheme of global markets. Rather than think about poverty, they might look at how income relates to conflict, politics and the availability of water. Whatever the object of study happens to be, complexity scientists assemble data, search for patterns and regularities, and build models to understand the dynamics and organization of the system. They step back from the parts and look at the whole.

This kind of thinking is a major departure from traditional science. For centuries, scientists have worked by reducing the object of study down to its constituent components. Complexity science, by contrast, provides a complementary perspective by seeking to understand systems as interacting elements that form, change, and evolve over time.The multiplicity of ideas, concepts, techniques and approaches embodied by the science of complexity can be applied to people, organizations and society as a whole, from economies and companies to epidemics and the environment.

The aim of this paper is to raise awareness about this new science and its ability to bring clarity and insight to many of the complex problems the world faces today.

This is from a new short (8 page) paper from the World Economic Forum Global Agenda Council on Complex Systems.

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

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

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

Sergio

Andy

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

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

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

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

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

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

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

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

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

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

This is what we found:

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

Our analysis generated 5 clusters as follows:

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

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

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

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

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

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

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

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

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

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

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

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

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

About the guest post authors:

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

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

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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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South Africa’s democracy: Complexity theory in action

Posted in Accountability, Economics, Evolution, Innovation, Institutions, Leadership, Public Policy, Resilience, Strategy on December 16, 2011 | 4 Comments »

Dr Brian Levy is a Public Sector Governance Advisor at the World Bank, andused to head up the unit responsible implementing the Bank’s governance and anti-corruption strategy.

In this guest post, cross-posted from here, he explores the relevance of complexity theory insights for South Africa. A fascinating read.

The edge of chaos is the balance point where the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either…The edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive and alive…”
M. Mitchell Waldrop, Complexity.

We human beings do not like uncertainty. We seek to understand what events portend, taking comfort in coming up with an answer. So whenever something new in South Africa makes headlines – and that’s been happening quite often recently, the news not always happy – people ask me, “What do you think? Is the political settlement falling apart?”. And always, the expectation is for a definitive answer, one way or the other.

Yet sometimes there is more wisdom, and more comfort to be taken, in acknowledging a more humbling truth – that which of many alternative futures (including ones we cannot imagine) will come to pass is unknowable, is a product of decisions and actions that have not yet been made. This understanding of change as something ‘emergent’, evolving, which can unfold in far-reaching yet ex ante unpredictable directions, is the key insight of ‘complexity theory’ – an insight which can offer a useful dose of humility to governance prognosticators.

In a previous post, I described South Africa’s abiding tension between democracy and inequality. As that post suggested, one of the results of that tension could be a rise of patronage, personalized decision-making as to how to share the ‘spoils’ of power. Indeed, the strains on the country’s constitutional democracy are visible in the drumbeat of daily headlines, as I found when I recently spent some time in the country. Consider the following:

  • Over the past year, the country’s estimable Public Protector, Thuli Madonsela, investigated and made public compelling evidence of corruption on the part of two sitting cabinet ministers, and the chief of police. For many months, President Zuma did nothing – until October 24th  2011, when he unexpectedly fired the cabinet ministers, suspended the police chief, and announced the appointment of a judicial commission to re-investigate long festering allegations of corruption (including against himself) in a weapons procurement deal.
  • Then there’s the populist demagoguery of the leader of the ruling African National Congress’s Youth League, Julius Malema, whose political star, and polarizing influence on South Africa’s political discourse, seemed to be rising – until a disciplinary committee of the ANC announced on November 10th, 2011 that it had found Malema guilty of bringing the ANC into disrepute, and suspended him from the party for five years.
  • There’s the China connection. Across Africa, a context is raging between China and Africa’s erstwhile Western colonial (and post-colonial) hegemonies – for hearts, minds, natural resources, and business more generally. With its sophisticated institutions, and proud transition to constitutional democracy, it might be thought that South Africa would be unambiguously in the Western camp. So against that backdrop, the failure to grant the global human rights icon, the Dalai Lama, a visa in time to attend the October 2011 80th birthday celebration of his friend, Archbishop Desmond Tutu, is startling – and can best be explained by a desire to curry favor with the Chinese.
  • Finally, there’s the on-again, off-again – but then on again – Protection of Information Bill which (after numerous delays, and promises of consultation before finalization) was rushed through South Africa’s parliament on 22nd November 2011. Passage of the bill was seen as a catastrophe by many in South Africa’s civil rights community. Indeed, it signals a startling turn away from a post-apartheid commitment to openness. But no less an observer than The Economist, noting that the bill had been ‘vastly improved’ over earlier versions, suggested that some of the doomsday criticism ‘may be over the top’. And, in light of the ANC’s hitherto ironclad hold on the loyalties of its core constituencies, it is surely no small matter that the Congress of South African Trade Unions (a stalwart part of the ANC alliance) has threatened to take the bill to the Constitutional Court if it is not amended to incorporate a ‘public interest’ exception for whistleblowers.

In the face of all of these cross-currents, any claims of certainty as to where this interplay of forces will lead is surely illusory. Each of them could turn out to be a salutary example of checks and balances in action – of how democratic institutions can keep a country on track, even in the face of pressures to the contrary. But they could also be portents of a future that will become increasingly challenging for the country’s constitutional order.

But in South Africa’s case, and surely elsewhere, too.   perhaps the lack of certainty is a source of comfort. Knowing that the future is not preordained can free us from endless preoccupation with what is inherently unknowable, enabling us instead to direct energy towards action, in service of a hopeful future that we can, perhaps, yet help to create.

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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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Success, failure and what lies between

Posted in Accountability, Economics, Evolution, Innovation, Institutions, Knowledge and learning, Leadership, Meetings, Public Policy, Strategy on June 17, 2011 | 1 Comment »

Earlier this week Tim Harford, also known as the Undercover Economist, gave a fantastic talk at ODI on the topic of ‘Development as Trial and Error’. Drawing on his latest book, Adapt: Why Success Always Starts with Failure, Tim provided the audience with a compelling account of the need for a different way of thinking about and navigating complex problems. (click here and scroll down for videos of the talk and
subsequent discussion)

One of his starting points was to highlight the considerable complexity of the economy, drawing on the work of Eric Beinhocker. This compared the ‘product space’ in different societies – the number of distinct products that are on offer. In hunter gatherer societies, it amounts to some 300 products. In the average Wal-Mart, it is 100,000. In a city like New York or London, it is 10 billion. (you can see Eric’s explanation of this in his presentation at a UKCDS workshop in May)

Tim used this concept, and a story about an unusual project to build a toaster from scratch, to argue that this kind of complexity can most effectively be navigated through evolutionary principles of ‘variation, selection and amplification’ that enable lots of small ‘trial and error’ experiments to be aggregated as effectively as possible.

The market was given as one example of such an aggregator, but there are obvious shortcomings. It has worked for some kinds of problems – Western affluence, most notably. But there are other problems where the market has failed, or where its influence has been far from desirable. Other examples include the scientific enterprise, consisting of diverse means of submitting and peer review of new ideas and experiments. But of course the scientific establishment is also prone to conservatism and inhibiting innovation.

So what can we say about situations where the market is not working or cannot work, where peer review mechanisms are not in place, and where tolerance of failure is impossibly high? The public sector was the example that everyone kept coming back to. How do you get the process of trial and error working in such settings? There are numerous ways Tim touched upon, including innovation prizes, creating artificial marketplaces, finding ways of supporting entrepreneurs, and so on. This is a great article he wrote for Slate, drawing from the book, on exactly this topic. As he puts it there:

Here’s the thing about failure in innovation: It’s a price worth paying. We don’t expect every lottery ticket to pay a prize, but if we want any chance of winning that prize, then we buy a ticket. In the statistical jargon, the pattern of innovative returns is heavily skewed to the upside; that means a lot of small failures and a few gigantic successes.

The paradox at the heart of the need to be more systematic (i.e. controlled,
managerial) about innovation (i.e. disruptive, entrepreneurial) is one that is
not easily overcome. Tim’s remarkable achievement in Adapt is in his clear and
eloquent articulation of this challenge, along some excellent accounts of how it
has been addressed. Let’s hope we in development and humanitarian aid are able to take his ideas and lessons on board.

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