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.
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.
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.
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.
A continuing theme on this blog has been the issue of leadership. Many reports and studies call for it, reforms are seen as impossible without it, critical challenges will not be met without it, and we are all ready to point out the lack of it (in others, at least).
Despite the fact that leadership is one of the most researched topics in management literature (or perhaps becauseof this fact) , our understanding of leadership remains vague and ambiguous.
A blog on HBR by Duncan Watts of Yahoo’s Human Social Dynamics research group eloquently explores the concept and ideas with reference to recent social movements.
The Occupy Wall Street movement has both perplexed and frustrated observers and analysts by its persistent refusal to nominate an identifiable leadership who can in turn articulate a coherent agenda. What is the point, these critics wonder, of a movement that can’t figure out where it’s trying to go, and how can it get there without anyone to lead it?
It’s a reasonable question, but it says at least as much about what we want from our social movements as it does about the way movements actually succeed.
Typically, the way we think of social change is some variant of the “great man” theory of history: that remarkable events are driven by correspondingly remarkable individuals whose vision and leadership inspire and coordinate the actions of the many. Sometimes these individuals occupy traditional roles of leadership, like presidents, CEOs, or generals, while at other times they emerge from the rank and file; but regardless of where they come from, their presence is necessary for real social change to begin. As Margaret Meade is supposed to have said: “Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”
It’s an inspiring idea, but over 100 years ago in his early classic of social psychology, “The Crowd,” the French social critic Gustave LeBon, argued that the role of the leader was more subtle and indirect. According to LeBon, it was the crowd, not the princes and generals, that had become the driving force of social change. Leaders still mattered, but it wasn’t because they themselves put their shoulders to the wheel of history; rather it was because they were quick to recognize the forces at work and adept at placing themselves in the forefront.
Even before LeBon, no less an observer of history than Tolstoy presented an even more jaundiced view of the great man theory. In a celebrated essay on Tolstoy’s War and Peace, the philosopher Isaiah Berlin summed up Tolstoy’s central insight this way: “the higher the soldiers or statesmen are in the pyramid of authority, the farther they must be from its base, which consists of those ordinary men and women whose lives are the actual stuff of history; and, consequently, the smaller the effect of the words and acts of such remote personages, despite all their theoretical authority, upon that history.” According to Tolstoy, in other words, the accounts of historians are borderline fabrications, glossing over the vast majority of what actually happens in favor of a convenient storyline focused on the skill and leadership of the great generals.
Thinkers like Le Bon and Tolstoy and Berlin therefore lead us to a radically alternative hypothesis of social change: that successful movements succeed for reasons other than the presence of a great leader, who is as much a consequence of the movement’s success as its cause. Explanations of historically important events that focus on the actions of a special few therefore misunderstand their true causes, which are invariably complex and often depend on the actions of a great many individuals whose names are lost to history.
Interestingly, in the natural world we don’t find this sort of explanation controversial. When we hear that a raging forest fire has consumed millions of acres of California forest, we don’t assume that there was anything special about the initial spark. Quite to the contrary, we understand that in context of the large-scale environmental conditions — prolonged drought, a buildup of flammable undergrowth, strong winds, rugged terrain, and on so — that truly drive fires, the nature of the spark itself is close to irrelevant.
Yet when it comes to the social equivalent of the forest fire, we do in effect insist that there must have been something special about the spark that started it. Because our experience tells us that leadership matters in small groups such as Army platoons or start-up companies, we assume that it matters in the same way for the very largest groups as well. Thus when we witness some successful movement or organization, it seems obvious to us that whoever the leader is, his or her particular combination of personality, vision, and leadership style must have supplied the critical X factor, where the larger and more successful the movement, the more important the leader will appear.
By refusing to name a leader, Occupy Wall Street presents a challenge to this view. With no one figure to credit or blame, with no face to put on a sprawling inchoate movement, and with no hierarchy of power, we simply don’t know how to process what “it” is, and therefore how to think about it. And because this absence of a familiar personality-centric narrative makes us uncomfortable, we are tempted to reject the whole thing as somehow not real. Or instead, we insist that in order to be taken seriously, the movement must first change to reflect what we expect from serious organizations — namely a charismatic leader to whom we can attribute everything.
In the case of Occupy Wall Street, we will probably get our wish, for two reasons. First, if OWS grows large enough to deliver any lasting social change, some hierarchy will become necessary in order to coordinate its increasingly diverse activities; and a hierarchy by nature requires a leader. And second, precisely because the outside world wants a leader — to negotiate with, to hold responsible, and ultimately to lionize — the temptation to be that person will eventually prove irresistible.
Leaders, in other words, are necessary, but not because they are the source of social change. Rather their real function is to occupy the role that allows the rest of us to make sense of what is happening — just as Tolstoy suspected. For better and worse, telling stories is how we make sense of the world, and it’s hard to tell a story without focal actors around which to center the action.
But as we witness a succession of popular movements, from the Arab Spring to Occupy Wall Street, we can at least pause to appreciate the real story, which is the remarkable phenomenon of a great many ordinary individuals coming together to change the world.
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.
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.
A piece in yesterday’s New Scientist titled ‘Can Complexity Theory Explain Egypt’s Crisis?’ explores ideas of complexity in the context of the ongoing events in Egypt. It draws on the insights of two noted complexity thinkers – Yaneer Bar-Yam and Thomas Homer-Dixon. Excerpts are reproduced below with permission:
Egyptians are the world’s biggest wheat importers and consumers, and most are poor. As a result, the government maintains order with heavy subsidies for bread. It also runs the ports where imported wheat arrives, the trucks that haul it, the flour mills and bakeries…
[Such systems] are fine so long as the top of the hierarchy is in place, and can recover quickly. But take the top away – as is happening in Egypt – and the entire system risks collapse.
The early signs of this are showing. Bread is getting scarce in Egypt’s capital, Cairo. Bakeries are closing for lack of flour… Imported wheat is sitting in ports as cranes and lorries stand idle. The interlocking dependencies that tie modern economies together spread dislocation further. Even where there is food, Egyptians have little money to buy it, as businesses and banks close, cash machines empty and wages dry up…
…The stresses of decades of dictatorship might have turned the entire Middle East into a “self-organised critical system”… The build-up of stresses makes such systems vulnerable to cascades of change triggered by relatively small disruptions…
The key argument of the article is that a hierarchical system (like the Egyptian government) facing a dynamic and interconnected problem is - in the extreme - prone to catastrophic collapse.
Regular Aid on the Edge of Chaos readers will know that this resonates strongly with previous reflections on this blog. The growing interconnectedness between finance, fuel and food systems was the focus of a recent piece exploring the ‘Globalisation of Vulnerability’. The maladaptive nature of organisational and governance systems in the face of change have also been covered on numerous occasions, including in a piece on ‘History on the Edge of Chaos’.
However, there is another vital dimension to complex adaptive systems that does not get sufficient coverage in the New Scientist piece. The author does briefly acknowledge that there are two sides to complex interdependencies: as well as collapse, they can also generate cascading change. (For an example, see the lessons from the Obama Presidential Campaign as recounted by veteran civil rights activist Marshall Ganz.) But the article misses out on the opportunity to reflect on the remarkable efforts of the anti-government protestors across Egypt through a complexity lens.
Without a doubt the most astonishing feature of the unfolding events in Egypt has been the leaderless, self-organised, networked movement that emerged and managed to maintain a peaceful and resilient presence – despite the efforts of the pro-Mubarak contingents.
As well as insights into collapse, complexity science can tell us something about how such movements happen, and give insights into the dynamic social processes that play out. It can tell us something about resilience in the face of oppression. It gives insights into the information and communication networks that feed and shape a movement. The ideas of complex adaptive systems can help us learn more about emergent collective action, and – through this – about how beliefs are reinforced, about how passion is shared and about how courage builds.
And – as we have seen repeatedly since January 25th - cascading, unpredictable change can have a profoundly human face.
Complexity science does more just than provide new ways to theorise descent, freefall and collapse. It can also help further our understanding of what human beings are capable of achieving. As Thomas Homer-Dixon, mentioned above, put it in the title of his book: there is an Upside to Down.
Most analysts agree that globalisation has become more intensive and dramatic in recent decades because of advances in technology, communications, science and transportation. While it can be a catalyst for development and progress, globalisation also carries significant and increasing challenges for aid policy makers and practitioners alike.
I: The new face of vulnerability?
Recent years have seen dramatic illustrations of the downsides of globalisation. Perhaps most significant of these has been the global financial crisis taking its toll on the poorest communities in developing countries.
However, the specific localised impacts and implications of such shocks often fall below the radar of research, analysis and existing monitoring systems. On the whole, these systems are too narrow in scope and too shallow in reach to capture the diverse, context-specific experiences of poor people. As Ban Ki Moon noted in the preface to ‘Voices of the Vulnerable’ in June 2010:
…in the face of the global financial crisis a number of developing countries have proven to be remarkably resilient – if judged purely in terms of economic growth. At the same time, it appears that the burden of coping has been borne disproportionately by poor and vulnerable people. This reality is poorly understood…” (emphases added)
In fact, much work on vulnerability has been traditionally undertaken in ‘disciplinary silos’ – in highly specialised ways which are often in isolation from each other. Environmental vulnerability is assessed by the climatologists, nutritional vulnerability by the food security experts, market vulnerability by the economists, disease vector vulnerability by epidemiologists, and so on. The precise nature of vulnerability is often also heavily debated, leading to differences within the silos.
The gap between this ‘stove-piped’ understanding and multi-faceted reality becomes heightened when one considers the number of ongoing global crises. The financial crisis is just one of a number of global trends (that we currently know about) which are interacting and impacting on the lives of poor and vulnerable people.
To take another example, the 2010 World Disasters Report focused on urbanisation, and found that “a high proportion of this urban growth is in cities at risk from the increased frequency and intensity of extreme weather events and storm surges that climate change is bringing or is likely to bring.”
Along similar lines, the global food system is showing signs of strain once again. Work done during the last upswing in prices in 2008 suggested that a key requirement was better monitoring and anticipation of future bubbles. Unfortunately anticipation has not led to preventative action. All the signs are that environmental disasters - driven by climate change - and a growing speculative bubble in commodities – driven partly by changing investor patterns in the wake of the financial crisis - are pushing the world into a new food price crisis.
In the face of these trends and shocks, there is a slowly growing recognition that vulnerability itself has become globalised. Interestingly this insight has not come from within the aid sector but from organisations such as the World Economic Forum, whose Global Risk Report 2010 shows that – like the world economy – vulnerabilities are now tightly interconnected.
Global shocks and stresses have multiple, unpredictable effects and increasingly demand – but do not always trigger – diverse responses at the local level. As recent research indicates, employing language which Aid on the Edge regulars will recognise:
Cause and effect in global systems is distinctly nonlinear. Inputs and outputs may not be proportional: a cause with ever-so-slightly different parameters than the previous instance might result in a wildly different effect. Additionally, systems and their component sub-systems interact to produce feedback loops that can either amplify or stabilize resulting effects. Feedbacks blur the line of what is cause and what is effect. The global system is characterised by various sizes and degrees of complexity combined into a tangled and heaving mass of interdependent actions.
Despite these shifts in the nature of vulnerability, international aid policy and practice are still dominated by narrow, parochial approaches. Take for example the findings of a report on chronic vulnerability in Africa which found that much of the analysis undertaken by international agencies did not examine root causes and tended to divide vulnerability into immediate and structural issues. The agencies then focused their efforts on the immediate issues, allowing the structural issues to be largely ignored.
By contrast, the reality of vulnerability for most poor people was found to be “complex and nuanced… vulnerability can be influenced by gender, ethnic group and generation issues, and by contemporary and historical social processes that are often not analysed and not explained.” (emphasis added)
It would seem that it is only after things go seriously wrong that the inter-relationships between the key drivers of vulnerability become of importance to international agencies. To cite one prominent and very current example, the densely urban population in Port-Au-Prince was up until January 2010 experiencing high levels of vulnerability and multiple climactic shocks. It was only after the 12 January earthquake that aid agencies became sensitive to this interconnected reality, by which time it was already too late for many in Haitian population. As one satirical headline put it at the time: ‘Massive earthquake reveals poor country called Haiti to the world’.
These examples give weight to the criticism I have made elsewhere – that the international community employs a ‘catastrophe-first’ model of lesson learning, which puts the emphasis on disasters striking before action is taken.
There are often good practical reasons for this – anticipation is still in its infancy, prediction is largely impossible. However, without wanting to diminish these challenges, the key is to do much more in terms of changing our mindsets.
As we argued in an ALNAP study on humanitarian innovations, there is a crucial need to find ways to move away from this catastrophe-first model of learning, towards putting vulnerability first.
II: From ‘Catastrophe-First’ to ‘Vulnerability-First’ ~ Three Ideas
This idea of putting vulnerability first has a number of possible implications, of which three are explored below.
Putting vulnerability first requires a better, more inter-disciplinary, understanding of the globalised vulnerability landscape among both policy makers and operational decision makers. As well as better shared data and analysis, we need to find better ways of breaking down disciplinary silos.
One way of doing this might be to examine how the ideas in one area might be transferred over to another. There is already some useful work in this direction, which seeks to generalise the work of the Intergovernmental Panel on Climate Change (IPCC). This suggests that vulnerability may be characterised as a function of three components: sensitivity, exposure and adaptive capacities. The UNFCC shows that such vulnerabilities can be understood through a combination of top-down modelling and scenarios with bottom-up, community-based approaches which recognise and build upon local knowledge and coping strategies. An interesting manifestation of this interdisciplinary approach in action is how the notion of ‘building back better’, a mainstay of disaster risk reduction, has found its way into the dialogue on the aftermath of the global financial crisis.
Putting vulnerability first also means finding ways of communicating vulnerability understanding in ways that capture the political and humanitarian imagination alike. For example, some agencies have started using the evocative image of vulnerability defined as communities and individuals ‘living on the edge’. People are living on the edge if their lives and livelihoods are exposed and sensitive to shocks and stresses, and their adaptive capacities are constantly on the verge of being overwhelmed. Living on the edge suggests that a small push could send a community or individual over the edge.
Being forced to live on the edge can have profound effects on the way people conduct their lives. It can lead to coping strategies which are overtly risk-averse – poor farmers faced with unpredictable prices might start to act in cautious and non-entrepreneurial ways even during normal times, limiting their prospects of increasing their well-being. At the other end of the spectrum, as has been found in coastal communities in Bangladesh, the desperately poor may adopt a “gambler’s throw” strategy, risking all or nothing when they know that a major livelihood shock is inevitable.
The emerging globalised vulnerabilities have already taken their tol, on poor people, and will continue to do so: increasing the numbers ‘living on the edge of disaster’, changing their geographic distribution, and increasing the diversity and unpredictability of risks faced. Successive waves of impacts have fundamentally changed coping mechanisms, reduced long term resilience for the sake of short-term coping mechanisms, undermining development prospects and making many communities more vulnerable to future shocks.
So we need to be paying much more attention to, and advocating for, people living on the edge of disaster, rather simply waiting until they have been tipped over the edge.
Finally, putting vulnerability first also means highlighting examples of good practice and innovations, and fostering national level and local engagement. Social protection, micro-insurance, community-based adaptive learning measures, and new national policy frameworks all have an important role to play in enhancing community-level and national resilience. It also means being honest about how far the international community still has to go as well as its inherent limitations. We need to see some significant organisational and professional changes in international agencies, who need to do more than simply ‘mainstream’ vulnerability. The globalised nature of vulnerability demands nothing short of a fundamental strategic re-orientation.
[This] line of thinking helps us avoid the trap of simply rebuilding and repairing flawed structures of the past—be it an economic system overly reliant on risky speculation or a health-care system that splits a nation at its financial seams and yet fails to deliver adequate coverage…
… the resilience perspective stands in stark contrast to development paradigms and global policies that… offer only minor adjustments of current behaviors, and that tend to concentrate on technical quick fixes to get rid of the problems…
...it encourages us to anticipate, adapt, learn, and transform human actions in light of the unprecedented challenges of our turbulent world…
Tipping points are found in ecosystems, economies and even bodies. But they’re usually recognized in retrospect, when it’s too late for anything but regret. Now a growing body of research suggests there are telltale mathematical signals. If scientists can figure out how to detect them, they may be able to forecast tipping points ahead of time.”
So starts a 2009 Wired article on prediction and catastrophes. Using the Nobel Prize winning work of Kenneth Wilson on sudden, non-linear changes in dynamic physcial systems, increasing numbers of scientists have been able model catastrophic changes in many other contexts. These include:
The researchers involved in these different examples argue that all critical transitions are preceded by the same basic mathematical patterns. Some specific examples of catastrophe signals are:
Stresses in the feedback loops that are evident in complex systems such as ecosystems
Systems take longer to recover from fluctuations that they normally are resilient to
Representations of system – visual or mathematical – that become jagged rather than smooth
The key to prediction of such events is then simply “to figure out what sort of data to look for, and then how to make sense of it.” As one of the authors of a Nature study put it:
….We are repeatedly blindsided by disasters that come out of the blue. If we had better tools for anticipating those events, we could avoid some of them…”
And a co-author of the same study:
…The fact that the patterns seem to recur in so many different circumstances suggests that the mechanisms underlying them may have universal characteristics…”
However, there is considerable scepticism about whether such prediction is possible.
Not only is the data gathering extremely time-intensive and expensive, but causality of catastrophic events is complex and there may be many interacting feedback loops and threshold points. Relevance may not be determinable ahead of time.
This debate is a vital one for policy makers and practitioners, and it illustrates the spectrum of approaches evident among those who would seek to make use of the complexity sciences.
On one end of the spectrum, discontinuous, non-linear change is being presented as a predictable property of complex systems, by those who seem to be suggesting that it is possible to exert greater control over real-world systems.
On the end of the spectrum, there are those that see interconnectedness of complex systems leading to inherent unpredictability. Two unlikely advocates for this perspective are the present and former Chairmen of the US Federal Reserve. As Alan Greenspan (whose evident affinity with complexity sciences was explored here a few weeks back) argued in recent evidence to Congress in the context of the financial crisis:
History tells us [we] cannot identify the timing of a crisis, or anticipate exactly where it will be located or how large the losses and spillovers will be… Nor can they fully eliminate the possibility of future crises.
His successor, Ben Bernanke, has linked such attempts to the futility of predicting the weather:
As an economist and policymaker, I have plenty of experience in trying to foretell the future, because policy decisions inevitably involve projections of how alternative policy choices will influence the future course of the economy… over the years, many very smart people have applied the most sophisticated statistical and modeling tools available to try to better divine the economic future. But the results, unfortunately, have more often than not been underwhelming. Like weather forecasters, economic forecasters must deal with a system that is extraordinarily complex, that is subject to random shocks, and about which our data and understanding will always be imperfect… To be sure, historical relationships and regularities can help economists, as well as weather forecasters, gain some insight into the future, but these must be used with considerable caution and healthy skepticism (emphasis added)
Stephen Strogatz sums up a pragmatic perspective on this debate very neatly:
It’d be very nice if it were true that there were precursors for tipping points in all these diverse systems. It’d be even nicer if we could find these precursors. I want to believe it, but I’m not sure I do”
The relevant quotes from the speech are highlighted below.
First, on uncertainty and the limits of models:
Uncertainty is … the defining feature of [the monetary] landscape… As a consequence, the conduct of monetary policy … requires an understanding of the many sources of risk and uncertainty that policy makers face…a critical result [of the attempt to achieve this understanding] has been the identification of a relatively small set of key relationships that, taken together, provide a useful approximation of our economy’s dynamics … [However] our knowledge about many … important linkages is far from complete and in all likelihood will always remain so. Every model … is a vastly simplified representation of the world that we experience…
A well-known proposition is that, under a very restrictive set of assumptions, uncertainty has no bearing on the actions that policy makers might choose … These assumptions are never met in the real world.
On assumptions of linearity and predictability:
… a prominent shortcoming of our structural models is that … not only are economic responses presumed fixed through time, but they are generally assumed to be linear …
… only a limited number of risks can be quantified with any confidence. And even these risks are generally quantifiable only if we accept the assumption that the future will replicate the past …
And on assumptions of interconnectedness:
… also the relationships underlying the economy’s structure change over time in ways that are difficult to anticipate … what constitutes money has been obscured by the introduction of technologies that have facilitated the proliferation of financial products …
… Our problem is not the complexity of our models but the far greater complexity of a world economy whose underlying linkages appear to be in a constant state of flux.
A little digging around highlighted another Greenspan speech, given to a symposium of central bankers in 2005:
We all temper the outputs of our models and test their results against the ongoing evaluations of a whole array of observations that we do not capture in either the data input or the structure of our models. We are particularly sensitive to observations that appear inconsistent with the causal relationships of our formal models.
And then this in an interview with the Daily Show in 2007:
I was telling my colleagues the other day… I’d been dealing with these big mathematical models for forecasting the economy… I’ve been in the forecasting business for 50 years, and I’m no better than I ever was, and nobody else is either…
And finally, Greenspan’s prepared testimony to the Financial Crisis Inquiry Commission in 2010, which utilises emergence, non-linear change, resilience and the limits of prediction:
Bubble emergence is easy to identify in narrowing credit spreads. But the trigger point of crisis is not. A financial crisis is descriptively defined as an abrupt, discontinuous drop in asset prices. If the imbalances that precipitate a crisis are visible, they tend to be arbitraged away. For the crisis to occur, it must be unanticipated by almost all market participants and regulators. Over the years, I have encountered an extremely small number of analysts who are consistently accurate at discontinuous turning points. The vast majority of supposedly successful turning point forecasts are, in fact, mere happenstance.
In my view, the recent crisis reinforces some important messages about what supervision and examination can and cannot do. Regulators who are required to forecast have had a woeful record of chronic failure. History tells us they cannot identify the timing of a crisis, or anticipate exactly where it will be located or how large the losses and spillovers will be. Regulators cannot successfully use the bully pulpit to manage asset prices, and they cannot calibrate regulation and supervision in response to movements in asset prices. Nor can they fully eliminate the possibility of future crises.
What supervision and examination can do is promulgate rules that are preventative and that make the financial system more resilient in the face of inherently unforeseeable shocks. Such rules would kick in automatically, without relying on the ability of a fallible human regulator to predict a coming crisis.
Of particular interest is whether and how these ideas move beyond speeches and conferences to have an influence on policy and practice. A few senior bankers do seem to be coming out of the ‘complexity closet’, drawing on the ideas expressed by Greenspan over the years.
Andrew Haldane, a director at the Bank of England, has given numerous speeches in which he talks about enhancing the robustness and resilience of the global financial network using the ideas of complexity science.
Dr DeLisle Worrell, Governor of the Central Bank of Barbados, has spoken about the importance of complexity science for re-thinking fundamental economic theories.
This is an interesting trend, and well worth watching. Some of the implications for development economics were explored in a previous Aid on the Edge post.
