Feeds:
Posts
Comments

Archive for the ‘Trade’ Category

The Limitations of Imitation

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

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

I: The Virtues of Copying?

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

Examples include:

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

Such imitators often proved to be the winners in business:

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

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

II: Imitation or Exploring Adjacent Possibilities?

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

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

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

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

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

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

III: Innovation in the App Ecosystem

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

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

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

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

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

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

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

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

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

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

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

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

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

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

IV: Conclusions: Mix it Up

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

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

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

Any lessons here for the aid system, I wonder?

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

Complexity and the Wealth of Nations

Posted in Economics, Evolution, Innovation, Networks, Public Policy, Self organisation, Technology, Trade on December 15, 2010 | 3 Comments »

Complexity scientists have long argued for the use of concepts such as nonlinearity and interconnectednesss to better understand economic phenomena, including growth, market failures and crashes. Ongoing research at the Harvard Center for International Development is taking this area of work forward in very promising ways.

In some ways, as Tim Harford has argued, the notion of complexity resonates closely with the classical roots of economic thinking. Adam Smith emphasised the importance of specialisation as a source of the wealth of nations, and “specialisation and complexity are closely linked: an economy with more specialists is one that requires more teamwork and more distinct interactions between individual activities.”

However, this is not how most economists – development or otherwise – have traditionally thought about growth. This may be about to change following ground-breaking work by researchers at the Harvard Center for International Development. Ricardo Hausmann and Cesar Hidalgo have been looking at economic complexity in a rigorous fashion in order to develop testable hypotheses about products, networks and self-organising processes of economic wealth creation.

The best introduction to this work is a thought-provoking TEDx talk Hidalgo gave in August 2010:

As Harford noted in his piece:

Development economists may find themselves paying more attention to such issues in future. We know very little about how to encourage an economy to become more complex and acquire new product capabilities. That may explain why we still have so much to learn about how to make poor countries rich.

There are of course downsides to complexity and interconnectedness, as the credit crunch and resulting global crisis has shown. Andy Haldane, director of the Bank of England,  put the case forward compellingly in a 2009 speech covered in a previous Aid on the Edge of Chaos post:

…interconnected networks exhibit a knife-edge, or tipping  point, property.  Within a certain range, connections serve as a shock-absorber.  The system acts as a mutual insurance device with disturbances dispersed and dissipated.  Connectivity engenders robustness. Risk-sharing – diversification – prevails. But beyond a certain range, the system can flip the wrong side of the knife-edge.  Interconnections serve as shock-amplifiers, not dampeners, as losses cascade.  The system acts not as a mutual insurance device but as a mutual incendiary device. Risk-spreading – fragility – prevails. The extent of the systemic dislocation is often disproportionate to the size of the initial shock…” (emphasis added)

The key may be to find the balancing point between the two: the point of ideal  trade-off between creativity and resilience – or what some theorists describe as the ‘edge of chaos’. This would seem to carry implications for economic development strategies used by international agencies. A follow-up post will look at how these ideas are being taken up in the IMF’s work on economic recovery in the wake of the crisis.

NB Interested readers can see previous Aid on the Edge of Chaos posts on the topic of complexity and economics here.

Read Full Post »

Easterly team discovers power law in international trade – what are the implications?

Posted in Economics, Public Policy, Reports and Studies, Trade on November 1, 2009 | 2 Comments »

A new study by William Easterly and colleagues at NYU may be a significant breakthrough for the use of complexity science concepts in international development.

In the growing community of complexity thinkers in the aid sector, those with a qualitative mindset have been rather more prominent than those taking a quantitative approach. Published papers on complexity and aid to date have been predominately, if not exclusively, qualitative in nature. Given the quantitative basis of much of complexity thinking, a greater balance in the mix of approaches would be no bad thing.

The recent World Bank publication of a report by William Easterly, Ariell Reshef, and Julia Schwenkenberg  (all at NYU) on ’power laws’ in international trade may be an important step towards achieving such a balance. To understand why, we need a bit of background on power laws and their use in different contexts.

There is a growing movement (lucidly described by McKinsey Strategy Principle Michelle Zanini in June 2009) whose focus is the application of quantitative complexity theory to social, economic and political issues. This work involves bringing so-called ‘hard’ scientists into the social science fold. One of the most notable examples is the geophysics and earthquake authority Didier Sornette, who also leads the Financial Crisis Observatory in Zurich – there are numerous others. The starting point of much of this work is that prediction in complex systems is impossible, or at least extremely difficult, because change is the products of many interdependent actors and factors. Such systems are characterised by cascades of events, known as sandpile effects, and as such are inherently unstable.

One of the widely accepted characteristics of complex systems is that the frequency and magnitude of outcomes can be described by a mathematical relationship called a “Power Law”. Power Law Curves are characterised by a short “head” of frequently occurring small events, dropping off to a long “tail” of increasingly rare but much larger ones.

Power Law Curves can be better understood by reference to another widely used mathematical tool, the Bell Curve. To take an example used by Mark Buchanan, if you set 500 salespeople to work independently on the telephones, then, according to bell curve thinking, their total weekly sales will almost always fall within a narrow range around some average; with large deviations proving very rare. The bell curve reflects an underlying and widely accepted theory for what happens when many independent events or actions contribute to some outcome – and underlies the widespread use of past averages as a guide to the future.

However, the Bell Curve is based on independent events. When one event influences another, as in interconnected complex systems, the bell curve does not apply. To continue Buchanan’s example, salespeople, in reality, rarely act completely independently. They are likely to be members of a product group, a regional body; they might meet to agree on goals and compare tactics, and they may compete for bonuses. In short, what one salesperson does has the potential to influence the behaviour of others, leading to collective swings in sales effectiveness. In such interdependent systems, power-law pattern frequently takes the place of the bell curve, because it is a rather more accurate tool. To take one example, in the case of market fluctuations, the bell curve predicts a one-day drop of 10 percent in the valuation of a stock just about once every 500 years. Power law approaches give a very different and more reliable estimate of such events occuring about once every five years.

As McKinsey analysis shows (see chart below), if the frequency of banking crises from 1970-2007 is plotted alongside their magnitude (measured by losses of GDP for each affected country) the result is a power curve. There is a short head of some  70 crises, each with losses of less than 15 percent of GDP, quickly falling off to a long tail of very few, very large crises .

Powercurves

Earthquakes, forest fires, and blackouts illustrate similar patterns, as the chart also shows. For example, from 1993 to 1995, Southern California registered 7,000 tremors at 2.0–2.5 on the Richter scale, falling off to the 1994 Northridge earthquake, at the end of the tail, with a magnitude of 6.7. Similar power laws can be found in US industrial production, in corporate bankruptcies, in terrorism and in global wars.

The Power Law Curve highlights a key property of complex systems: extremely large outcomes are more likely than they are in a normal, bell-shaped distribution, which implies a relatively even spread of values around a mean – in other words, shorter and thinner tails.
The above examples indicate that power law patterns, with their small, frequent outcomes mixed with rare, hard-to-predict extreme ones, exist in many aspects of the economy. The emerging view is that the economy is much like other complex systems characterised by power law behaviour.

With the NYU team’s discovery of power laws in international trade, we now have an example specific to development economics. Looking at manufacturing exports for a sample of 151 countries, and a range of 3000 products from the UN Comtrade database, Easterley et al document high degrees of concentration in the successful exports:

…For every country manufacturing exports are dominated by a few ‘big hits’ which account for most of the export value and where the ‘hit’ includes both finding the right product and finding the right market. Higher export volumes are associated with higher degrees of concentration, after controlling for the number of destinations a country penetrates. This further highlights the importance of big hits. The distribution of exports closely follows a power law…”

To give a few examples of big hits, out of 2985 possible manufacturing products in the dataset and 217 possible destinations for exports:

  • Egypt gets 23 percent of its total manufacturing exports from exporting one product – ceramic bathroom and kitchen items – to one destination, Italy, capturing 94 percent of the Italian import market for that product.
  • Fiji gets 14 percent of its manufacturing exports from exporting womens and girls cotton suits to the U.S., where it captures 42 percent of US imports of that product.
  • The Philippines gets 10 percent of its manufacturing exports from sending electronic integrated circuits and micro-assemblies to the US where it captures 80 percent of US imports of that product
  • Nigeria earns 10 percent of its manufacturing exports from shipping floating docks and special function vessels to Norway, making up 84 percent of Norwegian imports of that product.

On the basis of this analysis, Easterly and his fellow researchers conclude that ‘big hits’ cannot be identified through a policy of “picking winners” for export development. Specifically, the power law characterisation implies that the chance of picking a winner diminishes exponentially with the degree of success. Also, as developing countries are more exposed to the vagaries of product demand than rich ones, this further lowers the benefits from trying to pick single winners.

On the basis of this finding, the authors make some preliminary suggestions about the relative roles and importance of government and the market in ‘picking winners’. To find out more, read the paper, and wait for the trade specialists to battle it out (a debate has already started on the World Bank’s Private Sector Development Blog).

For all those with an interest in complexity sciences, a key point is that this study proves it is feasible to undertake quantitative analysis of complex global systems such as those dealt with by international aid agencies. With more such studies, we may be able to start to move beyond notions of complexity as a ’useful metaphor’, as suggested by Duncan Green last year, and start talking about complexity as a recognisable, empirically provable property of the systems in which aid agencies operate.

When complexity is proved in this way, simplistic linear assumptions  based on extrapolation of past averages become meaningless. Proving the complexity of  real world systems  means accepting that the world works in a far more erratic and unpredictable way than our current mental models allow, and this means we need to start to question both our desire and and our ability to control the world.

To return to the point that started this article, this work does not negate the qualitative approaches to complexity. Rather it shows that multi-methods approaches to understanding complexity are possible in a development context, and they may well make the argument more powerfully (forgive the pun!) than an approach skewed towards just qualitative analysis. So, hats off to Easterly & Co for this important and timely analysis.

Read Full Post »

Follow

Get every new post delivered to your Inbox.

Join 2,488 other followers