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The Limitations of Imitation

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

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

I: The Virtues of Copying?

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

Examples include:

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

Such imitators often proved to be the winners in business:

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

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

II: Imitation or Exploring Adjacent Possibilities?

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

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

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

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

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

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

III: Innovation in the App Ecosystem

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

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

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

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

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

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

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

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

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

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

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

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

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

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

IV: Conclusions: Mix it Up

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

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

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

Any lessons here for the aid system, I wonder?

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JP Morgan and the Price of Complexity

Posted in Accountability, Biology, Chaos, Economics, Evolution, Financial crisis, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Self organisation, Strategy, Technology, Trade on May 15, 2012 | 1 Comment »

This is the text of an article in the Washington Post by Dominic Basulto about last week’s events in the financial markets. Great stuff.

When news first broke Thursday that JPMorgan’s credit derivatives portfolio had sustained a loss of $2 billion, and potentially as much as $5 billion, on trades gone awry, there was an immediate call for greater regulatory oversight over banks’ high-risk trading activities. The message was clear: “If you’re going to be a bank, then you can’t play at the casino,” as the Post’s Ezra Klein writes. At the same time that the market was lopping off billions of dollars in shareholder value, JPMorgan was purging top executives responsible for the bungled trades and facing awkward questions about its public stance in favor self-regulation. If banks can’t regulate themselves, though, who can?

Inevitably, the answer to that question depends on whether you view the financial markets as complicated or complex. If the financial markets are merely complicated, traditional approaches to regulation can be effective: regulators can turn their attention to individual actors within the market and systematically make the requisite changes to restore the market to equilibrium. In a complex system, however, traditional approaches to regulation can be woefully inadequate — small changes may end up having outsized effects, while big changes may end up having little or no effect. In a complex system, you need to focus on the interactions between each of the participants as much as the condition of individual actors.

The trading screens of Wall Street are, if nothing else, the perfect example of how computers are able to mask the complexity of an underlying system by being able to reduce the real world into 1’s and 0’s. There is no shortage of algorithms, formulas, sophisticated risk management models and quantitative trading models promising to reduce complex financial market interactions to something that can be studied, adjusted and tweaked. In theory, regulators should be able to look at a few numbers, compare them to a few benchmarks, and suggest the necessary adjustments.

But it is rarely that easy.

There is a big difference between complicated and complex. In a classic example, an automobile is complicated, but a transportation system with human drivers is complex. Ultimately, you can fix an automobile by lifting up the hood and checking that everything is working properly, no matter how sophisticated the parts. You can only fix a transportation system, though, by understanding how each of the drivers interacts with each other and understanding the distributions of dynamic traffic patterns.

One of the most innovative areas of public policy, in fact, involves the intersection of complexity theory with regulatory policy. Complexity theory, which has been used to model complex systems ranging from ant colonies to climate change, has also been applied to financial regulation. Practitioners within the financial markets are well-versed with complexity theory and its cousin: chaos theory. One of the entities consistently singled out in the academic literature is the CDC (Center for Disease Control), which is held up as a role model for how to deal with complex systems. For example, using complexity theory, the CDC was able to suggest that — contrary to what you might see in movies like “Contagion” — restricting air travel would have little or no impact on stopping the SARS outbreak. As the CDC recognizes, you simply can’t regulate away diseases. You need to deal with them with in real-time as they appear and find the right levers to stop them. Most importantly, you need to be able to spot potential flare-ups before they occur and understand the emergent behaviors that lead to outbreaks.

Certainly, the types of events that we observe in the financial markets, such as “flash crashes” and billion-dollar Black Swan events in derivative markets, are reminiscent of complex systems behaviors where small changes lead to unimaginable consequences. While the Volcker Rule, which would keep banks from engaging in risky trading behavior, could be effective in the short-term in avoiding certain types of adverse market effects, it may not be as effective in dealing with the full range of market fluctuations in the long-term. Implicitly, we recognize the complexity of financial markets by ceding power to computers and algorithms to price financial instruments properly. Now, we need to recognize that this complexity also has important consequences for the way that we think about regulating these markets.

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Taming fragility?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I: The Official Views of ‘Development Churches’

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

Specifically:

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

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

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

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

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

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

II: Moving Away From Doing the Wrong Thing Righter

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

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

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

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

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

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

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

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

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

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

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

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

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Facebook, social media and the complexity of influence

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

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

I: Facebook’s Impact on Europe

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

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

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

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

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

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

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

II: Spurious reasoning, dodgy numbers

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

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

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

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

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

III: Social media hyperbole

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

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

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

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

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

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

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

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

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

IV: Navigate complexity, don’t ignore it

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

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

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

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

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

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

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

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

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What are leaders really for?

Posted in Accountability, Campaigns, Conflict and peace building, Evolution, Financial crisis, Innovation, Knowledge and learning, Leadership, Networks, Self organisation on December 7, 2011 | 5 Comments »

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 because of 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.

His argument resonates strongly with ideas of complex adaptive leadership explored in a previous post here.

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.

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New Scientist Briefing: Can ecosystems show how to fix the euro?

Posted in Biology, Economics, Financial crisis, Innovation, Institutions, Knowledge and learning, Networks, Public Policy, Technology, Trade on November 10, 2011 | 6 Comments »

The eurozone, like the rest of the world economy, is a complex networked system. That gives it properties economists rarely consider but which could help us understand the current crisis. This New Scientist ‘Science in Society’ Briefing examines the issues.

What is a complex network?

Complex networks have many interconnected components which influence each other’s behaviour. These changes then feed back on each other. A famous example is the numbers of predators and prey in a given environment, which vary in a complex interdependent way. The eurozone – the 17 countries that share a common currency, the euro – is similarly interdependent, with similar feedback mechanisms.

All complex networks are governed by a balance between negative feedback, such as interest rates, which is stabilising, and positive feedback, such as the self-reinforcing erosion of trust in markets, which is destabilising, says physicist Len Fisher at the University of Bristol, author of Crashes, Crises and Calamities: How we can use science to read the early-warning signs.

How does that help us understand economic crises?

In certain circumstances, one type of feedback can end up dominating the system, causing it to change so dramatically that it flips to another state. Examples include the way animal populations can suddenly collapse or the way economies can slip into recession.

These tipping points tend to be highly unpredictable. Even so, Fisher says computer models of the system can still show how the system can change. Yet leading economics journals, he says, do not accept computer-modelling studies. “Mainstream economists have not considered these non-linear effects,” agrees Oonsie Biggs of Stockholm University’s Stockholm Resilience Center in Sweden.

Can we understand complex systems well enough to control them?

Maybe. The diversity of a network’s components and the density and strength of its connections – called its connectivity – affect the system’s resilience, or resistance to change. More connections make a system more resilient: if one component fails others can fill in. But only up to a point. Go past a certain threshold and more connectivity makes the system less resilient because a single failure can cascade to every other component.

The trick is to get the balance right. “Cascades of failure may be controlled by changing the nature and strength of the links between various parts of the networks,” says Fisher. Much current research in complex systems focuses on assessing connectivity correctly to enable that. Other work aims to detect behaviour that indicates an imminent collapse.

So turning 17 separate currencies into one eurozone was a cascading failure waiting to happen?

Yes. That is why Greek debt is a crisis, even though Greece accounts for only 2.5 per cent of the eurozone’s GDP. News of its debts caused the trust that markets placed in Greek government bonds to plummet. Its creditors are mainly in the eurozone, so a Greek default is causing markets to lose confidence in other members, such as Italy – which is too big to bail out.

Could the crisis have been avoided?

Complexity theory shows what went wrong. Yaneer Bar Yam of the New England Complex Systems Institute in Cambridge, Massachusetts, says his still-unpublished studies show that investors profited by driving down the value of Greek government bonds, triggering the crisis. If instead of national bonds issued by sometimes weak economies, the eurozone had one common bond backed by powerhouses such as Germany, such an attack could not have happened.

Germany rejects eurobonds. But, says Bar Yam, complex systems such as multicellular organisms show that “if you are going to accept common risk, you have to invest in defences that extend to the weakest member”. Either that or make sure an attack on a weak member cannot spread, a technique that ant colonies have perfected: the death of a single ant has little effect on the colony as a whole. “Biology has solved this problem several ways,” says Bar Yam.

If connectivity is a risk, why create the euro?

Connectivity is also profitable as it makes economic production much more efficient. And it can adapt to problems: connectivity allows other eurozone countries to help Greece, and to build better common defences.

Trade-offs between efficiency and resilience may mean we need to develop ways to make systems more stable, such as pruning connectivity or paying for defence measures. “We now have the quantitative, analytical tools to do that,” says Bar Yam. Such models may also show when short-term costs that reduce a system’s efficiency may be warranted because of the long-term benefits of increased system resilience.

Some connectivity problems could be hard to prune, though. Biggs says close coupling between major global hubs, such as the eurozone and the US, is a big source of instability that which may threaten strong contributors in future, like France and Germany.

Why don’t economists know this?

They are starting to. Some economic theorists have drawn parallels between financial networks where bank failures are prevented, and forests where small fires are always put out. Such forests accumulate deadwood fuel and lose patchiness, increasing connectivity. When a fire eventually breaks out, it becomes huge. That’s why forest managers now encourage regular, small burns. Similarly, banking networks may need low-level failures to prune connectivity and risk.

Systems-based thinking has even reached the Bank for International Settlements in Basel, Switzerland, which sets global rules for the capital a bank must hold to back loans. It announced this month that the risks posed by banks depend on their “size, interconnectedness, complexity and global scope”. So from 2016, “global systemically important banks” – initially 29 of them – will be required to keep more capital on hand per dollar loaned than less vital banks. This is partly to “discourage banks from becoming even more systemically important” – in other worlds, too big to fail.

This recognition of the importance of complexity has been largely confined to banking, however. The eurozone is a network of governments. It is not clear how eagerly they will adopt a way of thinking that truly puts collective interests first.

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Results 2.0: Towards a portfolio-based approach

Posted in Accountability, Evaluation, Healthcare, Innovation, Institutions, Knowledge and learning, Leadership, Malaria, Meetings, Organisations, Public Policy, Strategy on June 30, 2011 | 9 Comments »

The international development sector has been in a tug of war around the ‘results agenda’ for the past few months. This post explores the tensions and suggests a way to bring the sides together by focusing on the relevance and appropriateness of different approaches.*

I: The Results Tug of War

Development results is one of many areas where discussion and debate seem increasingly polarised. On one side of the results tug of war are those calling for more and better results, more rigour in analysis and more discipline in reporting. The failure of development, they argue, is basically about the failure to focus on results. ‘Modern management techniques’, especially those that are embodied by ‘results-based management’ are seen as the answer.

On the other side are those who argue for a ‘push back’ against this approach. Such reductionist approaches are seen as only suitable for certain kinds of development interventions, and that at their worst, these approaches inhibit the creativity and innovation needed to achieve results in the first place. The danger here is that we throw out the results baby with the reductionist bathwater (see here for a previous Aid on the Edge post on this).

What is increasingly evident is that, in the diverse and dynamic aid landscape we face today, all agencies attempting to genuinely strengthen accountability and learning face a number of common challenges. This is a preliminary list, I am sure readers will be able to think of more.

  • Data availability, coverage and quality are perennial problems
  • Participation and ownership - as Robert Chambers might ask: ’whose results count?’
  • Incentives and disincentives to use information and results, especially when they run counter to individual and institutional interests
  • Bureaucratic inertia: all too often results-related work is placed on top of and increases the already considerable bureaucratic and administrative burden on aid agencies, rather than simplifying and reducing it
  • Risks and fear of failure: How can we manage and be transparent about the different kinds of risk failures inherent to development projects & programmes?
  • Many conflicting imperatives: learning vs accountability, policy vs operations, domestic vs international

The key point is that these apply equally to both sides of the results tug of war. As a result, a lot of effort is being wasted, with problems being dealt with in entrenched intellectual silos rather than in a collective manner.

So what to do to move beyond the ‘tug of war’? I would argue that a first step would to think about how to bring the different results approaches together to establish a more constructive dialogue. What is needed is a more flexible and differentiated approach to results, one which takes account of the diversity of the development and humanitarian portfolio.

II: A Draft ‘Portfolio of Results’ Framework

What might such a portfolio-based approach look like? There are a number of useful approaches from academia, civil society and business strategy that can help here. These include Brenda Zimmerman’s simple-complicated-complex distinction, the Cynefin framework of Cognitive Edge, work done by Alnoor Ebrahim at Harvard University, work done by Eliot Stern on relevance of different approaches to impact assessment and finally a recent model put forward by Patrick Moriarty of IRC.

All of these suggest in their different ways that appropriate strategic approaches (and by extension, results approaches) need to be based on:

(a) the nature of the intervention we are looking at, and

(b) the context in which it is being delivered.

Reading across these approaches we can suggest a preliminary framework which may prove useful in bringing together different results approaches in a productive and mutually beneficial way.

First, imagine an agencies projects and programmes being distributed across a spectrum of the ‘nature of interventions’, placing relatively simple interventions on one end, and more complex issues, at the other.

Then let’s add in a vertical axes on context. Again, think of a spectrum, this time from stable / identical to dynamic / diverse.

This gives us a 2 by 2 framework for analysing and mapping different development interventions - in effect, this is a draft ‘portfolio of results’ framework. Where an intervention is positioned on this framework has implications for the kinds of results orientation we can take, as shown below.

In the top left corner of simple interventions in identical stable settings, is the Plan and Control zone – here ‘traditional’ results-based management approach, conventional value for money analyses and randomised control trials work well.

The bottom right corner of complex interventions in diverse, dynamic settings is what I have termed Managing Turbulence – here the philosophy is less ‘Ready, Aim, Fire’ (as in the Plan and Contol zone) and more ‘Fire, Ready, Aim’. Here we need to learn from the work of professional crisis managers, the military and others working in dynamic and fluid contexts.

In between is what I have called Adaptive Management, where either because of the nature of the intervention or the nature of the context, multiple parallel experiments need to be undertaken, with real-time learning to check their relative effectiveness, scaling up those that work and scaling down those that don’t.

III: Applying a Portfolio of Results Approach: A health-focused illustration

By way of illustration, let’s look at three health interventions – vaccines, HIV-AIDs, and rebuilding national health systems. I would argue that they could be distributed on the matrix something like this.

So if we are looking at simple interventions in a stable / identical environment, or what might be called the plan and control domain, randomised control trials, traditional cost-based ’value for money and results-based management approaches work great. Vaccines are perhaps the best example here. And as the ongoing MSF campaign on reforming GAVI suggests, a focus numbers and bean-counting can be of vital importance to ensuring effectiveness.

But we may find ourselves managing interventions that are more complex, in stable contexts. We can also think about situations where the intervention is simple but the context is dynamic. In both of these instances we may need to move away from blueprints towards a more adaptive management approach, trying out multiple parallel experiments and monitoring progress and rates of success and adapting to context. In HIV-AIDS responses, the optimal mix of responses is key and almost always locally determined (see previous Aid on the Edge post here). Also increasingly relevant are global malaria responses which need to adapt to the changing patterns of incidence and the evolution of resistance (ditto here).

Finally, in environments where our interventions are complex and the context is dynamic and diverse, we have to take a leaf out of the book of those who work in high risk environments – professional crisis managers, military and so on. Programmes to rebuild health systems, especially in fragile states, are a good example here. Here we need to be doing action research, real-time assessments and learning by doing.

This is not a rigid framework and there is overlap between the different areas. But different approaches to results can be shown to be more or less effective in different domains. In general terms, you can do a detailed RCT in the bottom right quadrant, but it may be a thankless task and not the best use of resources. You can do an RCT in the top right quadrant, but it could well prove to be a necessary but not sufficient condition for success. And so on.

(This also helps think about the concerns of one side of the tug of war – that there is a pressure to push development to the top left domain, and a widespread misapplication of the top-left tools for the other domains.)

Obviously this is a preliminary framework based on reflection and discussion, and is open to critique and debate. The key principle is that a more nuanced approach to results will have to be based on a systematic assessment of, at a minimum, our interventions and the context we are working within.

IV: Taking the Results 2.0 agenda forward

This kind of framework can also be used to think strategically about our overall portfolio of projects and programmes. How is our overall spend allocated between these ‘domains’? What are the implications for risk? I think there is a useful analogy with investment portfolio managers are used to diversifying their portfolios in order to reduce their exposure (see diagram below).

We urgently need to develop new ways of analysing the different elements of our portfolio. Through this we can start to unpack and understand the diversity of our efforts, and ensure we don’t take a ‘one-size-fits-all’ approach to results and all that entails.

There are a number of follow-on issues about how we might take this area of work forward.

  • We will need to refine or adjust the draft ‘portfolio of results’ framework, based on more in-depth analysis, discussion and debate. Of course, we may need something completely different to what is proposed here (all feedback, however critical is warmly welcomed!), but the key is that we need something to bring diverse constituencies and approaches together.
  • We need to think about which sectors are amenable to a portfolio type  approach to results, where we can pilot a ‘Results 2.0 process’ and we need to think about what new kinds of tools and methods might be required. I think health would be a great sector to start on.
  • Different kinds of interventions will need different kinds of information, which will call for different tools for managing this information. New kinds of tools and techniques will be necessary. Importantly, these should help to consolidate and simplify, rather than just increase, the reporting and administrative burden on the sector.
  • We urgently need to think about how this affects development communications, and how we can start to develop more sophisticated framing and messaging of positive and negative results, based on the different elements of our portfolio. This will be perhaps the hardest part of this new results agenda, as it means that we will have to tell our key stakeholders things like ‘we don’t know’, or even worse, ‘we failed’. This may mean riding with punches in the short-term. But this will also mean we will need to think hard about what different stakeholders expectations are, and how they can be best met. The overall legitimacy and sustainability of such efforts demands greater involvement of national governments, civil society and poor communities.

I want to close with this thought from a cross-country study of results-based  management looking at Western countries – that results are not an end in themselves, but are a means by which to establish trust in the system. I would add: and within the system.

Because we do so many different things in development, we have to do different things to earn trust of our diverse constituencies. (We may also have to accept that in some quarters, trust will never be established, but that is another story.) What we cannot do is move forward without finding ways of trusting each other, whatever our methodological or conceptual background and prejudices.

Bringing our diverse opinions and ideas together to test their relevance and appropriateness seems like an essential first step.

* This is the summary of a talk I gave at the June 2011 IDS-ODI roundtable on results with the UK Secretary of State Andrew Mitchell, revised following useful comments from participants. Special thanks go to Robert Chambers and Simon Maxwell for thoughtful and constructive feedback.

Fellow participants have also blogged on the meeting:

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The Humpty-Dumpty Problem

Posted in Biology, Evolution, Innovation, Knowledge and learning, Networks, Reports and Studies on June 23, 2011 | 1 Comment »

The latest issue of American Scientist features some superb reflections by Robert L Dorit on the limitations of reductionist thinking in the biological sciences. They have clear parallels for social sciences and, by extension, for social policy. Selected extracts are below.

Despite Descartes’ contention that we could not distinguish a well-made automaton of an ape from an actual ape (“were there such machines exactly resembling organs and outward form an ape or any other irrational animal, we could have no means of knowing that they were in any respect of a different nature from these animals”), the relations of parts to wholes in living systems is entirely different from that in machines—and most unclocklike. If anything, living systems consistently violate all of the criteria for reducibility. The number of elements that compose any living system—an ecosystem, an organism, an organ or a cell—is enormous. In living systems, the specific identities of these component parts matter. Unlike chemistry, for instance, in which an electron in a lithium atom is identical to an electron in a gold atom, all proteins in a cell are not equivalent or interchangeable. Each protein is the result of its own evolutionary trajectory. We understand and exploit their similarities, but their differences matter to us just as much. Perhaps most importantly, the relations between the components of living systems are complex, context-dependent and weak. In mechanical machines, the conversation taking place between the parts involves clear and unambiguous interactions. These interactions result in simple causes and effects: They are instructions barked down a simple chain of command.

In living systems, by contrast, virtually every interesting bit of biological machinery is embedded in a very large web of weak interactions. And this network of interactions gives rise to a discussion among the parts that is less like a chain of command and more like a complex court intrigue: ambiguous whispers against a noisy and distracting background.

As a result, the same interaction between a regulatory protein and a segment of DNA can lead to different (and sometimes opposite) outcomes depending on which other proteins are present in the vicinity. The firing of a neuron can act to amplify the signal coming from other neurons or act instead to suppress it, based solely on the network in which the neuron is embedded. The disappearance of a single species can stabilize an ecosystem or send it spinning into chaos, depending on (you guessed it) the network of interactions that surrounds that species. This extensive and subtle connectivity, which gives meaning to the behavior of the underlying components, turns out to be a consistent feature of living systems.

The recurrent evolution of these networks of weak interactions suggests that they may allow biological systems to incorporate information from the environment while also maintaining stability in the face of constant perturbation. This general feature of living systems also has clear methodological consequences for modern biology. Once this gossamer web is taken apart in search of the smallest components we can study, the process of putting it back together bears no resemblance at all to reconstructing a clock. Thus we find ourselves, early in the 21st century, with extraordinarily detailed descriptions of the components of many biological systems. But reconstructing those systems is proving to be a monumental and consistently surprising enterprise.

The promise of reductionism rested on the belief that an intelligent dissection of complex phenomena would not only yield progress, but would eventually reduce any problem to its component parts. Complexity, we naively hoped, was simply a by-product of incomplete understanding, an illusion that would fall away once the parts were fully understood. But this is the dirty little secret of contemporary biology: Despite our reductionist successes, the central conceptual problems of biology have not yielded to study. We have revealed the elegant workings of neurons in exquisite detail, but the material understanding of consciousness remains elusive. We have sequenced human genomes in their entirety, but the process that leads from a genome to an organism is still poorly understood. We have captured the intricacies of photosynthesis, and yet the consequences of rising carbon-dioxide levels for the future of the rain forests remain frustratingly hazy. We are, in short, the king’s horses and the king’s men: We stare at the pieces, knowing what Humpty should look like, but unable to put him together again.

How does one deal with such complexity? In living systems, Dorit argues, the component parts are always embedded in an intricate web of interactions. Such complexity appears at multiple levels of organisation as shown in the three illustrations below: a network of interactions among 1,700 different human proteins (top), a food web in a Puerto Rican rain forest (middle) and a neuronal network in a mouse brain (bottom).

Dorit continues:

the days when we could have blind faith in the power of reductionist deconstruction are over. Humpty lies in fragments. Fortunately for us, a new approach is taking shape to replace the seductive appeal of reductionism. We biologists may have bought a little too much of what Descartes had to sell, but as the limits of naive reductionism become more obvious, additional methods emerge for understanding the complexity of life. This new, interactionist perspective on living systems, with its emphasis on the interplay of parts, has benefited from new computational tools and experimental approaches. Whole new subfields in the life sciences, as well as productive interactions among existing disciplines, have emerged. Systems biologists, complexity theorists and newly minted biologists now attend as carefully to the ways in which parts come together as they do to the parts themselves. In the process, features of living systems that we once carelessly overlooked (or destroyed) in our haste to deconstruct now snap into focus. We are, for instance, beginning to understand that modularity and redundancy are inherent features of all levels of biological organization. These features characterize systems that are simultaneously resilient and capable of evolving.

Read the full article here.

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Success, failure and what lies between

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

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

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

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

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

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

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

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

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