Over at the excellent Global Dashboard, Alex Evans has a post reflecting on the things he and David Stevens called wrong (and less wrong), looking through their development and poverty lens, in the aftermath of the crisis. In a similar spirit, my sometime colleague Ian Christie sent me ‘Ten notes on the crisis’, representing his take on what we’d learnt about economics and politics since 2008. I thought they deserved a wider audience. And so, with his permission, I’m republishing his Ten Notes here. They start below the fold.

Ian Christie writes:

Foresight’s report on the future of food and farming covers the ground, but seems to be blind to disruptive change

The recent UK Government Foresight report on the future of the global food and farming system can’t be faulted for a lack of ambition. It takes on the whole of the global food system, and looks out to 2050. Much of what it says is valuable (and the supporting papers look to be a useful research resource), and this is to be expected, given the calibre of the advisers the project was able to draw on. But there are some telling gaps, and these largely come from a lack of decent futures work in the report. Some of these gaps have been pointed up by some of the other work that’s been published recently on food and its wider impacts. This will be a long post, so I’m going to split it into two parts, the first about the Foresight report, the second on its limits.

I was asked to review the Foresight report for meeting organised by the Food Ethics Council (I’ll link to their meeting report when it goes on their website), and this summary is based on that. The starting question for the project (not included in the report) is, “How can a future global population of 9 billion people all be fed healthily and sustainably?“, and the report summarises the characteristics of a well-performing global food market in 2050 as follows:

• the supply and demand of food balances, and food is affordable
• food prices are stable
• there is global access to food (and hunger has been ended)
• food has lower carbon emissions
• biodiversity and ecosystem services are maintained.

The challenge, of course, is getting there. And the starting point is not so good. Right now 925 million people suffer from hunger, and much agricultural land is being degraded by poor  farming practices. And it’s worth emphasising that one of the things I liked about the report was the repeated focus on the seriousness of hunger as a continuing problem, perhaps the result of development academics such as Lawrence Haddad being among the expert advisers.

Knowledge transfer could triple Africa’s yields

There are six important drivers of change, the report says, which won’t surprise anyone who’s looked at these issues: Global population increases; increasing intensity of individual demand; climate change; national and international governance of food systems; competition for resources (energy, water, land, energy); and changes in consumer values. And there are some relatively easy wins, it says, including reducing waste, reducing demand for ‘high impact’ foods – basically red meat – and improving governance and knowledge transfer. The last of these could double yields in Russia and triple them in Africa.

From a futures perspective, though, the work has weaknesses. This is a vast complex system, but there is barely a feedback loop to be seen in the report or its supporting papers. My futurist colleague Wendy Schultz, who also presented at the Food Ethics Council meeting, looked at the caveats to the work which modelled future outcomes (opens pdf). It has used some widely employed and well-respected models, but the authors note – in Table C4.2 – the assumptions they have not addressed because they involve uncertainties or are hard to quantify or model. These include climate variability, the impact of pests and parasites, human behaviour and values, technological change, and ‘high-impact, high-uncertainty [so-called] ‘black swan’ events’.

The rise of urban agriculture is a blindspot

In other words, pretty much all of those factors which might have the potential to create disruptive change have been excluded from the modelling. Elsewhere, the report mentions such factors without ever quite getting its teeth into them. It touches on changes in consumer tastes, but doesn’t seem to believe there’s much chance of a significant change in consumption patterns over 40 years (even in the face of the acute resource pressures it has described). And although there’s discussion of agricultural land, it seems to miss – completely – the rather strong trends we see, everywhere, towards urban food production and the blurring of the distinction between agricultural land and other productive space.

So what is to be done? “No single approach can meet all of the complex challenges… decisive action is needed across a wide front”. The report lists 12 priority areas (p34-37, opens pdf), which are not in an order of importance:

1. Spread best practice.
2. Invest in new knowledge.
3. Make sustainable food production central in development.
4. Work on the assumption that there is little new land for agriculture.
5. Ensure long-term sustainability of fish stocks.
6. Promote sustainable intensification.
7. Include the environment in food system economics.
8. Reduce waste – both in high- and low-income countries.
9. Improve the evidence base upon which decisions are made and develop metrics to assess progress.
10. Anticipate major issues with water availability for food production.
11. Work to change consumption patterns.
12. Empower citizens.

Is this enough? Perhaps not. Oddly, governance is a quick win, but not a priority area. The report states boldly right at the beginning that “Nothing less is required than a redesign of the whole food system to bring sustainability to the fore”, but nowhere starts to develop the tools which would help people develop a vision of what such a re-design might look like, as if it doesn’t want to say ‘boo’ to power. It seems to take a relatively uncritical view of global and open markets; indeed, whenever the politics of food threatens to break the surface, the report seems to move swiftly on. I’ll come back to this in the next post.

The picture at the top of the post is from the Anne of Carversville blog, and is used with thanks.

Suddenly, with the turn of the decade and the latest World Economic Forum opening, we’re awash with projections of how the world economy might look in 2050, produced by economists, and each as implausible as the other. Two, from HSBC and PwC (both open in PDF) offer similar (and improbable) views of a hugely expanded global economy to 2050.  But they also seem to assume that the future will be a lot like the past.

The headlines for the news coverage of the HSBC and PwC reports focused more on the country rankings and how they might change. If you didn’t see them, and have not read anything about the shifting world economy in the last decade, these projections basically involve a long slide for the European economies, and a steady climb for most of the Asian ones, with – inevitably – China emerging as the largest economy sooner or later.

And of course, as a story about the shifting patterns of the world economy, this much seems likely to be true. We are slowly rewinding towards the shape of the global economy in – say – 1800, before Europe and the United States reaped the benefits of the industrial revolution and expanded rapidly (and ensured that China and India didn’t). But what is much less plausible is the scale of growth of the overall economy that these two reports project; pretty much a world which is about three times as rich as it is today.

Models simplify the world – not always helpfully

I should pause here, because these rates of growth are no more dramatic than we’ve seen over the past forty years. Economic growth of 3% per year would get you to that sort of figure over the next 40. But, I’d suggest, those were very different times. The economic model being used to generate the numbers in these reports (it’s broadly the same model in both) was not built to handle the constraints of the energy and resources shortages we’re likely to see between now and 2050.

Models are representations of the world, built through simplification. They’re not the world itself. Both the PwC and HSBC models are based on the work of the macro-economist Robert Barro, although HSBC is more open about this, more open about its modelling processes, and more willing to justify the assumptions it’s made. (In fact the HSBC report is a much better piece of work, since it also addresses uncertainties, of which more later.) Barro’s work, based on data from 1960 to 1985, uses the starting level of GDP per capita (since it is easier for poorer countries to grow faster than it is for richer ones) and assumptions about demographic trends, the quality of human capital and economic governance, to project a country’s likely rates of economic growth over time. (There’s an academic paper to be found here, in pdf). For its paper, HSBC re-tested the model against data from 2000-2010, and found it a ‘satisfactory’ fit.

Following the trends all the way to a single point prediction

But there are two howling great issues with this. The first is that, at heart, this is a rather simple form of trends analysis, in which past trends are projected forwards. To borrow Ged Davis’ phrase, ‘a trend is a trend is a trend, until it bends’. And from a futures perspective, the future doesn’t get credible until you start to see the bends.

In an interesting post on the HSBC projections for Poland, by way of example, World Bank economist Marcin Piatkowski effectively makes this point. He takes exception to Poland’s relatively poor projected performance, based on its low birthrate and historically low immigration levels. Both, he argues, are likely to change (the birthrate is already increasing, and public policy is changing to encourage this); actual outcomes are more complex and more uncertain than the Barro-based model suggests. In fact, we have a classic forecasting problem here: both PwC and HSBC have produced a set of single point predictions for national economies over a time period when uncertainty will be far larger.

The second problem is more serious. In treating each country’s long-term growth as if it is the product of its own internal variables, you lose sight of the whole system. And given the prognosis for energy, water, resources, and food out to 2050, without even starting on climate change, it seems unlikely that  the planet will have the carrying capacity to support a global economy which is three times larger than the present, at least without the kind of transformational change in technology, systems and infrastructure which would take a lot longer than forty years to achieve. In other words, the second thing that is missing is the feedback loops. The future is always more crinkly than it looks.

‘Our projections are based on a rosy backdrop’

The HSBC acknowledges this, weakly. The sustainability team gets a say (although seems to be under instructions not to tweak the tail of the economists too vigorously), and manage to point to the current level of ecological deficit – even before the resource demands implied by the global growth projections are factored in. Another short section admits that, “In a nutshell, our projections are based on a rather rosy backdrop – everything is going right, governments and policymakers are doing the right thing.” And then some. It’s not so much ‘base case’ as ‘best case’ (my words, not theirs).

So what to make of these economists’ projections of the future? In an article in The Scotsman, re-published by Open Democracy, Gerry Hassan argued that – while claiming to portray radical shifts in the world economy – they were infused by a sense of stasis. Like many futures exercises (look at the videos of any World’s Fair), this is a projection of the present.

The problem though with ‘The World in 2050’ … [is] the underlying philosophy behind the whole exercise. This can be seen as a progressive panglossian view of the world, of enlightenment globalisation, whereby the world becomes freer, wealthier and healthier. The world’s many poor, in China, India and the Far East, and eventually Africa, are slowly pulled out of hardship. Globalisation poses itself as a liberation movement spreading freedom, hope and opportunity. This version of the future is of course a story of today.

As I’ve observed elsewhere (in a paper written with Hardin Tibbs) “the dominant feature of modernity is that it is always expanding”. These are, in essence, modernist stories, being told in complex times. Gerry Hassan is being too kind. These are stories of yesterday.

The image of the 1939 World’s Fair is one of many fine photos on the website Before Pearl Harbour, and it is used with thanks.

Business risk is usually an exercise in corporate power. But your own business assumptions are about to become a risk in their own right.

A few months ago, I wrote about risk – suggesting that the ‘discipline’ of risk management tended to focus on risks which were (a) understood and (b) for which probabilities could be estimated, and that this led to far too narrow a view of risk. In particular, this meant that companies were usually very poor at assessing their blindspots. This second post has taken longer to write than I expected, but in this one I’m going to look at the factors which mean that companies tend to remain blinded by their blindspots.

In the first post, I used a couple of 2×2 models: one by Gary Kass, adapted fromthe work of Andy Stirling, which assessed risk by ‘knowledge of outcome’ and ‘knowledge of probability’, and a second one from Sohail Inayatullah, which explored what we know, and what we don’t know. I’ve put these in here as thumbnails by way of reference (click on them to make them larger).

But this leaves an important problem which is insufficiently addressed in organisational research or futures work: an organisation may become aware of an “unknown unknown” (thereby moving it to a “known” space) but precisely because such an insight is alien to their culture and worldview, they are unable to act on it. Either it ‘does not compute’, or it is understood well enough but is inconvenient. Organisations are not rational decision-makers, any more than individuals are.

And frankly, the evidence is depressing. It’s worth spelling out some examples. There’s a compelling account in Flirting With Disaster on the pre-launch discussion about the O-rings whose failure doomed the Challenger. This was a “known unknown”, an engineering problem whose outcomes were known but whose probability was disputed. It was a “complicated” problem rather than a “complex” one, to borrow some language from Dave Snowden’s Cynefin model, which is a useful tool to think about these issues (although this is a discussion for another day).

Recipes for failure

There was political pressure for the launch to proceed, largely from within NASA, which was worried that it was losing the battle to fund the space programme But there were internal voices who argued strongly that the O-ring seals would crack or malfunction at low temperatures, but they were, effectively, bullied into silence.

In the still incomplete story of the Deepwater Horizon disaster, we have already seen similar claims. BP, it is alleged, put pressure on its contractors to take some shortcuts because drilling was behind schedule and every day of delay was costing it another $750,000. The combination of financial pressures and technical over-confidence is a sure recipe for failure. We saw the same combination at work in the banking crisis, with people who pointed out risks being marginalised, victimised, or fired. As James Kwak wrote,

The problem is that there is a systematic bias within these companies against certain assessments and in favor of others. That is, the guy who shouts, “Danger! Danger!” will be ignored (or fired), and the guy who says, “Everything’s fine, the model says disaster can strike only once every hundred million years” will get the promotion — because the people in charge make more money listening to the latter guy. This is why banks don’t accidentally hold too much capital. It’s why oil companies don’t accidentally take too many safety precautions. The mistakes only go one way.

Managing risk by increasing resilience

In a co-written article (downloads pdf) in the Harvard Business Review, the ‘Black Swan’ author Naseem Taleb argues that organisations need to stop trying to assess probabilities of risk and disastrous events, and instead reduce their vulnerability to them: in other words, to increase their resilience. As they wrote, “Risk management, we believe, should be about lessening the impact of events we don’t understand”.

This is also the thesis of a recent paper by Bernard Lietaer, Robert Ulanowicz and others on the future of the financial system. Drawing on work on ecological systems, it argues that stable systems are on a point of balance between resilience and efficiency.  Too much resilence, and you stagnate; but too much efficiency, and the system becomes brittle. It’s also worth noting from the article that the point of balance is twice as far from the ‘efficiency’ end as it is from the ‘resilience’ end; but most modern corporations are the other way around. And, they argue, there’s a relatively small ‘window of viability’ either side of the point of balance.

But this requires systemic change, because organisations will only behave like this if they are incentivised to do so.  Some people argue that this is down to the Board. But the recurring sound of the large business in the early 21st century is still Chuck Prince’s notorious remark to the Financial Times, “As long as the music is playing, you’ve got to get up and dance.” And don’t even notice that you’re on the edge of a cliff.

‘A “Black Swan” isn’t an alibi for stupidity‘

Certainly during the long globalising boom, and so far since the financial collapse as well, boards have mostly been engaged in supporting short-term maximising behaviour and a certain amount of agent-type rent-seeking (for example the cosy “remuneration clubs”that are a feature of modern corporate life). And on the subject of notorious remarks, BP’s Tony Hayward’s description of Deepwater Horizon oil disaster as a ‘Black Swan’ was beyond irony. But there is a deeper point here: that one of the ways in which Taleb’s Black Swan idea has been used is as cover for a lack of foresight. As Alex Pang once noted, “The idea of a Black Swan shouldn’t be an alibi for stupity”. But as Pang observes, “The ‘nobody could have predicted’ defense … insulates leaders and experts from accountability for their failure”.

And the nature of calamitous accidents allows for such myopia. As Marc Gerstein writes of the ‘friendly fire’ which destroyed two Black Hawk helicopters over northern Iraq:

Latent conditions in technology and organisation can exist for a significant time, waiting for circumstances to line up that create a calamitous accident. … A systemic perspective is essential to shed light on the complex interplay of actions and how the loss might have been prevented.

Pricing blindspots

This is the significance of the $20 billion Gulf compensation scheme, and the subsequent government lawsuit. Unlike previous legislation, which capped liabilities arising from pollution, a plausible price has been attached to the “externalities” arising from BP’s exploration disaster. It’s also been made clear that the fund doesn’t represent a ceiling to potential liabilities. For the first time, blindspots are seriously expensive.

At that price, it’s large enough for boards and managers to worry about it. Some of the discussion about the financial sector, though nowhere near enough, has been about how to ensure that financial institutions can cover the costs of their failures, rather than leaving the state to step in. Capitalism has to have disincentives as well as incentives; otherwise price signals are incomplete, information one-sided and markets are not effective at allocating resources: so what does a Compensation Fund targeted at Wall Street or the City of London look like?

As systems become more complex, both risks and consequences of failure become more acute. The BP Fund creates both a precedent and a principle, and it is this. Corporate risk assessments which can’t tell you what your current business assumptions ignore or preclude, and what you’d have to do to to effect a recovery, are about to become a business risk in their own right.

BP’s oil disaster is partly the result of approaches to risk which lead us to believe we know more than we do.

It’s hard to know where to start with the BP oil disaster. Commentary has been gushing out almost as quickly as oil. We know the scale of the pollution, and have read ecologists who say the conditions are unlike any other seen on earth, certainly in the anthropocene period. Dark humour is one response; angry satire is another (The Onion: ‘Massive Flow Of Bullshit Continues To Gush From BP Headquarters‘).We have a good idea that BP’s conduct over the drilling was somewhere between careless and reckless (and that sooner or later a court is likely to decide on which), and that regulatory agencies were compliant or ineffective. One area that seems to deserve more thought – especially from a futures perspective – is the way in which essentially man-made disasters such as this are to a significant extent produced by a limited set of ideas about risk, both the way it gets assessed and the way it is managed.

In an interview this month, BP’s chief executive Tony Hayward called it a “low-probability, high-impact accident”. Elsewhere he’s quoted as saying it’s a “one in a million” chance. For the moment we’ll park the word “accident”, which implies that there’s no culpability anywhere along the line, or the finger-in-the-air “odds”, which are themselves at odds with a surprisingly long list of deepwater drilling near misses in US waters. But language like “low probability, high impact” would put the event in the territory of what (at least historically) futurists would have regarded as “wild cards” – events which are quite unexpected but have huge consequences.

We know less than we think about risk

Why this is problematic is because it implies more knowledge about the nature of risk than we usually enjoy. The diagram below is taken from a presentation by Gary Kass of Natural England to the UK Horizon Scanning Centre’s ‘FAN Club’. [Update: He adapted it from the work of Andy Stirling at SPRU: opens pdf.]

The axes are straightforward enough: vertical is ‘knowledge of outcome’ running from ‘low’ at the bottom to ‘high’ at the top, while horizontal is knowledge of probability, running from ‘low’ at the left to ‘high’ at the right.

The whole edifice of the risk management business is squashed into one quadrant in the top-right corner (which leaves an awful lot more that we know little or nothing about). Where we know about probability, but not outcome, then we move into the realm of ambiguity (and conventional risk calculations don’t work). In the top left, where we have some understanding of possible outcomes, our knowledge of probabilities is limited.  And in the bottom left, we know nothing, either of outcomes or probabilities. We are ignorant.

But because “risk management” has become such a dominant idea and set of practices in the late 20th century, for reasons which I’m not going to try to disentangle here, issues which shouldn’t be in the domain of the risk managers are squeezed into it by organisations. Hayward would say that BP’s oil disaster is an upper right quadrant event because he knows both probability (low) and impact (high). In fact, the first order outcomes (oil in sea threatening ecosystems, technical challenges with managing the rig failure) are at best in the area of uncertainty. The potential second order outcomes (economic destruction of other businesses dependent on the Gulf, possibility of ‘black rain’ in the hurricane season) seem to have been in the bottom left quadrant, where ignorance resides. Or to put it another way, the relatively limited analytical tools of the risk management business are extended to areas where they are all but useless.

Knowing and not knowing

So we end up using the wrong tools for the task at hand. As the amount we can reasonably know about a possible future question diminishes, along with possible understanding of the outcomes, we are in the realm of ontology and epistemology, of being and knowing. And Gary Kass’ model maps pretty well onto the model developed by Sohail Inayatullah, about what we know and don’t know, in his book Questioning the Future.

The ‘realm of risk’, above, corresponds to ‘What we know we know’, while the ‘realm of ignorance’ corresponds to ‘What we don’t know we don’t know’. (Pause for Donald Rumsfeld moment: although he was mocked it wasn’t a foolish thing to say).

Of course, organisations tend not to have a Director of Epistemology on the Board. Instead, current tools, and dominant models of the world are deployed even as some start to realise that they are no longer fit for purpose. This is because the traditional model of risk management – over-professionalised though it is – covers only one quadrant of these four.

Black swans and blindspots

And this inevitably takes us to the realm of the ‘black swan’, Nicolas Taleb’s construction for shocks and surprises which appear as if from nowhere, and which was first described in his book Fooled by Randomness. I’ve criticised his concept before. Most so-called ‘black swans’, such as the perennially cited 9/11 attacks, turn out to have form; similarly, the banking collapse of 2007-08 was well trailed, but not by bankers.In short, in futures work, there are inevitably trails of clues anticipating the discovery of a black swan, and a whole set of methods (such as emerging issues analysis) to help interpret the clues. ‘Black swans’ are surprises only because people have been looking in the wrong direction, or not looking at all.

In its latest incarnation, the idea of the black swan now seems to be about ‘low impact high probability events’, which seems unhelpful; we have methods for thinking about these already, even if they are unreliable. Instead, the value of the black swan should be that it focusses our attention on our blind spots, on the way in which our assumptions about the world obscure us to parts of it which don’t fit with out worldview – or with our self-interest. There are always more things in heaven and earth than are dreamt of in any of our philosophies.

Instead of thinking about “risk” – which almost always involves thinking about the familiar, and in familiar ways – we need to be thinking about corporate ignorance and the things that we don’t know that we don’t know.

This is the first of two posts on this subject. The second will appear here shortly. The picture at the top of the post, of the Deepwater Horizon rig, is from the Gulf of Mexico energy news service OCS BBS, and is used with thanks.

The row over whether the International Energy Agency has or has not nurdled its oil data to (a) prevent financial market panic or (b) appease the Americans or (c) neither of the above, is interesting but a bit of a sideshow. What’s more interesting is how fast the notion that ‘peak oil’ is imminent has moved from being a contested minority view to being mainstream.

The IEA’s been stung by suggestions that its research lacks independence, and has taken the unusual step of publishing material normally only available to subscribers (opens in pdf) to try to demonstrate it. But the assumptions embedded in its projections tell their own story. By 2030, it suggests, the majority of world production will come from currently undeveloped (or not yet discovered) oilfields, even though the rate of discovery is falling and it takes typically 30 years to move from discovery to peak production. And unconventionals – such as the environmentally disastrous tar sands – grow, a lot.

The IEA’s view is not shared by other recent reviews of energy outcomes. The Oil Crunch, published this time last year and produced by a group which included a cross-section of British businesses, concluded that the problem of declining oil supply could hit Britain as early as 201, with serious consequences for the economy. (News story here, report downloads in pdf here).

Just a month ago the NGO Global Witness produced a report – “Heads in the Sand“, based on official data, which concluded that by 2015 there could be a substantial daily shortfall in oil supplies. It summarises the argument as follows: the size of oil discoveries has been trending downwards for forty years, and production has exceeded discovery since 1984; 17 of the world’s 20 largest fields are producing at below their historical highest levels; conventional oil production failed to grow between 2005 and 2008, despite high demand and high prices; and the IEA projects a drop in production from existing fields of almost 50%. To hit its 2030 projection will require new capacity which is six times the current Saudi production levels to be developed and brought onstream. [Update: A thorough but high level analysis of the background data and the implications for the global economy has been posted at The Oil Drum since I wrote this blog post.]

Looking for miracles

It’s perhaps not surprising that investment banker Matthew Simmons, an energy sceptic, had this to say about the IEA’s general projections:

What the new IEA leadership is warning is that declines in mature super giant fields will make it necessary to add the equivalent of four new Saudi Arabias over the next two decades just to keep demand flat. They intentionally avoided saying “obviously, this is impossible.”

The IEA was set up in the wake of the 1970s OPEC crisis to provide the leading energy consuming nations with reliable information about oil reserves, production, and consumption, and to advise on policy. Critics have long accused it of being captured by oil industry assumptions, and closed to critiques of these. In turn, its worldview – generally repeating industry prognoses that there would be no pressure on oil supplies until 2030 – has over-influenced policy-makers, especially in the UK, which is one of the reasons why Britain’s energy policy is in poor shape.

The current production forecasts remind me of Pierre Wack’s story of Shell’s ‘preferred’ 1970s oil scenario, the one that most chimed with the company’s worldview. It would, Wack observed, take ‘Three Miracles’ to come about. Miracles will also be required for the IEA’s projections to come about in practice, and indeed, much the same miracles – new reserves rapidly found and brought into production, large suppliers willing to produce at high levels, and no problems on the supply side.

The global energy unit at Uppsala University in Sweden last week produced its own 2030 Energy Outlook, in which it reckoned that  global production in 2030 was more likely to be 75 million barrels per day than the IEA’s 105 mpd (thanks to the Real World Economics Review blog for the reference; there’s also a handy couple of charts which show their competing production assumptions). As the Uppsala group says in its conclusion, “All our projections imply that the world oil production by 2030 will be lower than today.” This is one of the reasons why Kjell Aleklett, director of the Global Energy, unit has described the IEA’s report as a “political document”. But policy – certainly not in the medium-to-long-term, is not best-served by politicised research; in futures terms, such behaviour simply creates blindspots.

See also: Challenging Britain’s energy assumptions

Every so often you notice a story that flashes a hazard light about the prevailing wisdom about the way the world works. Thus it was last week, reading Jack Schofield’s Guardian article about the limits of Moore’s ‘law’, the long-standing projection by Intel co-founder Gordon Moore that the number of transistors that can be placed on an integrated circuit will doubled every two years (effectively doubling performance against price in that time). It’s been suggested before that the microprocessor manufacturing process will reach physical limits. Schofield’s piece was on the increasing investment cost of processor manufacturing plants. One of the consequences is likely to be the end of the exponential growth in computing performance which has underpinned the speed and scale of the information and communications technology revolution.

While not wanting to write a long post, it’s worth making a few points.

• The growing cost of the fabrication plants has always been the ‘dark side’ of Moore’s law – the performance-to-price ratio of chips may double every two years, but the cost of building the production plants keeps on growing (if not quite so quickly). Nonetheless, the numbers are now quite large; Intel is spending $7 billion upgrading its fabrication plants;  Global Foundries, an AMD spinout, is spending $4.2 billion building a new fabrication plant in upstate New York, helped along by a $1.2 billion incentive from the state. As well as production costs, design costs are also increasing.

• The cost grows because the engineering becomes more complex as transistors shrink in size. The scale is staggering, but the current generation of chips is 45 nanometers (nm) across – or 45 billionths of a metre. The next generation will be 32 nm, with 22 nm following along behind it. By way of a yardstick, the Intel 8088 chip, in 1982, was 3,000 nm (or 3 m icrons), while atoms are about a nanometre across. As Charlie Stross said in an interesting discussion of this, “it’s hard to see how we can miniaturize our integrated circuits below the 10nm scale”. Since increasing miniaturisatioon is the basis of current sector competition, there will be a big shake-up in the industry as we reach these limits.

• I’ve written here before on the work of Carlota Perez, who has a model of technological innovation going back to the Industrial Revolution which sees industrial innovation progressing in waves of around 50 years, with each wave (this is a Schumpeterian model, not from Kondratiev, by the way) progressing through a cycle of rapid innovation, leading to irrational investment exuberance (with speculative bubbles thrown in) to a shift to slowing rates of growth and more normal rates of return. her model has the current ICT wave starting in 1971, and the characteristics described above, with cost and complexity reducing the rate of return, fits her model pretty well.

• Of course, this doesn’t mean that there won’t be innovation in the sector. But it does mean that innovation is more process driven, and may require suppliers to think about what customers need and value, rather than simply pushing for ever greater density of transistors.

The final observation here is that maybe the Moore’s law curve wasn’t exponential after all – maybe it was just a sigmoid S-curve with a particularly steep gradient on the way up. Without getting into arguments here about The Singularity (and thereby revealing myself to be a simple-minded type whose brain is not sophisticated enough to understand the implications of exponential growth) it’s worth just mentioning that Theodore Modis has been over this ground in some technical detail. As Modis observes, “Nothing in nature follows a pure exponential. All natural growth follows the logistic [S-curve] function, which indeed can be approximated by an exponential in its early stages”. Equally, systems theorists would observe that in general, in any system, a balancing loop will eventually slow the acceleration created by a reinforcing loop.

And that’s what seems to be happening here. The relationship between reducing the scale (from 45nm to 22nm and beyond), and its cost, isn’t linear; it gets progressively more expensive as scale reduces and density increases. As a result, the cost of the fabrication plants and chip design accelerate to a point where they are no longer viable investment propositions. Two connected balancing loops change the underlying dynamics of the computer business – and change our assumptions about the continuing rate of technological change.

The image at the top of the post – showing processing power performance on a logarithmic scale, is from Computer Measurement Group, and is used with thanks.

I first came across this chart which working on a set of scenarios for Foresight’s Brain Science, Addiction and Drugs project a few years ago – perhaps not surprisingly since Professor David Nutt. one of its creators, was a project adviser. (The other creator is Colin Blakemore, a former Chief Executive of Britain’s Medical Research Council.) The version here is from the Royal Society for the Arts’ Commission on drugs, published last year.

If the UK had a sensible drugs policy, there would be a reasonable correspondence between the colours and the ‘harm’ levels (the scores along the left hand edge); A-classification blacks would congregate at the left hand side, unclassifieds towards the right hand, with perhaps a few where their ‘external’ costs had changed their social impact and therefore their classification. The actual correlation is little better than random. The reason – several decades of knee-jerk political responses to drugs issues by successive Home Secretaries. Anthony Barnett has a wry post about this over at Our Kingdom. And, for the record, cannabis consumption in the UK is declining, according to the British Crime Survey, even if some strains are getting stronger.

I haven’t yet read the book, so this post is based on sampling the reviews. The data – as best as can be assembled – suggests that organised crime accounts for 15-20% of the global economy. Other data points (from the Sunday Times’ review):

The EU estimates that identity theft generates £25 billion a year in profits worldwide. Fake goods, from aircraft spares to spurious Harry Potter stories, are worth up to £250 billion annually. China is responsible for 60% of the market in stolen intellectual property.

The causes: a combination of opening up the global economy in the 1980s and the end of the cold war, in particular the collapse of the Soviet Union and the very selective deregulation of the Russian economy. As Anthony Andrew explained (in The Observer):

All price restrictions were removed by government, except those of Russia’s natural resources: oil, gas, diamonds and metals. Overnight, a vast number of Russians were impoverished, while a tiny minority was able to buy up vital commodities at up to 40 times less than their global market price. ‘This process of enrichment,’ Glenny writes, ‘was quite simply the grandest larceny in history and stands no historical comparison.’ In turn the oligarchs required protection, and jailbirds and former KGB agents alike moved into the lucrative if deadly business of the ‘kryshy’ protection rackets, or ‘armed entrepreneurs’.After the failure of the Soviet Union, in which the law was subordinate to shifting party requirements, Russia effectively privatised crime.

$5bln passed through the Israeli economy from eastern Europe following the Communist collapse; an estimated $20bln went to Switzerland. But other oligarchic states do well as well; 150-250,000 barrels of Nigerian oil go missing every day (at around $100 a barrel this all adds up to a tidy amount pretty quickly). But the drugs trade is the driver; 70% of the black economy is fuelled by drugs money.

David Goldblatt (in the Independent) picks up on the globalisation point:

Our misfortune is that the explosive growth of this shadow economy coincided with the deregulation and globalisation of financial markets. … On every page Glenny lays bare human mendacity and greed, failing or failed police forces and judicial systems. But, just as surely, he takes us to the insatiable maw of the North as consumer of cheap labour, cheap sex and cheap drugs. He makes us see a grotesque irony of globalisation; that the very people made rich by the global economy return to its illegal shadow to sate the insecurities and neuroses of their opulence.

The losers, of course, are also hidden in the shadows: Chinese migrant labourers and their gang-masters, the trafficked Moldavian prostitute in Tel Aviv. All of this seems to me to be something of a blindspot in many readings of the global economy, the State of the Future notwithstanding. And even when we see the symptoms, as we did for a moment after the Morecambe Bay tragedy, the response is muted. Any effective response would have to be political, as John Dickie argues in the Guardian:

McMafia amasses an irresistible weight of evidence to support some simple but telling conclusions. Transnational crime is a far bigger threat than terrorism. Offshore banking havens should be shut down. Capitalism without sound law enforcement is banditry. Tooting cocaine, visiting prostitutes and hiring untaxed immigrant maids are no mere peccadillos. And for any politician who hankers after immigration controls or yet another war on drugs, Glenny has a blunt message: it’s the black economy, stupid.

And probably transnational as well. It’s probably not “misfortune” (and I suspect Goldblatt was being ironic) that the growth in organised crime has gone hand in hand with the financial deregulation that was an essential component of the latest wave of globalisation. They go hand in hand. In structural terms, organised crime operates in the gap between where “fair markets” have failed, and where politics and jurisprudence are not accountable. The Salon review has a relevant quote from the book:

“For globalization to work, the world needs to be a level playing field: the West has to stop its protectionist practices and it has to reassess its resistance to the free, or at least a freer, movement of labor. The developing world in turn needs to address the issue of corruption and the strengthening of the rule of law. This raises the awkward question of global governance and standards that might be compatible across the world.”

The Times has a podcast of Glenny talking about his research.

Almost 1.19m cars were shipped abroad in 2007 – the highest figure, according to the SMMT website, since 1987 (as far back as the data on the site shows). The story is slightly more mixed than the headline suggests, since overall UK production was down (a much higher proportion went overseas). At the same time, though, just over 215,000 commercial vehicles were produced – the highest figure for ten years.

When I first saw the headline, I was slightly sceptical; first instinct was that there must have been better years back in the 1970s or so. Looking at the SMMT data behind the headlines, it is striking that – compared to the early ’90s – UK car production is up about 20%, and exports are far higher. I wasn’t able to find earlier data, but it’s possible that between the ’60s and mid-80s production may have been higher, with more UK sales. It’s also possible that – given much lower levels of affluence – car sales were simply lower generally across Europe.

The reasons: according to Paul Everitt of the SMMT, are a combination of the stronger Euro against the pound, well-designed vehicles from some famous British (if no longer completely British-owned) marques – including Rolls-Royce, Bentley, Land Rover, and the Mini, and also investment by foreign owned plants. Nissan, for example, has has to divert UK exports of Nissans to Europe instead.

One of the issues this raises is about the way policy assumptions about the future shape of the economy influence the outcome. If you hitch your economy to the financial services sector, it follows that you’ll prefer  higher interest rates and a strong currency – both of which are bad for exporting industries. This then reduces investment and skills. As Everitt said:

“Looking at areas [of the economy] where there is now concern, these are the areas we have been told were the future. We were told that this future was with service industries and we did not need to worry about manufacturing. I think it’s time we looked once again at the value of manufacturing.”

Related posts: Signs of deep shift in the economy