Economyths E-Book

David Orrell

• Herausgeber: Icon Books
• Kategorie: Geisteswissenschaft
• Sprache: Englisch

Seitenzahl: 442

Veröffentlichungsjahr: 2010

Beschreibung

From the inability of wealth to make us happier, to our catastrophic blindness to the credit crunch, "Economyths" reveals ten ways in which economics has failed us all. Forecasters predicted a prosperous year in 2008 for financial markets - in one influential survey the average prediction was for an eleven per cent gain. But by the end of the year, the Standard and Poor's 500 index - a key economic barometer - was down 38 per cent, and major economies were plunging into recession. Even the Queen asked - Why did no one see it coming? An even bigger casualty was the credibility of economics, which for decades has claimed that the economy is a rational, stable, efficient machine, governed by well-understood laws. Mathematician David Orrell traces the history of this idea from its roots in ancient Greece to the financial centres of London and New York, shows how it is mistaken, and proposes new alternatives. "Economyths" explains how the economy is the result of complex and unpredictable processes; how risk models go astray; why the economy is not rational or fair; why no woman (until 2009) had ever won the Nobel Prize for economics; why financial crashes are less Black Swans than part of the landscape; and, finally, how new ideas in mathematics, psychology, and environmentalism are helping to reinvent economics.

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CONTENTS

INTRODUCTION

Chapter 1: THE ANARCHIC ECONOMY

Chapter 2: THE CONNECTED ECONOMY

Chapter 3: THE UNSTABLE ECONOMY

Chapter 4: THE EXTREME ECONOMY

Chapter 5: THE EMOTIONAL ECONOMY

Chapter 6: THE GENDERED ECONOMY

Chapter 7: THE UNFAIR ECONOMY

Chapter 8: THE OVER-SIZED ECONOMY

Chapter 9: THE UNHAPPY ECONOMY

Chapter 10: THE GOOD ECONOMY

NOTES

RESOURCES

ACKNOWLEDGEMENTS

For Beatriz

David Orrellis an applied mathematician and author of popular science books. He studied mathematics at the University of Alberta, and obtained his doctorate from Oxford University on the prediction of nonlinear systems. His work in applied mathematics and complex systems research has since led him to diverse areas such as weather forecasting, economics, and cancer biology. His work has been featured in theNew Scientist,World Financeand theFinancial Times, and on BBC Radio. He lives and works in Oxford.

INTRODUCTION

Every dogma must have its day.

H.G. Wells (1866–1946)

The year 2008 was going to be a prosperous one for the financial markets, according to forecasters polled by Bloomberg.com at the start of the year. None foresaw a loss, and the average prediction was for a gain of 11 per cent. They were blissfully unaware that one of history’s biggest financial earthquakes was already taking shape beneath their feet. By year-end the S&P 500 index was down 38 per cent, $29 trillion had slipped through the cracks appearing in global markets, and many of the foundations of the world economy lay in ruins.1

The credit crunch had a number of phases, but perhaps the pivotal event was the collapse of the financial services firm Lehman Brothers in September 2008. With over $600 billion in assets, this was the largest bankruptcy in US corporate history. Lehman was also one of the key nodes in the financial network, and its extinction sent the crisis into a new and extremely dangerous phase. Many feared that the entire global financial system would break down completely. That didn’t happen, and markets eventually recovered from their near-death experience, but the aftershocks of those events are still being felt around the world.

The failure of economists to predict the credit crunch or the ensuing world recession was not atypical. As shown later, financial forecasts have an extremely poor track record of success, even when based on sophisticated mathematical models. This time, though, not only did the models fail to predict the crash – they actually helped cause it.

In the years preceding the crash, financiers had become increasingly reliant on quantitative mathematical models to make their decisions. Even if models couldn’t predict what exactly would happen in the future, they were supposed to be able to calculate risk. For example, in order to figure out how much risk a package of loans incurred, they needed only to make a statistical calculation using a simple formula or risk model, based on standard economic theory. This appeared to work well – so well that quantitative analysts began to use the models to take bigger and more sophisticated bets.

Even before the crisis was in full swing, though, there were signs that the models were failing to capture the true risks of the economy. On 11 August 2007, a year before Lehman Brothers went bust, some unexpected market turbulence brought on by a decline in US house prices led one of their employees to remark that ‘Events that models predicted would happen only once in 10,000 years happened every day for three days.’2

While that sounds most unusual, the chief financial officer at Goldman Sachs went even further: ‘We were seeing things that were 25-standard deviation moves, several days in a row.’3To unpack that statement, a 25-standard deviation event is something that is not expected to happen even once in the duration of the universe – let alone each day of a week.

You don’t need to be a mathematician to see that the models that lay at the core of the world financial system had something seriously wrong with them. But how could so many highly-paid experts have turned out to be completely mistaken about the workings of the economy? As Queen Elizabeth said on a visit to the London School of Economics: ‘Why did no one see it coming?’4

Storm warnings

Actually, not everyone was as surprised by the crisis as were the quantitative analysts and their mathematical models. As early as 2003, the investor Warren Buffett described the complex products known as derivatives, which played a key role in the credit crunch, as ‘financial weapons of mass destruction’. The same year, well before the collapse of Lehman sent a tsunami of destruction through the banking system, the network scientist Albert-László Barabási warned of the potential of ‘cascading failures’ in the economy.5Even central bankers were heard to muse that the financial system might be less stable than it seemed. In January 2007 Jean-Claude Trichet, the European Central Bank president, observed that ‘We are currently seeing elements in global financial markets which are not necessarily stable … we don’t know fully where the risks are located.’ Some, such as author Nassim Taleb and economist Nouriel Roubini, were more specific in their warnings; however, their voices were ignored or even ridiculed in the rush for profits that characterised the boom years.6

As with preceding crashes, the causes of the credit crunch have been much analysed and debated. The obvious lightning rod for criticism was of course the bankers themselves, who were earning fabulous salaries, and even more fabulous bonuses, for taking risks that turned out to have cataclysmic consequences for the real economy when the bets went wrong. Other culprits were the regulators, who failed to keep up with the pace of innovation in financial products; the American homeowners who took out subprime loans they could never afford to repay; the central banks, who (Trichet’s comments aside) often seemed to be in denial about the extent of the problem; and the economists who designed the flawed mathematical models in the first place.

This still leaves the question of how so many people in the financial industry could have been misled about the risks they were running and unaware of the dangers. The reason, I believe, is that the fundamental assumptions that form the basis of economic theory are flawed. This means that not just the mathematical models, but the actual mental models that economists have of the economy are completely wrong.

This problem goes well beyond the calculation of financial risk. The main problem with our economic system is not that it is hard to predict, but that, despite its enormous productivity and creativity, it appears to be in a state of ill health. The economy is unfair, unstable, and unsustainable. But economic theory has no way of dealing with these issues either.

The economy is unfair.Economic theory is supposed to be about optimising the allocation of resources. However, the reality is that the rich really do get richer. In 2009 one hedge fund manager earned over $2 billion, while over a billion people earned less than $1 a day.7That’s a strange way to allocate resources.

The economy is unstable.According to theory, the ‘invisible hand’ should keep asset prices at a stable level. But in reality, assets including oil, gold, and hard currencies are subject to enormous gyrations. In late 2007 the price of oil surged to over $140 a barrel, then plunged to under $40, all in the space of a few months. Oil is often called the lifeblood of the economy, but our own blood supply is much better regulated. For a while it seemed the economy was having a cardiac event.

The economy is unsustainable.According to theory, the economy can grow for ever without encountering limits. The reality is that we are bumping up against hard constraints due to things like over-crowding, climate change and environmental degradation. As environmentalists point out, never-ending growth is the philosophy of a cancer cell.

Together, these problems far exceed the importance of an event like the credit crunch. The debt that the global economy is building up with the environment, or the debt of rich countries to poor countries, is of much greater concern than the debt of banks to governments or shareholders. Indeed, it may turn out that this crisis was a blessing in disguise, if it provides the impetus for us to rethink our approach to money.

Just as economic theory fails to address the shortcomings of the economy, it also fails to properly account for its good qualities, of which there are many, including enormous dynamism and productivity. A model that emphasises stability isn’t very good at capturing the market’s creativity – as any artist or student of rock history will know, these two qualities rarely go hand in hand. So why do we persist with an economic theory that is so obviously unfit for purpose?

Bad coin

Economics is a mathematical representation of human behaviour, and like any mathematical model it is based on certain assumptions. I will argue, however, that in the case of economics the assumptions are so completely out of touch with reality that the result is a highly misleading caricature. The theory is less a science than an ideology. The reason why so many people are conned into thinking the assumptions reasonable is that they are based on ideas from areas like physics or engineering that are part of our 2,500-year scientific heritage dating back to the ancient Greeks. Superficially they have the look and feel of real science, but they are counterfeit coin.

Each chapter of this book begins with one of the misconceptions behind orthodox economic theory. It then goes back into the history to see where the idea came from, explains how it affects our everyday life, finds out why it persists despite evidence to the contrary, and proposes how we can change or replace it. The specific misconceptions are:

The economy can be described by economic lawsThe economy is made up of independent individualsThe economy is stableEconomic risk can be easily managed using statisticsThe economy is rational and efficientThe economy is gender-neutralThe economy is fairEconomic growth can continue for everEconomic growth will make us happyEconomic growth is always good

These ideas form the basis of orthodox economic theory and affect decision-making at the individual, corporate, and societal level; but the book will show they are mistaken and present alternatives. We will find out how the economy is the emergent result of complex processes that defy reduction; how the value of your home or pension is affected by unpredictable economic storms; why the economy is not rational or fair; and why economic growth is not automatically desirable, either for our own wellbeing or that of the planet.

Before proceeding, I should address a few concerns. The first is that, faced with the above list, most economists would protest that it is an over-simplified straw-man, and that economics is far more sophisticated than that. However, what counts is less what economists say – they are skilled at deflecting criticism, and have plenty of practice – than what kinds of calculations they actually perform. No one thinks that markets are perfectly stable, or that investors are perfectly rational, or that markets are fair and everyone has access to the same information – but key components of theory such as the efficient market hypothesis are explicitly based on exactly these assumptions. Peer under the hood of the risk models used by banks, or the models used to allocate your pension funds or determine government policy, and you will find the same assumptions there, with at best small modifications. As we’ll see, a number of so-called heterodox economists have been arguing against these assumptions for years, but until now their voices have carried little weight. We will go beyond a critique of these ideas, to explore where they came from in the first place and how they can be replaced. (I am also told that many economists do not really believe the mainstream theory, but play along in order to get publications and tenure – in which case they should enjoy this book.)

Some readers might find it hard to believe that mainstream economics is as flat-out wrong as I describe it here. After all, the great strength of science is that it is supposed to be self-correcting. If a theory is flawed, then it will be replaced by a better one. Even Newton’s laws of motion had to be modified with the development of quantum theory. A problem occurs, however, when no alternative is demonstrably better at making predictions, which is traditionally the acid test for a new theory. The new approaches discussed here do not amount to a single, unified replacement for orthodox theory, and nor do they claim to be much better at predicting the economy – in fact they openly acknowledge the uncertainty inherent in complex systems. That is why orthodox theory has struggled on for as long as it has, although things are beginning to change. As aNaturearticle entitled ‘Economics Needs a Scientific Revolution’ put it: ‘We need to break away from classical economics and develop completely different tools.’8

Another possible concern is that this book is written from the perspective of an applied mathematician, whose day job is in the area of systems biology (don’t tell my boss, but I never studied biology either). Some readers will prefer to get their economic analysis from economists, but I would argue that having a training in economics is actually a liability (which some particularly gifted people are capable of overcoming). If, as I believe, economics is an ideology, then being trained in it is effectively a way of closing your mind. Many of the new ideas that are revitalising economics come from diverse areas such as network theory, complexity, psychology, and indeed systems biology which are far outside the standard economics curriculum. When a field is in as poor a state as economics, being an outsider is a distinct advantage because it allows you to analyse the problems without having to justify previous theories that you were exposed to early in your career and feel compelled to defend.

Finally, readers of my previous book on economics,The Other Side of the Coin, may note that I am discussing many of the same points in this book. I’m guilty, it’s true – I did write that the economy is dangerously unstable and unbalanced, and that risk models are unreliable, before the crash. This book represents a complete updating and recrafting of those ideas in the face of what we have learnt about the economy in the last couple of years.

Enough justification. Economics, as already stated, is a mathematical model of human behaviour. The next chapter offers a brief tour through the history of such models, and asks whether there is any such thing as an economic law.

CHAPTER 1

THE ANARCHIC ECONOMY

Above, far above the prejudices and passions of men soar the laws of nature. Eternal and immutable, they are the expression of the creative power; they represent what is, what must be, what otherwise could not be. Man can come to understand them: he is incapable of changing them.

Vilfredo Pareto (1897)

Spread the truth – the laws of economics are like the laws of engineering. One set of laws works everywhere.

Lawrence Summers (1991)

Economics gains its credibility from its association with hard sciences like physics and mathematics. But is it really possible to describe the economy in terms of mathematical laws, as economists including President Obama’s economic advisor Lawrence Summers claim? Isaac Newton didn’t think so. As he noted in 1721, after losing most of his fortune in the collapse of the South Sea bubble: ‘I can calculate the motions of heavenly bodies, but not the madness of people.’

To see whether the economy is law-bound or anarchic, bear with me first for a little ancient history. It turns out that many of the ideas that form the basis of modern economics have roots that stretch back to the beginning of recorded time. That’s one reason why they are proving so hard to dislodge.

The first economic forecaster, in the Western tradition, was probably the oracle at Delphi in ancient Greece. The most successful forecasting operation of all time, it lasted for almost a thousand years, beginning in the 8th century BC. The predictions were made by a woman, known as the Pythia, who was chosen from the local population as a channel for the god Apollo. Her predictions were often vague or even two-sided and therefore hard to falsify, which perhaps explains how the oracle managed to persist for such a long time (rather like Alan Greenspan).

Our tradition of numerical prediction can be said to have begun with Pythagoras. He was named after the Pythia, who in one of her more famous moments of insight had predicted his birth. (She told a gem-engraver, who was actually looking for business advice, that his wife would give birth to a boy ‘unsurpassed in beauty and wisdom’. This was a surprise, especially because no one, including the wife, knew she was pregnant.)

As a young man, Pythagoras travelled the world, learning from sages and mystics, before settling in Crotona, southern Italy, where he set up what amounted to a pseudo-religious cult that worshipped number. His followers believed that he was a demi-god descended directly from Apollo, with superhuman powers such as the ability to dart into the future. Joining his inner circle required great commitment: candidates had to give up all material possessions, become vegetarian ascetics, and study under a vow of silence for five years.

The Pythagoreans believed that number was the basis for the structure of the universe, and gave each number a special, almost magical significance. They are credited with a number of mathematical discoveries, including the famous theorem about right-angled triangles and the square of the hypotenuse which we are all exposed to at school. However, their major insight, which backed up their idea that number underlay the structure of the universe, was actually about music.

If you pluck the string of a guitar, then fret it exactly halfway up and pluck it again, the two notes will differ by an octave. The Pythagoreans discovered that the notes that harmonise well together are all related by the same kind of simple mathematical ratio. This was an astonishing insight, because if music, which was considered the most expressive and mysterious of art forms, was governed by simple mathematical laws, then it followed that all kinds of other things were also governed by number. As John Burnet wrote inEarly Greek Philosophy: ‘It is not too much to say that Greek philosophy was henceforward to be dominated by the notion of the perfectly tuned string.’1

The Pythagoreans believed that the entire cosmos (a word coined by Pythagoras) produced a kind of tune, the music of the spheres, which could be heard by Pythagoras but not by ordinary mortals. And their interest in number was not purely theoretical or spiritual. They developed techniques for numerical prediction, which remained secret to the uninitiated, and it is also believed that Pythagoras was involved with the design and production of the first coins to appear in his area. Money is a way of assigning numbers to things, so it obviously fit with the Pythagorean philosophy that ‘number is all’.

Rational mechanics

If the cosmos was based on number, then it could be predicted using mathematics. The ancient Greeks developed highly complex models that could simulate quite accurately the motion of the stars, moon and planets across the sky. They assumed that the heavenly bodies moved in circles, which were considered to be the most perfect and symmetrical of forms; and also that the circles were centred on the earth. Making this work required some fancy mathematics – it led to the invention of trigonometry – and a lot of circles. The Aristotelian version, for example, incorporated some 55 nested spheres. The final model by Ptolemy used epicycles, so that planets would go around a small circle that in turn was circling the earth.

The main application of these models was astrology. For centuries astronomy and astrology were seen as two branches of the same science. In order for astrologers to make predictions, they needed to know the positions of the celestial bodies at different times, which could be determined by consulting the model. The Ptolemaic model was so successful in this respect that it was adopted by the church, and remained almost unquestioned until the Renaissance.

Classical astronomy was finally overturned when Isaac Newton combined Kepler’s theory of planetary motion with Galileo’s study of the motion of falling objects, to derive his three laws of motion and the law of gravity. Newton’s insight that the force that made an apple fall to the ground, and the force that propelled the moon around the earth, were one and the same thing, was as remarkable as the Pythagorean insight that music is governed by number. In fact Newton was a great Pythagorean, and believed Pythagoras knew the law of gravity but had kept it secret.

Newton held that matter was made up of ‘solid, massy, hard, impenetrable, movable particles’, and his laws of motion described what he called a ‘rational mechanics’ that governed their behaviour. It followed, then, that the motion of anything, from a cannonball to a ray of light, could be predicted using mechanics. His work therefore served as a blueprint for numerical prediction – reduce a system to its fundamental components, discover the physical laws that rule them, express as mathematical equations, and solve. Scientists from all fields, from electromagnetism to chemistry to geology, immediately adopted the Newtonian approach, to enormously powerful effect. You can hear the whisper coming from the Pythagoreans: ‘Spread the truth – one set of laws works everywhere.’

Rational economics

Among those to hear the whisper, if somewhat belatedly, were the new group of people calling themselves economists in the late 19th century. If Newtonian mechanics was proving so successful in other areas like physics and engineering, maybe it could also be applied to the flow of money.

The theory they developed is known as neoclassical economics. Today it still forms the basis of orthodox theory, and makes up the core curriculum taught to future economists and business leaders in universities and business schools around the world.2As a set of ideas, it might be the most powerful in modern history.

Neoclassical economics is based on an explicit comparison with Newtonian physics. Just as Newton believed that matter is made up of minute particles that bump off one another but are otherwise unchanged, so neoclassical theory assumes that the economy is made up of unconnected individuals who interact by exchanging goods and services and money but are otherwise unchanged. Their behaviour can be predicted using economic laws, which are as omnipresent as the laws that govern the cosmos.

To calculate the motions of the economy, one must determine the forces that make it move around. The neoclassical economists based their mechanics on the idea of utility, which the philosopher Jeremy Bentham described in his ‘hedonic calculus’ as the sum of pleasure minus pain. For example, if an apple gives you three units of pleasure, and paying for it gives you only two units of pain, then purchasing the apple will leave you one utility unit (sometimes called a util) in profit.

Leaving aside for a moment what units of measurement a util is expressed in, an obvious problem is that different people will assign different utility values to objects such as apples. The neoclassical economists got around this by arguing that all that counted was the average utility. It was then possible to use utility theory to derive economic laws. As William Stanley Jevons put it in his 1871 bookTheory of Political Economy, these laws were to be considered ‘as sure and demonstrative as that of kinematics or statics, nay, almost as self-evident as are the elements of Euclid, when the real meaning of the formulae is fully seized’.

Imaginary lines

If economics has an equivalent of Newton’s law of gravity, it is the law of supply and demand. The law is illustrated inFigure 1, which is a version of a graph first published in an 1870 essay by Fleeming Jenkin. It has since become the most famous figure in economics, and is taught at every undergraduate economics class.

The figure shows two curving lines, which describe how price is related to supply and demand. When price is low, supply is low as well, because producers have little incentive to enter the market; but when price is high, supply also increases (solid line). Conversely, demand is lower at high prices because fewer consumers are willing to pay that much (dashed line).

The point where the two lines cross gives the unique price at which supply and demand are in perfect balance. Neoclassical economists claimed that in a competitive market prices would be driven to this point, which is optimal in the sense that there is no under- or over-supply, so resources are optimally allocated. Furthermore, the price would represent a stable equilibrium. The market was therefore a machine for optimising utility.

Figure 1.The law of supply and demand. The solid line shows supply, which increases with price. The dashed line shows demand, which decreases with price. The intersection of the two lines represents the point where supply and demand are in balance.

For example, suppose that the average price for a house is 100,000 (currency units of your choice) when the market is at equilibrium. If sellers grew greedy and the price lifted temporarily to 110,000, then suppliers would respond by building more homes, and consumers by buying fewer. The net effect would be to pull prices down to their resting place, as sure as the force of gravity. Conversely, if prices fell too low, then supply would drop, demand would increase, and prices would bob back up again.

However, if demand were to increase for some structural reason, such as population growth, then the entire demand curve in Figure 1 would shift up, so the equilibrium price would be higher. If supply permanently increased, say because new land opened for development, then the equilibrium price would shift down along with the supply curve.

This is for just one good, and the situation becomes considerably more complicated when multiple goods and services are included, now and in the future, since consumers then have a choice on where and when to spend their money. One of the supposed triumphs of neoclassical economics in the 1960s was to mathematically prove that the entire economy will still be driven to a stable and optimal equilibrium, again subject to certain assumptions. This was seen as mathematical proof of Adam Smith’s ‘invisible hand’, which maintains prices at their ‘natural’ level, and formed the basis of General Equilibrium Models that are used to simulate the economy today.

The visibly shaking hand

We are all familiar and comfortable with the law of supply and demand, and it is often used to explain why prices are what they are. A strange thing, though: historical data for assets like housing just doesn’t look that stable or optimal. In fact it seems the invisible hand has a bad case of the shakes.

As an illustration, the top panel inFigure 2shows a plot of UK house prices over about three decades. The numbers have been corrected for inflation. It shows the large ramp up in house prices from 1996 until 2009. Similar behaviour was seen in other G8 economies.

Figure 2.Top panel shows the real growth in UK house prices from 1975 to 2009. Prices are in 1975 currency, adjusted for inflation.3Lower panel is the estimated relative mortgage payment. The scaling is relative only.

It appears from this figure that houses were much more affordable before 1985 than after 2000. However, the figure is a little misleading because affordability is a function not just of real house prices but also of mortgage rates, which were about twice as high in 1985 as they were in 2000. To correct for this, the lower panel shows the estimated typical mortgage payment, based on the prevailing interest rates. This reveals a distinct boom/bust pattern.

In 2008, at the peak of the recent housing boom, when prices appear to have been grossly inflated, it was frequently argued that prices were high because of the balance between supply and demand: the UK is a ‘small, crowded island’ so the supply of housing is constrained. But the UK was also a small, crowded island in 1995, when homes were relatively affordable. So were prices really optimal in 2008, as the law of supply and demand would dictate? Or was something else going on?

The lines and the unicorn

In one sense, the law of supply and demand captures an obvious truth – if something is in demand, then it will usually attract a higher price (unless it’s something like digital music, which is easily copied and distributed for free). The problem arises when you decide to go Newtonian, express the principle in mathematical terms, and use it to prove optimality or make predictions.

In order to translate the relationship between supply and demand into a mathematical law, neoclassical economists had to make a number of assumptions. In particular, the curves for supply and demand needed to be fixed and independent of one another. This was justified by the idea that the utility for producers and consumers should not change with time.

But here we come to one of the differences between economics and physics. The particles described in physics are stable and invariant, so an atom of, say, carbon on earth is indistinguishable from one in the sun, and has the same gravitational pull. The law of gravity therefore applies the same here on earth as it does elsewhere in the cosmos, which is why it is such a powerful tool. However, people are not atoms; they vary from place to place, and they also change their opinions and behaviour over time. The housing market is also linked to the rest of the global economy, which itself is in a state of ceaseless flux.

The law of supply and demand implies that if prices increase above their ‘equilibrium’ value then demand should decrease. This works reasonably well for most goods and services (if you omit things like luxury goods whose cachet increases as they become less affordable). If a baker overcharges for bread, he will come under pressure from competitors (unless he can distinguish his services); charge too much for your labour and you’ll find it hard to get a job (unless, as seen in Chapter 7, you’re a CEO or movie star). However, the relationship breaks down completely when you consider assets, such as real estate or gold bars, which are desired in part for their investment value. Both supply and demand are a function not just of price, but of the rate and direction at which prices are changing (this is explored further in Chapter 3). The perceived utility of owning a home is much greater when house prices are seen to be rising than when they are falling off a cliff. Matters become even more tenuous in today’s networked economy, where what is being supplied or demanded is often not a physical object at all, but something less tangible or constrained like information, a brand, or access to a network, which are shared rather than exchanged.

Supply and demand also depend in intricate ways on the exact context and history, even for basic goods. Suppose for example that the price of bread is everywhere uniformly raised by 5 per cent. According to theory, we should then be able to compute both supply and demand at this new price. Let’s consider three cases. In the first case, the government announces that the price rise is due to a new bread tax being applied. People will likely react by buying less bread. In the second case, a rumour goes out that the price change is because of a drought that has affected wheat prices. Whether the rumour is true or not, demand may increase because some people will buy extra loaves and store them in the freezer before prices increase further. In a third, hypothetical case, suppose that shoppers are given a drug so that any memory or preconception they have about the price of bread is rather hazy, so they respond only to big price changes (a lot of people are like this anyway). Then they would probably not notice the difference and just go ahead and buy the bread as usual. There is also a dynamic, time-sensitive element, because it is hard to know whether a change in demand will be long-lasting or short-lived.

In fact the idea that supply or demand can be expressed in terms of neat lines at all, as in Figure 1, is a fiction. As econophysicist Joe McCauley observed, there is no empirical evidence for the existence of such curves. Despite that, ‘intersecting neo-classical supply–demand curves remain the foundation of nearly every standard economics textbook’.4Like unicorns, the plot of supply and demand is a mythological beast that is often drawn, but never actually seen.

This helps explain why large economic models, which are based on the same laws, fail to make accurate predictions (traditionally the test of reductionist theories). As an example from something even slippier than house prices,Figure 3shows the price of crude oil over a quarter-century, along with predictions from the Energy Information Administration (EIA), which is part of the US Department of Energy. The computations are performed by estimating the global levels of supply and demand, using their World Oil Refining, Logistics, and Demand (WORLD) model. In the 1980s, the predictions called for prices to increase, probably because the models incorporated memory of the 1970s oil price shock. Prices instead fell and remained low for the next couple of decades. The forecasts eventually learned that prices were not going to return to previous levels, and flattened out; but as soon as they did, prices spiked up to $147 per barrel. Then plummeted to $33. Then doubled again.

Figure 3.Price of crude oil (solid line), along with predictions (dashed lines). Source: Energy Information Administration.

This oil price spike played a large part in exacerbating the credit crunch, but went completely unpredicted by the experts. The reason is that it had absolutely nothing to do with supply or demand. According to the EIA, world oil supply actuallyrose, and demanddropped, in the six-month period preceding the spike.5So why did prices go up? Well, the demand for actual oil – the black, gooey stuff they get out of the ground – wasn’t getting stronger. But as discussed in Chapter 8, oil futures – contracts giving the right to buy oil at a set price and future date – were all the rage in 2008. The spike in oil was a classic speculative bubble, with the same dynamics as a real estate bubble, except that it was played out in months instead of years.

The economic weather

Our poor record of foresight might still seem counter-intuitive: how can it be that specialists can’t predict the future of the economy given their immense expertise, huge amounts of data, and access to high-speed computers? Surely we know more than the Delphic oracle? One reason is that the economy is made up of people, rather than inanimate objects. But it’s interesting to note that the same problem is seen in other areas that appear more amenable to a Newtonian approach. Much can be learnt from a comparison with weather forecasting.

In a 2009 speech, the Federal Reserve chairman Ben Bernanke, today’s version of the oracle, discussed his institution’s long-standing involvement in economic forecasting as follows: ‘With so much at stake, you will not be surprised to know that, over the years, many very smart people have applied the most sophisticated statistical and modelling tools available to try to better divine the economic future. But the results, unfortunately, have more often than not been underwhelming. Like weather forecasters, economic forecasters must deal with a system that is extraordinarily complex, that is subject to random shocks, and about which our data and understanding will always be imperfect.’6

Of course this uncertainty doesn’t stop the Federal Reserve from regularly cranking out predictions, which everyone takes at face value. But as an illustration of Bernanke’s point, the top panel ofFigure 4is a plot of sea-surface temperature in a zone of the Atlantic ocean, which indicates the presence of El Niño events. I have chosen a timespan such that the fluctuations match quite closely the plot of housing price affordability from Figure 2, shown rescaled in the lower panel (unfortunately the timescale is different, so, no, we can’t use El Niño to predict UK housing prices). El Niño drives global weather patterns that have a huge economic impact on everything from agriculture to insurance, so there is even more incentive to predict it than there is to predict house prices. And yet our most sophisticated weather models still do a poor job of predicting El Niño.7As with housing prices, it is possible to discern a distinct pattern, but it is almost impossible to call the exact timing of the next peak or trough. The reason is that both El Niño and housing markets are part of complex, global systems that elude reduction to simple rules or laws.

Figure 4.Top panel is a plot of sea-surface temperature anomalies.8Above 0.5 indicates an El Niño event, below –0.5 La Niña. Lower panel is a rescaled plot of estimated mortgage payments from Figure 2.

Indeed the whole idea of a fundamental law, given by a simple equation, is applicable only to certain specialised cases, such as gravity. In weather forecasting, one of the main challenges is to predict the formation and dissipation of clouds, which drive much of the weather and determine precipitation. However, there is no law or equation for clouds, which are formed in a complex process whereby droplets of water congregate around minute particles such as salt, dust or pollen in the air. In fact, clouds are best described asemergent propertiesof the atmospheric dynamics.

The definition of an emergent property is somewhat hazy, and depends on the context; but in general it refers to some feature of a complex system that cannot be predicted in advance from knowledge of the system components alone. Scientists know a lot about the parts of a cloud – air, water, particles – but they still can’t produce a realistic one on the computer, let alone predict the behaviour of real clouds. Engineers know a lot about fluid flow, but they still find it hard to model the effects of turbulence, which is why Formula 1 racing teams are among the largest users of wind tunnels. Some scientists even believe that so-called fundamental physical laws – including the law of gravity – are just the emergent result of a more complex dynamics. As we’ll discuss further in later chapters, economic forces such as supply and demand are also best seen as emerging from a mix of social, economic, and psychological factors.

Emerging economy

So if the traditional reductionist approach doesn’t work, what is the alternative? Emergent phenomena have been widely studied by complexity scientists, through the use of techniques such as cellular automata or agent-based models. Cellular automata are computer programs that typically divide the screen into a grid of cells. The evolution of the system is governed by simple rules that describe how one cell affects its neighbours. While the laws are simple at the local level, the emergent behaviour at the global level can be extremely complex, and can’t be modelled directly using equations. Cellular automata have been used to study a wide range of phenomena, including turbulent fluid flow, avalanches, the spread of forest fires, and urban development.

Agent-based models consist of multiple software ‘agents’ that could represent, say, investors in the stockmarket. The agents are allowed to influence each other’s behaviour, just as in reality investors communicate with those around them. They make decisions based not on uniform laws, but on fuzzy heuristics or rules of thumb. Agents can also learn and adapt their behaviour, in the same way that investors become more conservative after being burned by a market fall. It is therefore impossible to assign them a fixed and independent demand curve of the sort required by the ‘law of supply and demand’.

The collective effect of the agents is again to produce emergent behaviour that is often quite surprising, and that can lead to useful insights about how the system works. Agent-based models have been used to reproduce the boom/bust behaviour of markets, and have found many other applications in areas from transport to cancer therapy.9Programmes in complexity are starting to appear at business schools and institutions like the London School of Economics. The first way to revive economics, then, is to encourage this trend, and in the process rid the field of its quasi-Newtonian pseudo-laws.

One drawback of this type of research is that it has none of the icy glamour and prestige of great Newtonian mathematical laws. It is unlikely that anyone will ever win a Nobel Prize for an agent-based model. Nor does complexity theory offer a single unified approach. Models are seen more as patches, each of which captures an aspect of the complex reality.

Also, while the complex systems approach is useful for simulating many aspects of the economy, it is unlikely that it will prove to be much better than orthodox theory at predicting the course of something like the housing markets. The reason is that the exact behaviour of a system depends on all the exact details, and the only way to predict a system would be to reproduce it on the computer. That’s the point of emergent properties: they can’t be predicted by a simple equation. Instead, complexity scientists search for pockets of predictability – aspects of the system that are amenable to prediction.10

Complexity research has many implications for economics (most of the conclusions of this book are based on a complexity viewpoint), but its most devastating consequence is that it throws a spanner in the entire mechanistic approach for modelling complex systems like the economy. Newton’s blueprint for numerical prediction, again, was to reduce a system to its fundamental components, discover the physical laws that rule them, express as mathematical equations, and solve. But this reductionist method doesn’t work for emergent properties. There are no fixed laws – only general fuzzy principles that can be roughly captured by rules of thumb but rarely conform to neat mathematical equations. The message of the Pythagoreans – that all can be reduced to number – turns out not to be true.

In the next chapter, we consider the behaviour of groups of people as they engage in the economy – and ask whether they behave as independent individuals, as theory tells us, or more like the components of a cloud.

CHAPTER 2

THE CONNECTED ECONOMY

The pernicious love of gambling diffused itself through society, and bore all public and nearly all private virtue before it.

Charles Mackay,Memoirs of Extraordinary Popular Delusions and the Madness of Crowds(1848)

There is no such thing as society.

Margaret Thatcher (1987)

Economists are taught that the economy is the net result of the actions of individual investors, who act independently of one another to maximise their own utility. This view of the economy – similar to the atomic theory of physics – sees the individual as all-important, and downplays the role of society (which according to one of Margaret Thatcher’s more famous statements doesn’t even exist). The reality, however, is that we influence one another all the time. We buy houses not just for a roof over our head, but also because everyone else is buying one and we are afraid to be left off the ‘housing ladder’ – now known as the housing bungee. This chapter shows how economists ignore or downplay the herd behaviour of markets, and therefore fail to predict or properly prepare for economic crises.

One of Pythagoras’ most famous disciples – though he was born after the master’s death – was the philosopher Democritus. His biographer Laertius wrote that he derived all his doctrines from Pythagoras, to the point that ‘one would have thought that he had been his pupil, if the difference of time did not prevent it’. Today, Democritus is best known for his theory that matter is made up of atoms, named after the Greek wordatomosfor indivisible.

The idea that a system can be broken down into its smallest components is a key plank of our reductionist scientific tradition. Today, scientists are still following this quest at facilities like the Large Hadron Collider near Geneva, by flinging small pieces of matter together at nearly the speed of light and analysing the debris. The atomic theory has also had enormous influence in other areas, including economics.

Democritus arrived at his idea by imagining that you could take an object – say a page from this book – and cut it into two pieces, then cut it again, and again, and so on. At some point, he argued, you would have to come to a smallest possible piece, because otherwise you could continue for ever and that would make no sense (the Greeks didn’t cope well with the notion of infinity). That smallest unit is an atom. Substances had different properties because of the shape of their atoms – the atoms of oil, for example, had to be very smooth so that they would slide over one another easily.

The atomic theory never really caught on at the time, in part because Aristotle didn’t like it, and it came into favour only much later when scientists such as Galileo and Newton lent their support. When Newton said that matter was made up of ‘solid, massy, hard, impenetrable, movable particles’, he was describing atoms.

Because no one could actually see atoms, they remained a mostly theoretical construct until 1905, when Albert Einstein convincingly demonstrated their existence and even managed to estimate their size and velocity. It had long been known that, when viewed under a microscope, particles such as dust or pollen in a suspension tended to jostle around in a random fashion almost as if they were alive. This Brownian motion – named after the Scottish botanist Robert Brown, who was the first to investigate it – was something of a mystery, but Einstein showed that it could best be explained by assuming that the particles were constantly buffeted by individual atoms in the suspension. Atoms were small, but sometimes they could make themselves felt.

Particle theory

While physical atoms may have been just a theory in the late 19th century, the concept was eagerly adopted by neoclassical economists such as William Stanley Jevons, with the difference that the atoms of the economy were individual people (or firms). An advantage was that people were larger than atoms so you could see what they were doing; a disadvantage was that they showed considerable variability. But as Jevons argued in hisTheory of Political Economy(1871), it was necessary only to model ‘the single average individual, the unit of which population is made up’.

One of Newton’s key insights was that, to compute the gravitational pull of a spherical body like the earth, it wasn’t necessary to compute the effect of each individual part of the earth – each atom in a lump of rock or blade of grass. Instead it sufficed to assume that a single point mass, equal to the mass of the earth, was located at its centre. In the 19th century, physicists working in the new field of statistical mechanics had also shown that states such as temperature were governed not by what was happening with individual atoms, but by the statistical average. Jevons believed in the same way that it was possible to ignore the fact that people are different, and take into account only the population average. Indeed, this is exactly what modern economic models do to estimate the demand for a commodity like oil: it is impossible to take into account each person or company, so they make guesses for aggregate demand over a country or sector.

By equating the aggregate supply with the aggregate demand, the economists could in principle predict the equilibrium level of the economy, where supply and demand were in perfect agreement. But what explained the apparent day-to-day fluctuations in prices, of the sort seen, for example, on financial markets for stocks and bonds?

In 1900, even before Einstein’s explanation of Brownian motion, the French economist Louis Bachelier came up with a similar theory for the economy. In his Ph.D. thesis, he proposed that financial markets are always close to equilibrium, but are buffeted around by the actions of individual investors as they respond in different ways to news or just the market’s current state. Any change in price is therefore essentially random. As with a piece of pollen undergoing Brownian motion, the market might look like it’s alive and has a sense of purpose, but that’s just an illusion.

Bachelier’s work initially made little impact, perhaps because it appeared to say that forecasting was impossible (never popular with forecasters). However, Bachelier had also pointed out that it should still be possible to evaluate the probability of the market changing by a certain amount over any given period. Price movements could be modelled using the normal distribution, or bell curve, which had long been used by astronomers and other scientists to account for the effect of random errors in their observations. In the 1950s and 60s, this aspect of his thesis was picked up on by economists, who used it to develop an elaborate theory of risk using the same mathematics as that used to describe Brownian motion.

Atomic markets

The atomic theory of the economy reached its point of highest glory in 1965 with the efficient market hypothesis, which was proposed, in another Ph.D. thesis, by Eugene Fama of the University of Chicago. He described the market as made up of ‘large numbers of rational profit-maximizers’ who had access to all relevant information and were in active competition with each other. Given these assumptions, Fama argued, prices of any security would automatically adjust to reflect its ‘intrinsic value’. Any deviations from that level would be small and random.

While Bachelier’s work never gained popularity until after his death, Fama became something of a celebrity among economists. The reason was that he had taken the same idea – that market movements were random – and created a new story around it. Instead of the market being as dumb and lifeless as a piece of dust, it was granted a semi-divine status: a deity with a tag machine that can stick the correct price on anything.1The reason we can’t predict it is that no forecaster can possibly outwit this god.

The efficient market hypothesis also granted we ordinary mortals various special properties, such as rationality, an obsession with reading the news, and an intense focus on making money. However, the most striking thing about it is that, like inert atoms, its people never interact except by bouncing off one another in the marketplace. No one ever gets together to talk about the price of houses or oil or the stockmarket; they all have to make their own mind up. They are truly independent.