Tim Harford The Undercover Economist

Undercover EconomistUndercover Economist

My weekly column in the Financial Times on Saturdays, explaining the economic ideas around us every day. This column was inspired by my book and began in 2005.

Undercover Economist

William Golding explains Brexit

For educational reading, the British political establishment might pick up something by William Golding, winner of the Nobel Prize for literature in 1983. Lord of the Flies (UK) (US) is his most famous work, with its grim suggestion that the line between innocent children and murderers is thin. For an insight into Brexit Britain’s current predicament after a week of chaos, however, I recommend The Spire (UK) (US).

The book is a study of monomania. Dean Jocelin has visions of adding to his cathedral a 400-foot steeple, an expression of human prayers reaching into the heavens. But the intensity of his ambition blinds him to his other duties and threatens both the cathedral and the community around it. As Jocelin himself admits, “at the moment of vision, the eyes see nothing”.

It is impossible to miss the analogy. The UK is being driven by visionary enthusiasts for Brexit just as surely as Jocelin’s attendants had to bend to his will. Whether their visions are realistic is quite another question. In the summer of 2016 David Davis, the man charged with delivering Brexit, predicted that the UK would be able to negotiate a free trade area “massively larger than the EU” within two years. That was 17 months ago. Whatever Mr Davis was gazing at back then, it wasn’t reality.

Then there are those wretched experts, who tell Jocelin that his spire is impossible. The master builder, Roger Mason, confronts him with an inescapable dilemma: if the spire’s structure is too lightweight, the next storm will blow it down; a sturdier structure will warp the cathedral beneath it, or sink into the swamp. Jocelin berates him for lack of faith; the dean wants to have his cake and eat it.

Nor can evidence dissuade him. When Mason digs into the cathedral’s non-existent foundations, showing Jocelin the soft earth writhing under the weight of the building, the zealot’s faith is strengthened. If the current cathedral stands on foundations of mud, isn’t that proof that miracles are possible?

Brexit, of course, is not only possible but almost inevitable. But the promises that have been made cannot be fulfilled any more than Jocelin’s spire could safely be built. We cannot “have access” to the single market (that is, remain in it) while also ending freedom of movement; we cannot leave the customs union without introducing a customs border. The discovery of the week — surprising to nobody who has been paying attention — is that this customs border can be located on neither land nor sea.

Jocelin ignores the experts. “I thought it would be simple,” he says. “I had to build in faith, against advice. That’s the only way.” And it proves all too easy to ignore those who might restrain him. One faithful priest, “Father Anonymous”, is too boring to notice. In another life, perhaps he would have been an economist. Others, Jocelin remarks sharply, would profit if the project was thwarted. It is true that sometimes the experts have an eye on their own finances. They may nonetheless be right.

Admittedly, pure determination sometimes finds a way. Monomaniacs change the world, sometimes for the better. One recalls the description of Steve Jobs in his early years at Apple, generating a “reality distortion field” that could redefine what was possible by “sheer mental force”.

Even The Spire was inspired not by folly, but by a triumph. For more than 15 years Golding was a teacher in the shadow of Salisbury Cathedral, whose 404-foot spire has been the tallest in the country for nearly five centuries. Perhaps long-forgotten experts once warned that it could never be built.

Whether Brexit eventually turns into something worth admiring will be for future historians to judge. For now, Golding invites us to ponder the cost. Jocelin’s ambition requires an army of builders to deliver it; that army murders an innocent person.

“Let it be so,” says Jocelin to the heavens. “Cost what you like.”

The project takes a toll in ways that blinkered Jocelin did not consider and takes too long to notice. Letters from allies go unanswered. Urgent business is postponed. The cathedral starts to die; the congregation leaves. Observers of British politics will not find the parallel hard to discern: the centre ground has been hollowed out, the economy is faltering, respect for basic norms of truth-telling are in tatters, and the union itself is under strain.

Jocelin slowly realises the toll his project is taking on those around him, but since he is doing the work of God, any price must be worth paying. British politicians obey the will not of God, but of the British people as expressed in a referendum. It seems to amount to the same thing.

In the end, Jocelin is stripped of his job and his dignity. The long-predicted storm comes. Reality asserts itself. The vision, the visionary and the spire itself crack under the strain. “I thought I was doing a great work,” Jocelin confesses. “And all I was doing was bringing ruin and breeding hate.”

And yet the spire does not fall. That is where Golding leaves us: the project cannot go on, but it cannot be undone. Disaster hangs in the air. “Has it fallen yet?” asks the stricken Jocelin. Not yet.

 

Written for and first published in the Financial Times on 8 December 2017.

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Could we run the economy with an app?

The control room is hexagonal, containing a circle of white fibreglass swivel-chairs with red-brown cushions and inbuilt push-button panels. The room is reminiscent of Star Trek, but it is no film set. Project Cybersyn was an attempt in the early 1970s to algorithmically manage the Chilean economy in accordance with democratic socialist principles under President Salvador Allende.
The idea was not entirely a new one. Between the wars, economists debated the “socialist calculation” problem: could a benevolent central planner somehow coordinate all the production and consumption necessary to run a modern economy, bypassing the greed and waste of the market with a more rational system?
The answer was not obvious to economists – at least, not then. The uncompromising Ludwig von Mises argued that it was logically impossible, others that it was merely impractical. But Oskar Lange argued that it could be done: if an economy could be described as a series of simultaneous equations for supply and demand, then the central planner could solve those equations, if only by trial and error.
This proved easier said than done. Nobel laureate Leonid Kantorovich spent six years gathering the data and performing the calculations necessary to optimise Soviet steel production in the 1960s, far too slow to be useful in an ever-changing economy.
Computers promised more. In an essay published after his death in 1965, Lange wrote, “Let us put the simultaneous equations on an electronic computer and we shall obtain the solution in less than a second. The market process… appears old-fashioned.”
That was typical of the awe with which we continue to view these silicon brains. But it was still premature: the computers of five decades ago weren’t fast enough. One credible estimate is that the Soviet Union produced 12 million types of product at its zenith, a mathematical knot that would have taken decades for a vintage computer to unpick. A modern economy produces perhaps 10 billion.
Chile’s Project Cybersyn never had much chance to prove its worth: like Allende himself, it died when the murderous Augusto Pinochet seized power in Chile in 1973.
It was probably doomed from the start. As described in Eden Medina’s book Cybernetic Revolutionaries (UK) (US), the sleek control room masked the fact that Allende’s government only owned four computers. One of them was a Burroughs 3500, which by coincidence is a type of machine that my own father used to install, keeping himself trim by lugging around hard drives the size of tumble-dryers. It was still too soon to try to replace the marketplace with a computer network.
But the project’s ambitions no longer seem quite so unfeasible. We shouldn’t underestimate the task: Chile’s GDP in 1970 was about $50bn – perhaps $300bn at current prices. Even now, Amazon’s revenue, $135bn in 2016, is less than that.
But the power of computers is growing far more quickly than economic output. Could we build an app to run an economy, to not only replace Steve Mnuchin and Janet Yellen, Jeff Bezos and Tim Cook, but to oversee the fine details of production and consumption everywhere, eliminating waste, recessions, and inequality?
The idea has resurfaced in the writings of two Chinese economists, Binbin Wang and Xiaoyan Li. Wang and Li argue that modern computers and cheap sensors make it possible to optimise production in real time, personalised to the needs of citizens.
In some ways this has already happened. Advertisements on Google and Facebook are handled by vast algorithmic markets. If you work for Uber or Deliveroo, your boss is an algorithm. But firms have always been islands of planning in a sea of market forces; an economy in which the government controls all the platforms is something quite different.
One enduring obstacle is tacit knowledge. A textbook economy of supply and demand curves is, in principle, the kind of system that can be understood mathematically. But as Friedrich Hayek argued in 1945 there is a great deal going on in any economy that cannot be counted or even described.
Decisions to produce, to consume, and to take a risk trying to create something new, are all taken with the knowledge of “particular circumstances of time and place”. Wang and Li believe that big data make this once-tacit knowledge explicit; I am not convinced.
Then there is the issue of power. Facebook and Google already have too much. What would Stalin have done with such information? Or Pinochet? China is already using the data exhaust collected by Alibaba and TenCent to exert social control.
Hayek himself twice visited Pinochet’s Chile without speaking out about the regime’s abuses; that is indefensible.
But Hayek was right about the power of market prices to coordinate a complex economy steeped in tacit knowledge. Market forces remain a more powerful computer than anything made of silicon. We can shape its inputs and outputs with taxes that penalise pollution, redistribute income, or encourage social goods. But replacing the market with state-run algorithms is an idea that should stay in the realms of science fiction.
Written for and first published in the Financial Times on 1 Dec 2017.

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Economicky words are just plain icky

Why can’t economists just speak plainly and clearly? The dismal science has had an image problem for a long time — long enough for most people to forget that the “dismal science” insult was hurled by the despicably eloquent racist Thomas Carlyle, in an argument over whether black plantation workers should be paid for their work or motivated with the “beneficent whip”.

If you’re arguing with an apologist for racism and he has better lines than you, you’re doing something wrong. True in 1849, true today.

Yet the problem seems to have intensified in the past few years; gone are the glory days of Freakonomics, when every economist seemed an investigator with the cachet of Sherlock Holmes. Now we economists are painted as jargon-spouting spreadsheet jockeys, malevolent string-pulling ideologues, or worst of all, “experts”. What went wrong and what are we going to do about it?

Language is part of our problem. Even in a medium that demands brevity and clarity — Twitter — we seem to be drawn to polysyllabic obfuscations like wasps to jam. Marina Della Giusta and colleagues at the University of Reading recently conducted a linguistic analysis of the tweets of the top 25 academic economists and the top 25 scientists on Twitter. (The top 3 economists: Paul Krugman, Joseph Stiglitz and Erik Brynjolfsson; the top 3 scientists: Neil deGrasse Tyson, Brian Cox, and Richard Dawkins.)

Ms Della Giusta and her colleagues found that the economists tweeted less and had fewer Twitter conversations with strangers. I sympathise, but nevertheless the scientists managed it and the economists did not. The economists also used less accessible language with more complex words and more abbreviations. Both their language and their behaviour was less chatty.

This is true in more formal settings, too. Last year on Bank Underground, a blog for Bank of England staff, analyst Jonathan Fullwood compared the bank’s reports to the writings of Dr Seuss. Long words, long sentences or long paragraphs make for difficult prose. The Cat In The Hat stands at one end of the scale; bank reports at the other.

The World Bank is another culprit: this summer its chief economist Paul Romer made few friends when he berated his colleagues over their feel-good bureaucratese in which projects “are emerging” while “players” are “partnering”, all the while advising “corporate governance and competition policies and reform and privatise state-owned enterprises and labour market/social protection reform”. It is surprisingly easy to write like this when you don’t know what you think, or cannot agree, or dare not say. The result occupies the overlap on a Venn diagram between unobjectionable and incomprehensible.

According to Stanford’s Literary Lab, World Bank reports were not always like this: they once described specific facts (“Congo’s present transport system is geared mainly to the export trade”) and what the World Bank had done to improve them.

We should do better, whether writing a tweet or a report. But there is a reason that this stuff is hard: politics. In most spheres of life people are happy to trust doctors, engineers and scientists to get on with whatever it is they do. Politics changes that: when scientists must communicate ideas about climate change, vaccines, or genetic engineering, they suddenly find themselves dragged into political fights for which they have neither the stomach nor weapons. Scientific literacy is no cure: on contentious topics such as climate change, political polarisation actually increases with education.

Economists, of course, cannot boast the same regard as doctors, engineers and scientists — but they are on contested territory more often. Economics discusses public spending, inequality, regulation, taxes and other topics in the no-man’s-land of a political war. No wonder we hesitate to engage on Twitter; no wonder we write reports that try to please everyone by saying nothing much. We then seem evasive and tedious, so nobody trusts us. But when we set out a position clearly and plainly, we risk being dragged into poisonous squabbles — something that has happened repeatedly during and since the Brexit referendum.

There are no easy answers — although emerging evidence from political scientist Dan Kahan’s research group at Yale University suggests that we might do well by trying to engage people’s sense of curiosity. It is not enough to write with clarity; the great science communicators, from Carl Sagan to David Attenborough, inspire a sense of wonder. If we use a surprising fact as an ambush, that will provoke a defensive response; far better to present an intriguing puzzle. But if we cannot inspire awe, we should at least write clearly — a habit that helps us think clearly, too.

Simplicity alone, of course, is not enough. “We’re going to build a big, beautiful wall and Mexico is going to pay for it,” has the same simple tone as Dr Seuss, although it lacks his compassion. Does it reflect clear, trustworthy thinking? I do not think so, Sam-I-Am.

Written for and first published in the Financial Times on 24 November 2017.

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Why the robot boost is yet to arrive

To adapt a 30-year-old quip from the great economist Robert Solow: you can see the robots everywhere except in the productivity statistics. This fact has been puzzling me for a few years now. Productivity growth is disappointing — especially but not only in the UK — and it has been for years. Unemployment is near record lows, and employment is high. All this is the opposite of what one would expect if the robot job apocalypse was upon us.

Yet there is no denying the remarkable advances in various branches of artificial intelligence. The most talked-about example is the self-driving car. This technology has come a long way in a short time, which is more than one can say for the original participants in the 2004 Darpa Grand Challenge, a race sponsored by the US military. With large cash prizes for the first autonomous vehicle to complete a 150-mile course in the Mojave desert, the best effort foundered after just seven miles. The contest became a punchline. Just 13 years later, nobody is laughing about autonomous vehicles.

Then there are deep-learning technologies such as AlphaGo Zero, which took just 72 hours to teach itself to become seemingly invincible at the formidable board game, Go. Alexa, Cortana, Google Assistant and Siri have made voice recognition an everyday miracle. Strides are being made in image recognition, medical diagnosis and translation. There are behind-the-scenes triumphs: deep learning is optimising power-hungry cooling in server farms.

All of this makes the puzzle of high employment and low productivity even more puzzling. Yet there are several ways to resolve it. A simple explanation is that the robot talk is all hype. Computer scientists have been over-optimistic before. Nobel laureate Herbert Simon predicted in 1957 that a computer would beat the world chess champion within 10 years; it took 40. In 1970 Marvin Minsky predicted that computers would have human-like general intelligence “within three to eight years”, a prediction even more inaccurate than Mr Simon’s.

A more encouraging story is that we are understating productivity, for example, by undervaluing the output of services in general and the digital economy in particular, much of which is free and therefore invisible to normal measures of economic output.

A third possibility is that — to borrow an idea from the writer William Gibson — the future has already arrived, but it is unevenly distributed. Perhaps the zero-sum scramble to dominate winner-takes-all markets is simply squandering most of the potential gains.

To tease apart these accounts, a research paper by a team including both sides: Erik Brynjolfsson, an economist well known for his writings on “the new machine age”, and Chad Syverson, one of the leading experts on economic productivity.

The researchers argue that the productivity slowdown is real. It may feel plausible to suggest our data simply are not good enough to recognise that productivity is growing strongly, but the story seems off in a number of ways — most obviously that the productivity shortfall is just too large to be a statistical illusion. Something similar can be said for the zero-sum fight for corporate dominance: it may well be happening, but is it really so wasteful that huge productivity gains simply evaporate?

How, then, to resolve the puzzle? In the simplest way possible: to say, “just wait”. There is no contradiction between disappointing productivity growth now and spectacular productivity growth in the near future.

This is true in the narrow statistical sense that productivity growth tends to bounce around: a bad decade may be followed by another bad decade, or by a good one, and today’s productivity growth tells us little about tomorrow’s.

But it is also true that there tends to be a delay between a technical breakthrough and a productivity surge. The most famous case in point is the electric motor, which seemed poised to transform American manufacturing in the 1890s, but did not realise that potential until the 1920s. To take advantage of the new technology, factory owners had to turn their organisations upside down, with new architecture, processes and training. Prof Brynjolfsson’s early research in the 1990s found companies saw little benefit from investing in computers unless they also reorganised.

If the benefits of today’s new ideas are real but delayed, that may also explain the productivity slowdown itself. Consider the self-driving car: right now it is a research expense, all cost and no benefit. Later, it will start to displace traditional cars, the traditional car industry, and many related businesses from parking garages to automotive repair. Finally, perhaps decades after a self-driving car becomes feasible, the full benefits are likely to be apparent. One does not simply invent a new machine: economic progress requires much more than that.

Perhaps, then, this is a brief lull before an explosion of new technology that will radically reshape the world around us. Or perhaps we are due for another decade or two of disappointment. Either scenario seems possible — and both of them promise an uncomfortable ride.

 
Written for and first published in the Financial Times on 17 November 2017.

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The dangers of dark nudging

“If you want people to do the right thing, make it easy.”

That is the simplest possible summary of Nudge (UK) (US) by Cass Sunstein and Richard Thaler. We are all fallible creatures, and so benevolent policymakers need to make sure that the path of least resistance goes to a happy destination. It is a simple but important idea, and deservedly influential: Mr Sunstein became a senior adviser to President Obama, while Mr Thaler is this year’s winner of the Nobel memorial prize in economics.

Policy wonks have nudged people to sign up for organ donation, to increase their pension contributions — and even insulate their homes by coupling home insulation with an attic-decluttering service. All we have to do is make it easy for people to do the right thing.

But what if you want people to do the wrong thing? The answer: make that easy; or make the right thing difficult. Messrs Thaler and Sunstein are well aware of the risk of malign nudges, and have been searching for the right word to describe them. Mr Thaler likes “sludge” — obfuscatory language or procedures that accidentally or deliberately encourage inertia. Voter ID laws, he says, are a good example of sludge, calculated to softly disenfranchise. Meanwhile Mr Sunstein has written an entire book about the “ethics of influence” (UK) (US).

And as we are starting to realise, Vladimir Putin is well aware of the opportunity that behavioural science presents, too. Rumours circulate that the Russian authorities are keen recruiters of young psychologists and behavioural economists; I have no proof of that, but it seems like a reasonable thing for the Russian government to do. I am willing to bet that not all of them are working on attic-decluttering.

According to Richard Burr, chair of the US Senate intelligence committee, Russian troll accounts on Facebook managed to organise both a protest and a counter-protest in Houston, in May 2016. Americans are perfectly willing to face off against each other on the streets, but if you want it to happen more often, make it easy.

A number of other memes, political advertisements and provocateur accounts — both left- and rightwing — have since been identified as of Russian origin. Social media networks have unwittingly sold them air time; news sites have cited them; people have shared them, or spent effort refuting them. Nudge isn’t the word for this, but neither is sludge. What about “grudge”?

The Russians are not alone in using grudge theory to manipulate public opinion. Three social scientists — Gary King, Jennifer Pan and Margaret Roberts — recently managed to infiltrate networks of shills in China, who are paid to post helpful messages on Chinese social media. (Their nickname is the “50 cent army”.) Unlike the Russian trolls, their aim has been to avoid engaging “in debate or argument of any kind . . . they seem to avoid controversial issues entirely”. The tactic is, rather, to keep changing the subject, especially at politically sensitive moments, by talking about the weather, sports — anything. If you want potential protesters to make cheery small talk instead, make it easy.

Just as noble tools can be turned to wicked ends, so shady techniques can be used to do the work of the angels. For example, why not disrupt online markets for illegal drugs by leaving bad reviews for vendors? Research by social scientists Scott Duxbury and Dana Haynie suggests that because people rely on user reviews on illicit markets, law enforcement officers could attack those markets by faking negative reviews, thus undermining trust.

The parallel with Mr Putin is alarmingly clear: it is possible to attack democracy and rational discourse by creating an information ecosystem where everyone yells at everyone else and nobody believes anything.

But we should not give too much credit to Mr Putin. He did not create the information ecosystem of the western world; we did. The Russians just gave us a push, and probably not a very big push at that. Perhaps I should say they gave us a nudge.

Social media do seem vulnerable to dark nudges from foreign powers. But more worrying is our vulnerability to smears, skews and superficiality without any outside intervention at all. Messrs Sunstein and Thaler ask policymakers to make it easy to do the right thing; what have we made it easy to do?

It is easy to find a like-minded tribe. It is easy to share, retweet or “like” something we have not even read. It is easy to repeat false claims. It is easy to get angry or personal.

It’s less easy to distinguish truth from lies, to clear time and attention to read something deep, and to reward an important article with something more than a digital thumbs up. But then, none of this is fundamental to the business model of many media companies — or of the social media networks that spread the news.

Nudge, sludge or grudge, we can change this. And we should start by asking ourselves whether when it comes to news, information and debate, we have made it difficult to do the right thing — and all too easy to stray.

 

 
Written for and first published in the Financial Times on 10 November 2017.

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A way to poke Facebook off its uncontested perch

We need to talk about Facebook. Google (or Alphabet, if you prefer) is more ubiquitous; Apple makes more money; Amazon is a more obvious threat to the bricks-and-mortar economy; yet there is something uniquely troubling about the social media leviathan.

One concern is Facebook’s unwholesome contribution to our diet of information. Because what we see in Facebook is a function of what our friends share, the site echoes our prejudices. This effect is accentuated — at least modestly — by Facebook’s own algorithms, which have learnt to show us more of what we like to keep our eyeballs on the site.

Then there is accuracy. Whether what we are shown is true or false does not much matter for Facebook’s business model, unless we start to show more interest in not being lied to. For now, fake news entrepreneurs have realised that it is far more profitable to invent eye-catching fables than to research and confirm the everyday truth.

We are also beginning to realise that Facebook is the perfect vector for carefully-targeted advertisements containing dark political smears. A false claim in a TV spot or the side of a campaign bus can be challenged; a false claim carefully targeted to a few thousand voters in a swing state may go unchallenged and, for that matter, unnoticed except by the intended few.

These problems are sometimes exaggerated, and are not Facebook’s alone: Twitter is politically polarised; Google also shows targeted ads; and few Facebook news feeds are as relentlessly blinkered as the pages of a British tabloid newspaper. But Facebook bundles them into a uniquely powerful package.

And the inconvenient fact is that Facebook seems to make us miserable. We log on like joyless addicts, two billion of us each month. I doubt that we truly value Facebook. But we use it anyway. Writing in the London Review of Books, John Lanchester cites numerous studies that suggest Facebook use goes hand in hand with envy and sadness, and quite plausibly causes them. It is also a notorious time-sink and source of distraction.

None of this is good, unless you are Facebook. But behind all these injuries is a final insult: there is no serious alternative. Buyers of Microsoft’s Office and Apple’s iPhone could choose something else. Even dominant services such as Google’s search or Amazon’s store could in principle be challenged. It would be no easy thing to build a better rival, but anyone who did would be just a click away.

In contrast, making a superior social network app is not enough to unseat Facebook: the main appeal of the site is that everyone already uses it. A rival social network would need to somehow attract groups of users en masse, an extremely difficult prospect. Two of the companies that were managing it — WhatsApp and Instagram — were bought by Facebook. It is hard to understand why regulators thought these mergers were benign.

The lack of competition may explain why Facebook retains its grip on our attention despite being clunky and pernicious; a company that faces no serious competition can afford to stop worrying about keeping its users happy. It is easy to imagine a better social network than Facebook: more privacy, a slicker interface, and less fake news. It is not so easy to see how such a rival could tempt entire social groups to migrate together.

Could regulators change this? Perhaps. They could certainly have been more aggressive in scrutinising mergers. But traditional measures such as price regulation seem less relevant to what is, after all, a free service.  Instead, we should ask ourselves if we can find a way to re-introduce serious competition in social networking.

Luigi Zingales and Guy Rolnik of the University of Chicago have proposed an intriguing idea. They build on the concept of “number portability”, the principle that you own your own phone number, and you can take your number with you to a different phone provider. The idea has promise in retail banking.

Zingales and Rolnik suggest an analogy: social graph portability. The idea is that I could take my Facebook contacts with me to another service — call it “ZingBook”. I could read their Facebook posts on ZingBook and they could see my ZingBook posts over on Facebook.  I can send emails from any program or service provider to any other, so why not guarantee interconnection between social networks? I would get whatever it was I liked about ZingBook while maintaining contact with my own social network back on Facebook.

In practice, the Zingales/Rolnik idea faces serious stumbling blocks — making the technology work, preventing cheating, and navigating permissions. If a friend decides to move over to, say, NaziBook, will he still receive my Facebook content? Will I even know where my words are now being viewed?  But the idea of social graph portability squarely addresses one of the big issues of 21st-century economic policy. The new tech titans need serious competition. For a social network, serious competition needs new rules to enable it.
Written for and first published in the Financial Times on 3 November 2017.

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What AlphaGo Zero teaches us about what’s going wrong with innovation

It is hard not to be impressed — and perhaps a little alarmed — by the progression. In 1997, IBM’s supercomputer Deep Blue beat the world’s greatest chess player, Garry Kasparov. It was a hugely expensive piece of hardware, closely tended and coached by humans.

Go is a far harder game for computers to master than chess. Yet when the AlphaGo programme emerged with muted fanfare in 2016, it comfortably outclassed the world’s best Go players after a few months of training.

Then last week, the AI research firm DeepMind unveiled AlphaGo Zero. It is faster, uses less hardware, beat its predecessor AlphaGo by 100 games to none, and is entirely self-taught. What is more, it achieved this performance after just 72 hours of practice.

The bewildering progress of AlphaGo Zero has fed an already-febrile anxiety about a robot takeover causing mass unemployment. Yet that anxiety sits uneasily with the high employment rates and disappointing productivity growth we see in the US and particularly the UK. There are plenty of jobs, but apparently not a lot of innovation.

There are various possible explanations for this paradox, but the simplest one is this: AlphaGo Zero is an outlier. Productivity and technological progress are lacklustre because the research behind AlphaGo Zero is not typical of the way we try to produce new ideas.

Mr Kasparov’s own perspective on this is fascinating. In his recent book, Deep Thinking (UK) (US), he quotes the late computer scientist Alan Perlis: “Optimization hinders evolution”. In the case of computer chess, Perlis’s maxim describes researchers who chose pragmatic short-cuts for quick results. Deeper, riskier research was neglected. IBM’s priority with Deep Blue was not knowledge, but victory — and victory was a scientific dead end.

That is a shame. Computing pioneers such as Alan Turing and Claude Shannon (UK) (US) believed that chess might be a fertile field of research to develop artificial intelligence in more meaningful areas. This hope was quickly sidelined by brute-force approaches that taught us little but played strong chess.

It is easy to see why a commercial company would have had little interest in the early pattern-recognition techniques now refined by AlphaGo. Mr Kasparov describes an attempt to use them in chess; observing that grandmasters promptly won games in which they had sacrificed their queens, the machine concluded that it should sacrifice its own queen at every opportunity. Yet in the end, these pattern-recognition techniques have proved far more powerful and generally applicable than the methods used by the best chess-playing computers; the question is whether we wish to change our world, or merely win a chess game.

This is not just a cautionary tale about chess. Corporations have reined in their ambitions elsewhere. Corporate research laboratories once bankrolled fundamental research of the highest importance. Leo Esaki of Sony and IBM won a Nobel Prize in physics, as did Jack Kilby of Texas Instruments. Irving Langmuir of General Electric won a Nobel in chemistry. Bell Labs boasted too many Nobel laureates to list — along with Shannon himself. It was a time when companies weren’t afraid to invest in basic science.

That has changed, as a research paper from three economists — Ashish Arora, Sharon Belenzon, and Andrea Patacconi — shows. Companies still invest heavily in innovation, but the focus is on practical applications rather than basic science, and research is often outsourced to smaller outfits whose intellectual property can easily be bought and sold.

Corporate researchers produce more patents but they are less visible in the pages of learned journals. As Prof Arora puts it, research and development has become “less R, more D”. The AlphaGo research, he says, is an exception. And this matters because most basic research ends up being commercially useful eventually. We like the golden eggs, but we may be starving the golden goose.

All this need not be disastrous if other research bodies such as universities fill in the gap. Yet this is not something to take for granted. As the economist Benjamin F. Jones has documented, new ideas are harder to find. One sign of this is the complexity of research teams, which are larger, full of increasingly specialised researchers and ever costlier.

Perhaps it is naive to simply exhort companies to spend more on fundamental research — but somebody has to. One interesting approach is for governments to fund “innovation prizes” for breakthroughs. Such prizes mobilise public funds and public goals while deploying the agility and diversity of private sector approaches. But such prizes only work in certain situations.

Professional sport has made fashionable the practice of “marginal gains” — rapid optimisation in search of the tiniest edge. It turns out that corporate research took the same turn decades ago. There is nothing wrong with marginal improvements, but they must not be allowed to crowd out more speculative research. Science is a deeper, messier practice than sport. We must continue to devote time, space and money to bigger, riskier leaps.

Written for and first published in the Financial Times on 27 Oct 2017.

My new book is “Fifty Inventions That Shaped The Modern Economy”. Grab yourself a copy in the US or in the UK (slightly different title) or through your local bookshop.

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Undercover Economist

Disruption can be a benefit – shame our politicians are giving us the wrong sort

“We seem to be living in the riskiest moment of our lives, and yet the stock market seems to be napping,” said Richard Thaler recently. He is not alone in expressing such views, although since he had just won the Nobel memorial prize in economics, his comment understandably drew attention.

Money is loose and global economic growth is robust, yet the strength and the stability of the S&P 500 is still puzzling. This week it was the 30th anniversary of Black Monday, the largest-ever one-day crash in US stock prices, and it is eerily quiet.

One explanation of the puzzle is that markets have finally got smart: when behavioural economists turned their attention to financial markets in the 1980s, two key findings were that shares were implausibly volatile and implausibly cheap. They are now much less volatile and much less cheap; perhaps that is simply a long-awaited recognition of the way things should be. Perhaps.

An alternative view is that, while 2017 seems risky, it really isn’t. This is hard to believe. Among the obvious political risks: Donald Trump’s attempts to undermine both the North American Free Trade Agreement and the Iran nuclear deal; the rise of the far right in Germany and Austria, with Marine Le Pen waiting for President Emmanuel Macron to stumble in France; serious unrest in Spain over the Catalan independence movement; a Brexit process that seems as unclear as ever; and of course, the small-yet-conceivable chance of a nuclear war on the Korean peninsula. This is no time to panic, but it hardly seems a time for euphoria either.

There is never a good moment to create uncertainty over the prospects of a nuclear first strike. As the great Thomas Schelling once explained: “If I go downstairs to investigate a noise at night, with a gun in my hand, and find myself face to face with a burglar who has a gun in his hand, there is a danger of an outcome that neither of us desires. Even if he prefers to just leave quietly, and I wish him to, there is danger.” The danger results from mutual uncertainty over what the other person may do.

Other threats are less apocalyptic, but still have the potential to cause serious economic harm. Those who think Brexit will boost the British economy — I am not among them — have begun to acknowledge that the uncertainty is damaging. Business can no longer afford to wait for the British government to finish negotiations with itself and begin serious negotiations with the EU27.

Similar damage is likely to be done by Donald Trump’s attacks on Nafta. As the economist Nuno Limão has shown, unpredictable trade policy is itself a form of trade barrier.

And yet, despite all these political risks, the world economy might benefit from a little more disruption. Whether a more volatile stock market might usefully puncture complacency, in the real economy volatility can be an asset. We should expect old companies to fade and die, being absorbed or replaced by fresh ideas: that corporate failure is the flip side of economic vitality.

For example, a study by Kathy Fogel, Randall Morck and Bernard Yeung — published in 2008 — compiled lists of the 10 largest employers in each of 44 countries across the world. More churn in the list was a sign of a strongly growing economy, and a predictor of fast growth to come, too. The results were being driven by the extinction of corporate dinosaurs more than the rapid ascent of new stars.

The upbeat word to describe this process of success through failure is dynamism. But economic dynamism is at risk. The economist John Haltiwanger has charted a fall since the early 1980s in the rate of start-ups, business exits, job creation and job destruction. It is probably not a coincidence that low-productivity companies are able to limp on rather than disappearing.

Calm waters eventually stagnate. It is time to agitate the real economy. But how? Even acts of economic vandalism such as a train-crash Brexit can have unexpected benefits, much as Tube strikes have been shown to help some commuters discover better routes. Yet overall the costs of chaos seem likely to outweigh the benefits.

There are more positive ways to shake things up: looser planning regulations in the sclerotic UK economy, more infrastructure in much of the western world, and support for small-business finance, would all add much needed fizz to the economic system.

And the authorities could be much more assertive in challenging market power. According to the economist Luigi Zingales, federal antitrust cases in the US were five times more common between 1970 and 1999 than since the year 2000. In a world of high profits and high concentration, that passivity is hard to excuse.

I have little doubt that financial markets will rediscover the knack of panicking in due course, but the real economy may need more help. If only our politicians would stop shaking the nitro-glycerine of geopolitics, and start stirring the cocktail of market forces instead.

Written for and first published in the Financial Times on 20 October 2017.

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Undercover Economist

Richard Thaler: How to change minds and influence people

The best thing about Thaler, what really makes him special, is that he is lazy.” So said Daniel Kahneman, winner in 2002 of the Nobel memorial prize in economics. Prof Kahneman was talking about Richard Thaler, who has emulated that achievement 15 years later. Prof Thaler’s thesis adviser, the economist Sherwin Rosen, put it differently: “We didn’t expect much of him.”

The story of how a lazy and unpromising man won a Nobel memorial prize is perhaps just as important as what he won the prize for. The Nobel announcement recognised Prof Thaler “for his contributions to behavioural economics”. But there’s another way to describe the way he reshaped economics: he persuaded a large group of successful people with a strongly held view of the world to change their minds.

What was that view? To oversimplify, it was that all of us are Spock-like rational optimisers, able to instantly trade off risk and reward, rebalance a spending plan in the face of a price change, and resist temptations such as chocolate brownies or payday loans.

Of course, no economist has ever quite believed this. But for several decades most economists believed that departures from the world of Spock were small enough, rare enough and random enough that they could be ignored. Humans weren’t quite like Spock, yet when building economic models and formulating economic policies, we could treat them as if they were.

This approach is not as absurd as it might seem. It’s flexible, powerful, and consistent. It is often close enough to reality to be useful. Prof Thaler himself told me: “If you want one unifying theory of economic behaviour, you won’t do better than the neoclassical model.”

Yet the power of the neoclassical approach made it hard to challenge. Prof Thaler wasn’t the first Nobel laureate to operate outside that paradigm — others include Maurice Allais, Herb Simon and Thomas Schelling. Yet all these men, while admired, did not manage to divert the mainstream of economic thought beyond the well-worn channel of rational optimisation. It was Prof Thaler who shifted the norms of how economics is practised, both in academia and in the policy world.

Behavioural economics is now respectable in places from the American Economic Review to the World Bank. Whether or not you think behavioural economics matters, as a feat of persuading people to change their minds this is a case worth studying. So how did he do it? We could all do with knowing, because the world is full of stubborn-minded people who need to be persuaded to change their views about important things.

Part of the story is simple persistence: Prof Thaler’s first behavioural economics paper was published in 1980; he has been banging this drum for a long time. More important was that Prof Thaler fully understood what he was criticising. It is all too easy to attack those with whom we disagree based on the haziest idea of what they think and why they think it. But he grasped perfectly why his fellow economists embraced rationality, and the arguments (good and bad) they used to defend it. Prof Thaler engaged honestly and thoughtfully with the mainstream.

His third technique was to look at the facts — not only clever statistics, but everyday facts about human existence. We find snack food hard to resist. We divide up money into separate mental accounts — rainy-day money, an entertainment budget, money for food, money for clothes. If we find a fine old bottle of port in the attic, we might refuse to sell it for hundreds of pounds, even though we would not dream of spending a three figure sum on a bottle of anything. Having secured agreement on these facts, he then moved to arguing that they might matter.

Finally, Prof Thaler engaged people’s sense of curiosity. His long running series “Anomalies”, published in the widely-read scholarly Journal of Economic Perspectives, would often begin with a puzzle — some piece of behaviour or pattern in the data that simply didn’t make sense from the mainstream point of view. He would then explore the puzzle, extend it, and consider various possible solutions.

Economists would talk about these anomalies in faculty coffee rooms. They would, at Prof Thaler’s invitation, send in their own suggestions. Rather than telling his opponents they were wrong, Prof Thaler would present a conundrum and invite everyone to discuss it together. One of his critics, the great Chicago economist Merton Miller, was reduced to complaining that Prof Thaler’s anomalies were a distraction from serious modelling because they were simply too interesting.

Which brings us back to his laziness. Prof Kahneman thought Prof Thaler’s laziness made him “special” because it meant that he could only be bothered to work on the most fascinating questions. Maybe.

But perhaps the truth is that laziness isn’t special at all. Prof Thaler realised that most of us are lazy. Most of us don’t want to think hard about our beliefs, or challenges to them. His solution was to make sure those challenges were simply too intriguing to ignore.

Written for and first published in the Financial Times on 13 Oct 2017.

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Undercover Economist

Monty Hall and the game show stick-or-switch conundrum

Forget Fermat’s last theorem. The most vexing challenge in mathematics just might be the Monty Hall problem. Monty Hall — born Monte Halparin — presented nearly 5,000 episodes of Let’s Make a Deal, the US game show that inspired the puzzle.

It is an onion of a conundrum; layer after layer, and guaranteed to make you cry. The puzzle is this: a contestant faces three doors. Behind one of them is a big prize such as a Cadillac. Each of the other two doors conceals a booby prize such as a goat.

The contestant chooses a door, hoping to win the grand prize. But just as the door is about to be opened, Hall steps in and halts proceedings. He opens one of the other two doors instead, revealing a goat. Then he turns to the contestant. Would they like to switch to the other closed door? Or would they prefer to stick with their original choice?

The problem was initially posed in The American Statistician in 1975, by Steve Selvin, but achieved national prominence when Marilyn vos Savant wrote about it in Parade in 1990. She suggested that it pays to switch doors. Her reward was a mailbag full of letters assuring her she was wrong — some from prominent mathematicians. John Kay’s inbox also overflowed when he addressed the problem in 2005 in the Financial Times.

What should the contestant do? Mathematically, it seems not to matter: there are two doors now, so surely they face a 50-50 proposition. And as it happens, many people prefer to stick. They have made their choice and would regret switching if it did not work out.

More careful analysis, however, reveals that the contestant should switch. One way to think of the problem is to notice that their chance of picking the grand prize was initially one in three.

Hall’s intervention does not change that, but it does guarantee that if they failed to pick the correct door initially then they will definitely get the prize by switching. Two times out of three, switching will win the prize.

A second way to think about the problem is to exaggerate the underlying process. Imagine 100 doors, but still only one grand prize. The contestant picks a door, probably not the correct one. Hall then opens 98 other doors, revealing no prize.

Should we really conclude that we have learnt nothing about the other door? The first door was picked at random but the one that Hall has left closed was selected with great care. With probability of 99 per cent, switching will win the prize.

A third way to attack the puzzle is to run an experiment. It will quickly reveal what intuition does not: the contestant should switch. In Ms vos Savant’s experience, many mathematicians changed their minds only on the basis of empirical evidence — which is revealing, since the underlying proof, using Bayes’ theorem, is not especially technical.

Some people will find these explanations persuasive, and others will not. Over the years I have concluded that there is something about the Monty Hall problem that makes it wonderfully resistant to our intuitions.

And there is a twist in the tale, too: after Ms vos Savant brought the problem to national attention, the journalist John Tierney visited Hall himself and they began to play the game repeatedly at his dining-room table, with car keys representing the grand prize and a pack of raisins serving as the goat. At first, things went as Mr Selvin and Ms vos Savant had explained: switching won the prize far more often.

Then, suddenly, things changed. At the beginning of a game, Mr Tierney pointed to one of the options. “Too bad,” said Hall, immediately. “You’ve just won a goat.”

He did not offer Mr Tierney a chance to switch. He did not always make such an offer in the game show — why should he make it now?

Hall’s change of approach turns a probability puzzle into what we might call a cheesecake bet. In the musical Guys and Dolls, Nathan Detroit offers Sky Masterson a bet that Mindy’s sells more strudel than cheesecake. Sky is sceptical, and rightly so, since Nathan already knows the answer. For much the same reason, a contestant in Let’s Make a Deal should ask themself: “If it is really such a good idea to switch, then why has Hall offered me the chance?”

I have great respect for the way Ms vos Savant faced down a posse of contemptuous mathematicians. But we must be careful not to confuse a precise mathematical description of a game for the vagaries of reality itself, something Nassim Nicholas Taleb has named the “ludic fallacy”. Rigorous mathematical thinking can be invaluable, or it can leave you blinkered and on the wrong side of a cheesecake bet.

The solution to the formal Monty Hall problem is counterintuitive and incontrovertible. But the right approach in the game show depended on what Hall himself was trying to do in offering the choice. Was he benevolent, malevolent, or simply aiming for great television?

Alas, we can no longer ask him. Hall died in September. But the Monty Hall problem will live on.

 
Written for and first published in the Financial Times on 6 October 2017.

My new book is “Fifty Inventions That Shaped The Modern Economy”. Grab yourself a copy in the US or in the UK (slightly different title) or through your local bookshop.

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