From the geeks who took over poker to the nuclear safety experts who want to prevent the next banking meltdown, these are my favourite long-form articles.
Modern life now forces us to do a multitude of things at once — but can we? Should we?
Forget invisibility or flight: the superpower we all want is the ability to do several things at once. Unlike other superpowers, however, being able to multitask is now widely regarded as a basic requirement for employability. Some of us sport computers with multiple screens, to allow tweeting while trading pork bellies and frozen orange juice. Others make do with reading a Kindle while poking at a smartphone and glancing at a television in the corner with its two rows of scrolling subtitles. We think nothing of sending an email to a colleague to suggest a quick coffee break, because we can feel confident that the email will be read within minutes.
All this is simply the way the modern world works. Multitasking is like being able to read or add up, so fundamental that it is taken for granted. Doing one thing at a time is for losers — recall Lyndon Johnson’s often bowdlerised dismissal of Gerald Ford: “He can’t fart and chew gum at the same time.”
The rise of multitasking is fuelled by technology, of course, and by social change as well. Husbands and wives no longer specialise as breadwinners and homemakers; each must now do both. Work and play blur. Your friends can reach you on your work email account at 10 o’clock in the morning, while your boss can reach you on your mobile phone at 10 o’clock at night. You can do your weekly shop sitting at your desk and you can handle a work query in the queue at the supermarket.
This is good news in many ways — how wonderful to be able to get things done in what would once have been wasted time! How delightful the variety of it all is! No longer must we live in a monotonous, Taylorist world where we must painstakingly focus on repetitive tasks until we lose our minds.
And yet we are starting to realise that the blessings of a multitasking life are mixed. We feel overwhelmed by the sheer number of things we might plausibly be doing at any one time, and by the feeling that we are on call at any moment.
And we fret about the unearthly appetite of our children to do everything at once, flipping through homework while chatting on WhatsApp, listening to music and watching Game of Thrones. (According to a recent study by Sabrina Pabilonia of the US Bureau of Labor Statistics, for over half the time that high-school students spend doing homework, they are also listening to music, watching TV or otherwise multitasking. That trend is on the increase.) Can they really handle all these inputs at once? They seem to think so, despite various studies suggesting otherwise.
And so a backlash against multitasking has begun — a kind of Luddite self-help campaign. The poster child for uni-tasking was launched on the crowdfunding website Kickstarter in December 2014. For $499 — substantially more than a multifunctional laptop — “The Hemingwrite” computer promised a nice keyboard, a small e-ink screen and an automatic cloud back-up. You couldn’t email on the Hemingwrite. You couldn’t fool around on YouTube, and you couldn’t read the news. All you could do was type. The Hemingwrite campaign raised over a third of a million dollars.
The Hemingwrite (now rebranded the Freewrite) represents an increasingly popular response to the multitasking problem: abstinence. Programs such as Freedom and Self-Control are now available to disable your browser for a preset period of time. The popular blogging platform WordPress offers “distraction-free writing”. The Villa Stéphanie, a hotel in Baden-Baden, offers what has been branded the “ultimate luxury”: a small silver switch beside the hotel bed that will activate a wireless blocker and keep the internet and all its temptations away.
The battle lines have been drawn. On one side: the culture of the modern workplace, which demands that most of us should be open to interruption at any time. On the other, the uni-tasking refuseniks who insist that multitaskers are deluding themselves, and that focus is essential. Who is right?
The ‘cognitive cost’
There is ample evidence in favour of the proposition that we should focus on one thing at a time. Consider a study led by David Strayer, a psychologist at the University of Utah. In 2006, Strayer and his colleagues used a high-fidelity driving simulator to compare the performance of drivers who were chatting on a mobile phone to drivers who had drunk enough alcohol to be at the legal blood-alcohol limit in the US. Chatting drivers didn’t adopt the aggressive, risk-taking style of drunk drivers but they were unsafe in other ways. They took much longer to respond to events outside the car, and they failed to notice a lot of the visual cues around them. Strayer’s infamous conclusion: driving while using a mobile phone is as dangerous as driving while drunk.
Less famous was Strayer’s finding that it made no difference whether the driver was using a handheld or hands-free phone. The problem with talking while driving is not a shortage of hands. It is a shortage of mental bandwidth.
Yet this discovery has made little impression either on public opinion or on the law. In the United Kingdom, for example, it is an offence to use a hand-held phone while driving but perfectly legal if the phone is used hands-free. We’re happy to acknowledge that we only have two hands but refuse to admit that we only have one brain.
Another study by Strayer, David Sanbonmatsu and others, suggested that we are also poor judges of our ability to multitask. The subjects who reported doing a lot of multitasking were also the ones who performed poorly on tests of multitasking ability. They systematically overrated their ability to multitask and they displayed poor impulse control. In other words, wanting to multitask is a good sign that you should not be multitasking.
We may not immediately realise how multitasking is hampering us. The first time I took to Twitter to comment on a public event was during a televised prime-ministerial debate in 2010. The sense of buzz was fun; I could watch the candidates argue and the twitterati respond, compose my own 140-character profundities and see them being shared. I felt fully engaged with everything that was happening. Yet at the end of the debate I realised, to my surprise, that I couldn’t remember anything that Brown, Cameron and Clegg had said.
A study conducted at UCLA in 2006 suggests that my experience is not unusual. Three psychologists, Karin Foerde, Barbara Knowlton and Russell Poldrack, recruited students to look at a series of flashcards with symbols on them, and then to make predictions based on patterns they had recognised. Some of these prediction tasks were done in a multitasking environment, where the students also had to listen to low- and high-pitched tones and count the high-pitched ones. You might think that making predictions while also counting beeps was too much for the students to handle. It wasn’t. They were equally competent at spotting patterns with or without the note-counting task.
But here’s the catch: when the researchers then followed up by asking more abstract questions about the patterns, the cognitive cost of the multitasking became clear. The students struggled to answer questions about the predictions they’d made in the multitasking environment. They had successfully juggled both tasks in the moment — but they hadn’t learnt anything that they could apply in a different context.
That’s an unnerving discovery. When we are sending email in the middle of a tedious meeting, we may nevertheless feel that we’re taking in what is being said. A student may be confident that neither Snapchat nor the live football is preventing them taking in their revision notes. But the UCLA findings suggest that this feeling of understanding may be an illusion and that, later, we’ll find ourselves unable to remember much, or to apply our knowledge flexibly. So, multitasking can make us forgetful — one more way in which multitaskers are a little bit like drunks.
All this is unnerving, given that the modern world makes multitasking almost inescapable. But perhaps we shouldn’t worry too much. Long before multitasking became ubiquitous, it had a long and distinguished history.
In 1958, a young psychologist named Bernice Eiduson embarked on an long-term research project — so long-term, in fact, that Eiduson died before it was completed. Eiduson studied the working methods of 40 scientists, all men. She interviewed them periodically over two decades and put them through various psychological tests. Some of these scientists found their careers fizzling out, while others went on to great success. Four won Nobel Prizes and two others were widely regarded as serious Nobel contenders. Several more were invited to join the National Academy of Sciences.
After Eiduson died, some of her colleagues published an analysis of her work. These colleagues, Robert Root-Bernstein, Maurine Bernstein and Helen Garnier, wanted to understand what determined whether a scientist would have a long productive career, a combination of genius and longevity.
There was no clue in the interviews or the psychological tests. But looking at the early publication record of these scientists — their first 100 published research papers — researchers discovered a pattern: the top scientists were constantly changing the focus of their research.
Over the course of these first 100 papers, the most productive scientists covered five different research areas and moved from one of these topics to another an average of 43 times. They would publish, and change the subject, publish again, and change the subject again. Since most scientific research takes an extended period of time, the subjects must have overlapped. The secret to a long and highly productive scientific career? It’s multitasking.
Charles Darwin thrived on spinning multiple plates. He began his first notebook on “transmutation of species” two decades before The Origin of Species was published. His A Biographical Sketch of an Infant was based on notes made after his son William was born; William was 37 when he published. Darwin spent nearly 20 years working on climbing and insectivorous plants. And Darwin published a learned book on earthworms in 1881, just before his death. He had been working on it for 44 years. When two psychologists, Howard Gruber and Sara Davis, studied Darwin and other celebrated artists and scientists they concluded that such overlapping interests were common.
Another team of psychologists, led by Mihaly Csikszentmihalyi, interviewed almost 100 exceptionally creative people from jazz pianist Oscar Peterson to science writer Stephen Jay Gould to double Nobel laureate, the physicist John Bardeen. Csikszentmihalyi is famous for developing the idea of “flow”, the blissful state of being so absorbed in a challenge that one loses track of time and sets all distractions to one side. Yet every one of Csikszentmihalyi’s interviewees made a practice of keeping several projects bubbling away simultaneously.
Just internet addiction?
If the word “multitasking” can apply to both Darwin and a teenager with a serious Instagram habit, there is probably some benefit in defining our terms. There are at least four different things we might mean when we talk about multitasking. One is genuine multitasking: patting your head while rubbing your stomach; playing the piano and singing; farting while chewing gum. Genuine multitasking is possible, but at least one of the tasks needs to be so practised as to be done without thinking.
Then there’s the challenge of creating a presentation for your boss while also fielding phone calls for your boss and keeping an eye on email in case your boss wants you. This isn’t multitasking in the same sense. A better term is task switching, as our attention flits between the presentation, the telephone and the inbox. A great deal of what we call multitasking is in fact rapid task switching.
Task switching is often confused with a third, quite different activity — the guilty pleasure of disappearing down an unending click-hole of celebrity gossip and social media updates. There is a difference between the person who reads half a page of a journal article, then stops to write some notes about a possible future project, then goes back to the article — and someone who reads half a page of a journal article before clicking on bikini pictures for the rest of the morning. “What we’re often calling multitasking is in fact internet addiction,” says Shelley Carson, a psychologist and author of Your Creative Brain. “It’s a compulsive act, not an act of multitasking.”
A final kind of multitasking isn’t a way of getting things done but simply the condition of having a lot of things to do. The car needs to be taken in for a service. Your tooth is hurting. The nanny can’t pick up the kids from school today. There’s a big sales meeting to prepare for tomorrow, and your tax return is due next week. There are so many things that have to be done, so many responsibilities to attend to. Having a lot of things to do is not the same as doing them all at once. It’s just life. And it is not necessarily a stumbling block to getting things done — as Bernice Eiduson discovered as she tracked scientists on their way to their Nobel Prizes.
The fight for focus
These four practices — multitasking, task switching, getting distracted and managing multiple projects — all fit under the label “multitasking”. This is not just because of a simple linguistic confusion. The versatile networked devices we use tend to blur the distinction, serving us as we move from task to task while also offering an unlimited buffet of distractions. But the different kinds of multitasking are linked in other ways too. In particular, the highly productive practice of having multiple projects invites the less-than-productive habit of rapid task switching.
To see why, consider a story that psychologists like to tell about a restaurant near Berlin University in the 1920s. (It is retold in Willpower, a book by Roy Baumeister and John Tierney.) The story has it that when a large group of academics descended upon the restaurant, the waiter stood and calmly nodded as each new item was added to their complicated order. He wrote nothing down, but when he returned with the food his memory had been flawless. The academics left, still talking about the prodigious feat; but when one of them hurried back to retrieve something he’d left behind, the waiter had no recollection of him. How could the waiter have suddenly become so absent-minded? “Very simple,” he said. “When the order has been completed, I forget it.”
One member of the Berlin school was a young experimental psychologist named Bluma Zeigarnik. Intrigued, she demonstrated that people have a better recollection of uncompleted tasks. This is called the “Zeigarnik effect”: when we leave things unfinished, we can’t quite let go of them mentally. Our subconscious keeps reminding us that the task needs attention.
The Zeigarnik effect may explain the connection between facing multiple responsibilities and indulging in rapid task switching. We flit from task to task to task because we can’t forget about all of the things that we haven’t yet finished. We flit from task to task to task because we’re trying to get the nagging voices in our head to shut up.
Of course, there is much to be said for “focus”. But there is much to be said for copperplate handwriting, too, and for having a butler. The world has moved on. There’s something appealing about the Hemingwrite and the hotel room that will make the internet go away, but also something futile.
It is probably not true that Facebook is all that stands between you and literary greatness. And in most office environments, the Hemingwrite is not the tool that will win you promotion. You are not Ernest Hemingway, and you do not get to simply ignore emails from your colleagues.
If focus is going to have a chance, it’s going to have to fight an asymmetric war. Focus can only survive if it can reach an accommodation with the demands of a multitasking world.
Loops and lists
The word “multitasking” wasn’t applied to humans until the 1990s, but it has been used to describe computers for half a century. According to the Oxford English Dictionary, it was first used in print in 1966, when the magazine Datamation described a computer capable of appearing to perform several operations at the same time.
Just as with humans, computers typically create the illusion of multitasking by switching tasks rapidly. Computers perform the switching more quickly, of course, and they don’t take 20 minutes to get back on track after an interruption.
Nor does a computer fret about what is not being done. While rotating a polygon and sending text to the printer, it feels no guilt that the mouse has been left unchecked for the past 16 milliseconds. The mouse’s time will come. Being a computer means never having to worry about the Zeigarnik effect.
Is there a lesson in this for distractible sacks of flesh like you and me? How can we keep a sense of control despite the incessant guilt of all the things we haven’t finished?
“Whenever you say to someone, ‘I’ll get back to you about that’, you just opened a loop in your brain,” says David Allen. Allen is the author of a cult productivity book called Getting Things Done. “That loop will keep spinning until you put a placeholder in a system you can trust.”
Modern life is always inviting us to open more of those loops. It isn’t necessarily that we have more work to do, but that we have more kinds of work that we ought to be doing at any given moment. Tasks now bleed into each other unforgivingly. Whatever we’re doing, we can’t escape the sense that perhaps we should be doing something else. It’s these overlapping possibilities that take the mental toll.
The principle behind Getting Things Done is simple: close the open loops. The details can become rather involved but the method is straightforward. For every single commitment you’ve made to yourself or to someone else, write down the very next thing you plan to do. Review your lists of next actions frequently enough to give you confidence that you won’t miss anything.
This method has a cult following, and practical experience suggests that many people find it enormously helpful — including me (see below). Only recently, however, did the psychologists E J Masicampo and Roy Baumeister find some academic evidence to explain why people find relief by using David Allen’s system. Masicampo and Baumeister found that you don’t need to complete a task to banish the Zeigarnik effect. Making a specific plan will do just as well. Write down your next action and you quiet that nagging voice at the back of your head. You are outsourcing your anxiety to a piece of paper.
A creative edge?
It is probably a wise idea to leave rapid task switching to the computers. Yet even frenetic flipping between Facebook, email and a document can have some benefits alongside the costs.
The psychologist Shelley Carson and her student Justin Moore recently recruited experimental subjects for a test of rapid task switching. Each subject was given a pair of tasks to do: crack a set of anagrams and read an article from an academic journal. These tasks were presented on a computer screen, and for half of the subjects they were presented sequentially — first solve the anagrams, then read the article. For the other half of the experimental group, the computer switched every two-and-a-half minutes between the anagrams and the journal article, forcing the subjects to change mental gears many times.
Unsurprisingly, task switching slowed the subjects down and scrambled their thinking. They solved fewer anagrams and performed poorly on a test of reading comprehension when forced to refocus every 150 seconds.
But the multitasking treatment did have a benefit. Subjects who had been task switching became more creative. To be specific, their scores on tests of “divergent” thinking improved. Such tests ask subjects to pour out multiple answers to odd questions. They might be asked to think of as many uses as possible for a rolling pin or to list all the consequences they could summon to mind of a world where everyone has three arms. Involuntary multitaskers produced a greater volume and variety of answers, and their answers were more original too.
“It seems that switching back and forth between tasks primed people for creativity,” says Carson, who is an adjunct professor at Harvard. The results of her work with Moore have not yet been published, and one might reasonably object that such tasks are trivial measures of creativity. Carson responds that scores on these laboratory tests of divergent thinking are correlated with substantial creative achievements such as publishing a novel, producing a professional stage show or creating an award-winning piece of visual art. For those who insist that great work can only be achieved through superhuman focus, think long and hard on this discovery.
Carson and colleagues have found an association between significant creative achievement and a trait psychologists term “low latent inhibition”. Latent inhibition is the filter that all mammals have that allows them to tune out apparently irrelevant stimuli. It would be crippling to listen to every conversation in the open-plan office and the hum of the air conditioning, while counting the number of people who walk past the office window. Latent inhibition is what saves us from having to do so. These subconscious filters let us walk through the world without being overwhelmed by all the different stimuli it hurls at us.
And yet people whose filters are a little bit porous have a big creative edge. Think on that, uni-taskers: while you busily try to focus on one thing at a time, the people who struggle to filter out the buzz of the world are being reviewed in The New Yorker.
“You’re letting more information into your cognitive workspace, and that information can be consciously or unconsciously combined,” says Carson. Two other psychologists, Holly White and Priti Shah, found a similar pattern for people suffering from attention deficit hyperactivity disorder (ADHD).
It would be wrong to romanticise potentially disabling conditions such as ADHD. All these studies were conducted on university students, people who had already demonstrated an ability to function well. But their conditions weren’t necessarily trivial — to participate in the White/Shah experiment, students had to have a clinical diagnosis of ADHD, meaning that their condition was troubling enough to prompt them to seek professional help.
It’s surprising to discover that being forced to switch tasks can make us more creative. It may be still more surprising to realise that in an age where we live under the threat of constant distraction, people who are particularly prone to being distracted are flourishing creatively.
Perhaps we shouldn’t be entirely surprised. It’s easier to think outside the box if the box is full of holes. And it’s also easier to think outside the box if you spend a lot of time clambering between different boxes. “The act of switching back and forth can grease the wheels of thought,” says John Kounios, a professor of psychology at Drexel University.
Kounios, who is co-author of The Eureka Factor, suggests that there are at least two other potentially creative mechanisms at play when we switch between tasks. One is that the new task can help us forget bad ideas. When solving a creative problem, it’s easy to become stuck because we think of an incorrect solution but simply can’t stop returning to it. Doing something totally new induces “fixation forgetting”, leaving us free to find the right answer.
Another is “opportunistic assimilation”. This is when the new task prompts us to think of a solution to the old one. The original Eureka moment is an example.
As the story has it, Archimedes was struggling with the task of determining whether a golden wreath truly was made of pure gold without damaging the ornate treasure. The solution was to determine whether the wreath had the same volume as a pure gold ingot with the same mass; this, in turn, could be done by submerging both the wreath and the ingot to see whether they displaced the same volume of water.
This insight, we are told, occurred to Archimedes while he was having a bath and watching the water level rise and fall as he lifted himself in and out. And if solving such a problem while having a bath isn’t multitasking, then what is?
Tim Harford is an FT columnist. His latest book is ‘The Undercover Economist Strikes Back’. Twitter: @TimHarford
Six ways to be a master of multitasking
1. Be mindful
“The ideal situation is to be able to multitask when multitasking is appropriate, and focus when focusing is important,” says psychologist Shelley Carson. Tom Chatfield, author of Live This Book, suggests making two lists, one for activities best done with internet access and one for activities best done offline. Connecting and disconnecting from the internet should be deliberate acts.
2. Write it down
The essence of David Allen’s Getting Things Done is to turn every vague guilty thought into a specific action, to write down all of the actions and to review them regularly. The point, says Allen, is to feel relaxed about what you’re doing — and about what you’ve decided not to do right now — confident that nothing will fall through the cracks.
3. Tame your smartphone
The smartphone is a great servant and a harsh master. Disable needless notifications — most people don’t need to know about incoming tweets and emails. Set up a filing system within your email so that when a message arrives that requires a proper keyboard to answer — ie 50 words or more — you can move that email out of your inbox and place it in a folder where it will be waiting for you when you fire up your computer.
4. Focus in short sprints
The “Pomodoro Technique” — named after a kitchen timer — alternates focusing for 25 minutes and breaking for five minutes, across two-hour sessions. Productivity guru Merlin Mann suggests an “email dash”, where you scan email and deal with urgent matters for a few minutes each hour. Such ideas let you focus intensely while also switching between projects several times a day.
5. Procrastinate to win
If you have several interesting projects on the go, you can procrastinate over one by working on another. (It worked for Charles Darwin.) A change is as good as a rest, they say — and as psychologist John Kounios explains, such task switching can also unlock new ideas.
“Creative ideas come to people who are interdisciplinary, working across different organisational units or across many projects,” says author and research psychologist Keith Sawyer. (Appropriately, Sawyer is also a jazz pianist, a former management consultant and a sometime game designer for Atari.) Good ideas often come when your mind makes unexpected connections between different fields.
Tim Harford’s To-Do Lists
David Allen’s Getting Things Done system — or GTD — has reached the status of a religion among some productivity geeks. At its heart, it’s just a fancy to-do list, but it’s more powerful than a regular list because it’s comprehensive, specific and designed to prompt you when you need prompting. Here’s how I make the idea work for me.
Write everything down. I use Google Calendar for appointments and an electronic to-do list called Remember the Milk, plus an ad hoc daily list on paper. The details don’t matter. The principle is never to carry a mental commitment around in your head.
Make the list comprehensive. Mine currently has 151 items on it. (No, I don’t memorise the number. I just counted.)
Keep the list fresh. The system works its anxiety-reducing magic best if you trust your calendar and to-do list to remind you when you need reminding. I spend about 20 minutes once a week reviewing the list to note incoming deadlines and make sure the list is neither missing important commitments nor cluttered with stale projects. Review is vital — the more you trust your list, the more you use it. The more you use it, the more you trust it.
List by context as well as topic. It’s natural to list tasks by topic or project — everything associated with renovating the spare room, for instance, or next year’s annual away-day. I also list them by context (this is easy on an electronic list). Things I can do when on a plane; things I can only do when at the shops; things I need to talk about when I next see my boss.
Be specific about the next action. If you’re just writing down vague reminders, the to-do list will continue to provoke anxiety. Before you write down an ill-formed task, take the 15 seconds required to think about exactly what that task is.
Written for and first published at ft.com.
Billions of dollars are spent on experts who claim they can forecast what’s around the corner, in business, finance and economics. Most of them get it wrong. Now a groundbreaking study has unlocked the secret: it IS possible to predict the future – and a new breed of ‘superforecasters’ knows how to do it
Irving Fisher was once the most famous economist in the world. Some would say he was the greatest economist who ever lived. “Anywhere from a decade to two generations ahead of his time,” opined the first Nobel laureate economist Ragnar Frisch, in the late 1940s, more than half a century after Fisher’s genius first lit up his subject. But while Fisher’s approach to economics is firmly embedded in the modern discipline, many of those who remember him now know just one thing about him: that two weeks before the great Wall Street crash of 1929, Fisher announced, “Stocks have reached what looks like a permanently high plateau.”
In the 1920s, Fisher had two great rivals. One was a British academic: John Maynard Keynes, a rising star and Fisher’s equal as an economic theorist and policy adviser. The other was a commercial competitor, an American like Fisher. Roger Babson was a serial entrepreneur with no serious academic credentials, inspired to sell economic forecasts by the banking crisis of 1907. As Babson and Fisher locked horns over the following quarter-century, they laid the foundations of the modern economic forecasting industry.
Fisher’s rivals fared better than he did. Babson foretold the crash and made a fortune, enough to endow the well-respected Babson College. Keynes was caught out by the crisis but recovered and became rich anyway. Fisher died in poverty, ruined by the failure of his forecasts.
If Fisher and Babson could see the modern forecasting industry, it would have astonished them in its scale, range and hyperactivity. In his acerbic book The Fortune Sellers, former consultant William Sherden reckoned in 1998 that forecasting was a $200bn industry – $300bn in today’s terms – and the bulk of the money was being made in business, economic and financial forecasting.
It is true that forecasting now seems ubiquitous. Data analysts forecast demand for new products, or the impact of a discount or special offer; scenario planners (I used to be one) produce broad-based narratives with the aim of provoking fresh thinking; nowcasters look at Twitter or Google to track epidemics, actual or metaphorical, in real time; intelligence agencies look for clues about where the next geopolitical crisis will emerge; and banks, finance ministries, consultants and international agencies release regular prophecies covering dozens, even hundreds, of macroeconomic variables.
Real breakthroughs have been achieved in certain areas, especially where rich datasets have become available – for example, weather forecasting, online retailing and supply-chain management. Yet when it comes to the headline-grabbing business of geopolitical or macroeconomic forecasting, it is not clear that we are any better at the fundamental task that the industry claims to fulfil – seeing into the future.
So why is forecasting so difficult – and is there hope for improvement? And why did Babson and Keynes prosper while Fisher suffered? What did they understand that Fisher, for all his prodigious talents, did not?
In 1987, a young Canadian-born psychologist, Philip Tetlock, planted a time bomb under the forecasting industry that would not explode for 18 years. Tetlock had been trying to figure out what, if anything, the social sciences could contribute to the fundamental problem of the day, which was preventing a nuclear apocalypse. He soon found himself frustrated: frustrated by the fact that the leading political scientists, Sovietologists, historians and policy wonks took such contradictory positions about the state of the cold war; frustrated by their refusal to change their minds in the face of contradictory evidence; and frustrated by the many ways in which even failed forecasts could be justified. “I was nearly right but fortunately it was Gorbachev rather than some neo-Stalinist who took over the reins.” “I made the right mistake: far more dangerous to underestimate the Soviet threat than overestimate it.” Or, of course, the get-out for all failed stock market forecasts, “Only my timing was wrong.”
Tetlock’s response was patient, painstaking and quietly brilliant. He began to collect forecasts from almost 300 experts, eventually accumulating 27,500. The main focus was on politics and geopolitics, with a selection of questions from other areas such as economics thrown in. Tetlock sought clearly defined questions, enabling him with the benefit of hindsight to pronounce each forecast right or wrong. Then Tetlock simply waited while the results rolled in – for 18 years.
Tetlock published his conclusions in 2005, in a subtle and scholarly book, Expert Political Judgment. He found that his experts were terrible forecasters. This was true in both the simple sense that the forecasts failed to materialise and in the deeper sense that the experts had little idea of how confident they should be in making forecasts in different contexts. It is easier to make forecasts about the territorial integrity of Canada than about the territorial integrity of Syria but, beyond the most obvious cases, the experts Tetlock consulted failed to distinguish the Canadas from the Syrias.
Adding to the appeal of this tale of expert hubris, Tetlock found that the most famous experts fared somewhat worse than those outside the media spotlight. Other than that, the humiliation was evenly distributed. Regardless of political ideology, profession and academic training, experts failed to see into the future.
Most people, hearing about Tetlock’s research, simply conclude that either the world is too complex to forecast, or that experts are too stupid to forecast it, or both. Tetlock himself refused to embrace cynicism so easily. He wanted to leave open the possibility that even for these intractable human questions of macroeconomics and geopolitics, a forecasting approach might exist that would bear fruit.
. . .
In 2013, on the auspicious date of April 1, I received an email from Tetlock inviting me to join what he described as “a major new research programme funded in part by Intelligence Advanced Research Projects Activity, an agency within the US intelligence community.”
The core of the programme, which had been running since 2011, was a collection of quantifiable forecasts much like Tetlock’s long-running study. The forecasts would be of economic and geopolitical events, “real and pressing matters of the sort that concern the intelligence community – whether Greece will default, whether there will be a military strike on Iran, etc”. These forecasts took the form of a tournament with thousands of contestants; it is now at the start of its fourth and final annual season.
“You would simply log on to a website,” Tetlock’s email continued, “give your best judgment about matters you may be following anyway, and update that judgment if and when you feel it should be. When time passes and forecasts are judged, you could compare your results with those of others.”
I elected not to participate but 20,000 others have embraced the idea. Some could reasonably be described as having some professional standing, with experience in intelligence analysis, think-tanks or academia. Others are pure amateurs. Tetlock and two other psychologists, Don Moore and Barbara Mellers, have been running experiments with the co-operation of this army of volunteers. (Mellers and Tetlock are married.) Some were given training in how to turn knowledge about the world into a probabilistic forecast; some were assembled into teams; some were given information about other forecasts while others operated in isolation. The entire exercise was given the name of the Good Judgment Project, and the aim was to find better ways to see into the future.
The early years of the forecasting tournament have, wrote Tetlock, “already yielded exciting results”.
A first insight is that even brief training works: a 20-minute course about how to put a probability on a forecast, correcting for well-known biases, provides lasting improvements to performance. This might seem extraordinary – and the benefits were surprisingly large – but even experienced geopolitical seers tend to have expertise in a subject, such as Europe’s economies or Chinese foreign policy, rather than training in the task of forecasting itself.
“For people with the right talents or the right tactics, it is possible to see into the future after all”
A second insight is that teamwork helps. When the project assembled the most successful forecasters into teams who were able to discuss and argue, they produced better predictions.
But ultimately one might expect the same basic finding as always: that forecasting events is basically impossible. Wrong. To connoisseurs of the frailties of futurology, the results of the Good Judgment Project are quite astonishing. Forecasting is possible, and some people – call them “superforecasters”– can predict geopolitical events with an accuracy far outstripping chance. The superforecasters have been able to sustain and even improve their performance.
The cynics were too hasty: for people with the right talents or the right tactics, it is possible to see into the future after all.
Roger Babson, Irving Fisher’s competitor, would always have claimed as much. A serial entrepreneur, Babson made his fortune selling economic forecasts alongside information about business conditions. In 1920, the Babson Statistical Organization had 12,000 subscribers and revenue of $1.35m – almost $16m in today’s money.
“After Babson, the forecaster was an instantly recognisable figure in American business,” writes Walter Friedman, the author of Fortune Tellers, a history of Babson, Fisher and other early economic forecasters. Babson certainly understood how to sell himself and his services. He advertised heavily and wrote prolifically. He gave a complimentary subscription to Thomas Edison, hoping for a celebrity endorsement. After contracting tuberculosis, Babson turned his management of the disease into an inspirational business story. He even employed stonecutters to carve inspirational slogans into large rocks in Massachusetts (the “Babson Boulders” are still there).
On September 5 1929, Babson made a speech at a business conference in Wellesley, Massachusetts. He predicted trouble: “Sooner or later a crash is coming which will take in the leading stocks and cause a decline of from 60 to 80 points in the Dow-Jones barometer.” This would have been a fall of around 20 per cent.
So famous had Babson become that his warning was briefly a self-fulfilling prophecy. When the news tickers of New York reported Babson’s comments at around 2pm, the markets erupted into what The New York Times described as “a storm of selling”. Shares lurched down by 3 per cent. This became known as the “Babson break”.
The next day, shares bounced back and Babson, for a few weeks, appeared ridiculous. On October 29, the great crash began, and within a fortnight the market had fallen almost 50 per cent. By then, Babson had an advertisement in the New York Times pointing out, reasonably, that “Babson clients were prepared”. Subway cars were decorated with the slogan, “Be Right with Babson”. For Babson, his forecasting triumph was a great opportunity to sell more subscriptions.
But his true skill was marketing, not forecasting. His key product, the “Babson chart”, looked scientific and was inspired by the discoveries of Isaac Newton, his idol. The Babson chart operated on the Newtonian assumption that any economic expansion would be matched by an equal and opposite contraction. But for all its apparent sophistication, the Babson chart offered a simple and usually contrarian message.
“Babson offered an up-arrow or a down-arrow. People loved that,” says Walter Friedman. Whether or not Babson’s forecasts were accurate was not a matter that seemed to concern many people. When he was right, he advertised the fact heavily. When he was wrong, few noticed. And Babson had indeed been wrong for many years during the long boom of the 1920s. People taking his advice would have missed out on lucrative opportunities to invest. That simply didn’t matter: his services were popular, and his most spectacularly successful prophecy was also his most famous.
Babson’s triumph suggests an important lesson: commercial success as a forecaster has little to do with whether you are any good at seeing into the future. No doubt it helped his case when his forecasts were correct but nobody gathered systematic information about how accurate he was. The Babson Statistical Organization compiled business and economic indicators that were, in all probability, of substantial value in their own right. Babson’s prognostications were the peacock’s plumage; their effect was simply to attract attention to the services his company provided.
. . .
When Barbara Mellers, Don Moore and Philip Tetlock established the Good Judgment Project, the basic principle was to collect specific predictions about the future and then check to see if they came true. That is not the world Roger Babson inhabited and neither does it describe the task of modern pundits.
When we talk about the future, we often aren’t talking about the future at all but about the problems of today. A newspaper columnist who offers a view on the future of North Korea, or the European Union, is trying to catch the eye, support an argument, or convey in a couple of sentences a worldview that would otherwise be impossibly unwieldy to explain. A talking head in a TV studio offers predictions by way of making conversation. A government analyst or corporate planner may be trying to justify earlier decisions, engaging in bureaucratic defensiveness. And many election forecasts are simple acts of cheerleading for one side or the other.
“Some people – call them ‘superforecasters’– can predict geopolitical events with an accuracy far outstripping chance”
Unlike the predictions collected by the Good Judgment Project, many forecasts are vague enough in their details to allow the mistaken seer off the hook. Even if it was possible to pronounce that a forecast had come true or not, only in a few hotly disputed cases would anybody bother to check.
All this suggests that among the various strategies employed by the superforecasters of the Good Judgment Project, the most basic explanation of their success is that they have the single uncompromised objective of seeing into the future – and this is rare. They receive continual feedback about the success and failure of every forecast, and there are no points for radicalism, originality, boldness, conventional pieties, contrarianism or wit. The project manager of the Good Judgment Project, Terry Murray, says simply, “The only thing that matters is the right answer.”
I asked Murray for her tips on how to be a good forecaster. Her reply was, “Keep score.”
. . .
An intriguing footnote to Philip Tetlock’s original humbling of the experts was that the forecasters who did best were what Tetlock calls “foxes” rather than “hedgehogs”. He used the term to refer to a particular style of thinking: broad rather than deep, intuitive rather than logical, self-critical rather than assured, and ad hoc rather than systematic. The “foxy” thinking style is now much in vogue. Nate Silver, the data journalist most famous for his successful forecasts of US elections, adopted the fox as the mascot of his website as a symbol of “a pluralistic approach”.
The trouble is that Tetlock’s original foxes weren’t actually very good at forecasting. They were merely less awful than the hedgehogs, who deployed a methodical, logical train of thought that proved useless for predicting world affairs. That world, apparently, is too complex for any single logical framework to encompass.
More recent research by the Good Judgment Project investigators leaves foxes and hedgehogs behind but develops this idea that personality matters. Barbara Mellers told me that the thinking style most associated with making better forecasts was something psychologists call “actively open-minded thinking”. A questionnaire to diagnose this trait invites people to rate their agreement or disagreement with statements such as, “Changing your mind is a sign of weakness.” The project found that successful forecasters aren’t afraid to change their minds, are happy to seek out conflicting views and are comfortable with the notion that fresh evidence might force them to abandon an old view of the world and embrace something new.
Which brings us to the strange, sad story of Irving Fisher and John Maynard Keynes. The two men had much in common: both giants in the field of economics; both best-selling authors; both, alas, enthusiastic and prominent eugenicists. Both had immense charisma as public speakers.
Fisher and Keynes also shared a fascination with financial markets, and a conviction that their expertise in macroeconomics and in economic statistics should lead to success as an investor. Both of them, ultimately, were wrong about this. The stock market crashes of 1929 – in September in the UK and late October in the US – caught each of them by surprise, and both lost heavily.
Yet Keynes is remembered today as a successful investor. This is not unreasonable. A study by David Chambers and Elroy Dimson, two financial economists, concluded that Keynes’s track record over a quarter century running the discretionary portfolio of King’s College Cambridge was excellent, outperforming market benchmarks by an average of six percentage points a year, an impressive margin.
This wasn’t because Keynes was a great economic forecaster. His original approach had been predicated on timing the business cycle, moving into and out of different investment classes depending on which way the economy itself was moving. This investment strategy was not a success, and after several years Keynes’s portfolio was almost 20 per cent behind the market as a whole.
The secret to Keynes’s eventual profits is that he changed his approach. He abandoned macroeconomic forecasting entirely. Instead, he sought out well-managed companies with strong dividend yields, and held on to them for the long term. This approach is now associated with Warren Buffett, who quotes Keynes’s investment maxims with approval. But the key insight is that the strategy does not require macroeconomic predictions. Keynes, the most influential macroeconomist in history, realised not only that such forecasts were beyond his skill but that they were unnecessary.
Irving Fisher’s mistake was not that his forecasts were any worse than Keynes’s but that he depended on them to be right, and they weren’t. Fisher’s investments were leveraged by the use of borrowed money. This magnified his gains during the boom, his confidence, and then his losses in the crash.
But there is more to Fisher’s undoing than leverage. His pre-crash gains were large enough that he could easily have cut his losses and lived comfortably. Instead, he was convinced the market would turn again. He made several comments about how the crash was “largely psychological”, or “panic”, and how recovery was imminent. It was not.
One of Fisher’s major investments was in Remington Rand – he was on the stationery company’s board after selling them his “Index Visible” invention, a type of Rolodex. The share price tells the story: $58 before the crash, $28 by 1930. Fisher topped up his investments – and the price soon dropped to $1.
Fisher became deeper and deeper in debt to the taxman and to his brokers. Towards the end of his life, he was a marginalised figure living alone in modest circumstances, an easy target for scam artists. Sylvia Nasar writes in Grand Pursuit, a history of economic thought, “His optimism, overconfidence and stubbornness betrayed him.”
. . .
So what is the secret of looking into the future? Initial results from the Good Judgment Project suggest the following approaches. First, some basic training in probabilistic reasoning helps to produce better forecasts. Second, teams of good forecasters produce better results than good forecasters working alone. Third, actively open-minded people prosper as forecasters.
But the Good Judgment Project also hints at why so many experts are such terrible forecasters. It’s not so much that they lack training, teamwork and open-mindedness – although some of these qualities are in shorter supply than others. It’s that most forecasters aren’t actually seriously and single-mindedly trying to see into the future. If they were, they’d keep score and try to improve their predictions based on past errors. They don’t.
“Successful forecasters aren’t afraid to change their minds and are comfortable with the notion that fresh evidence might mean abandoning an old view”
This is because our predictions are about the future only in the most superficial way. They are really advertisements, conversation pieces, declarations of tribal loyalty – or, as with Irving Fisher, statements of profound conviction about the logical structure of the world. As Roger Babson explained, not without sympathy, Fisher had failed because “he thinks the world is ruled by figures instead of feelings, or by theories instead of styles”.
Poor Fisher was trapped by his own logic, his unrelenting optimism and his repeated public declarations that stocks would recover. And he was bankrupted by an investment strategy in which he could not afford to be wrong.
Babson was perhaps wrong as often as he was right – nobody was keeping track closely enough to be sure either way – but that did not stop him making a fortune. And Keynes prospered when he moved to an investment strategy in which forecasts simply did not matter much.
Fisher once declared that “the sagacious businessman is constantly forecasting”. But Keynes famously wrote of long-term forecasts, “About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.”
Perhaps even more famous is a remark often attributed to Keynes. “When my information changes, I alter my conclusions. What do you do, sir?”
If only he had taught that lesson to Irving Fisher.
Also published at ft.com.
Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What’s more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google’s tracking had only a day’s delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors’ surgeries. Google was faster because it was tracking the outbreak by finding a correlation between what people searched for online and whether they had flu symptoms.
Not only was “Google Flu Trends” quick, accurate and cheap, it was theory-free. Google’s engineers didn’t bother to develop a hypothesis about what search terms – “flu symptoms” or “pharmacies near me” – might be correlated with the spread of the disease itself. The Google team just took their top 50 million search terms and let the algorithms do the work.
The success of Google Flu Trends became emblematic of the hot new trend in business, technology and science: “Big Data”. What, excited journalists asked, can science learn from Google?
As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell. Some emphasise the sheer scale of the data sets that now exist – the Large Hadron Collider’s computers, for example, store 15 petabytes a year of data, equivalent to about 15,000 years’ worth of your favourite music.
But the “big data” that interests many companies is what we might call “found data”, the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast. Google Flu Trends was built on found data and it’s this sort of data that interests me here. Such data sets can be even bigger than the LHC data – Facebook’s is – but just as noteworthy is the fact that they are cheap to collect relative to their size, they are a messy collage of datapoints collected for disparate purposes and they can be updated in real time. As our communication, leisure and commerce have moved to the internet and the internet has moved into our phones, our cars and even our glasses, life can be recorded and quantified in a way that would have been hard to imagine just a decade ago.
Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.
Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”
Found data underpin the new internet economy as companies such as Google, Facebook and Amazon seek new ways to understand our lives through our data exhaust. Since Edward Snowden’s leaks about the scale and scope of US electronic surveillance it has become apparent that security services are just as fascinated with what they might learn from our data exhaust, too.
Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes.
But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons.
“There are a lot of small data problems that occur in big data,” says Spiegelhalter. “They don’t disappear because you’ve got lots of the stuff. They get worse.”
. . .
Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.
The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.
But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. One explanation of the Flu Trends failure is that the news was full of scary stories about flu in December 2012 and that these stories provoked internet searches by people who were healthy. Another possible explanation is that Google’s own search algorithm moved the goalposts when it began automatically suggesting diagnoses when people entered medical symptoms.
Google Flu Trends will bounce back, recalibrated with fresh data – and rightly so. There are many reasons to be excited about the broader opportunities offered to us by the ease with which we can gather and analyse vast data sets. But unless we learn the lessons of this episode, we will find ourselves repeating it.
Statisticians have spent the past 200 years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster and cheaper these days – but we must not pretend that the traps have all been made safe. They have not.
. . .
In 1936, the Republican Alfred Landon stood for election against President Franklin Delano Roosevelt. The respected magazine, The Literary Digest, shouldered the responsibility of forecasting the result. It conducted a postal opinion poll of astonishing ambition, with the aim of reaching 10 million people, a quarter of the electorate. The deluge of mailed-in replies can hardly be imagined but the Digest seemed to be relishing the scale of the task. In late August it reported, “Next week, the first answers from these ten million will begin the incoming tide of marked ballots, to be triple-checked, verified, five-times cross-classified and totalled.”
After tabulating an astonishing 2.4 million returns as they flowed in over two months, The Literary Digest announced its conclusions: Landon would win by a convincing 55 per cent to 41 per cent, with a few voters favouring a third candidate.
The election delivered a very different result: Roosevelt crushed Landon by 61 per cent to 37 per cent. To add to The Literary Digest’s agony, a far smaller survey conducted by the opinion poll pioneer George Gallup came much closer to the final vote, forecasting a comfortable victory for Roosevelt. Mr Gallup understood something that The Literary Digest did not. When it comes to data, size isn’t everything.
Opinion polls are based on samples of the voting population at large. This means that opinion pollsters need to deal with two issues: sample error and sample bias.
Sample error reflects the risk that, purely by chance, a randomly chosen sample of opinions does not reflect the true views of the population. The “margin of error” reported in opinion polls reflects this risk and the larger the sample, the smaller the margin of error. A thousand interviews is a large enough sample for many purposes and Mr Gallup is reported to have conducted 3,000 interviews.
But if 3,000 interviews were good, why weren’t 2.4 million far better? The answer is that sampling error has a far more dangerous friend: sampling bias. Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all. George Gallup took pains to find an unbiased sample because he knew that was far more important than finding a big one.
The Literary Digest, in its quest for a bigger data set, fumbled the question of a biased sample. It mailed out forms to people on a list it had compiled from automobile registrations and telephone directories – a sample that, at least in 1936, was disproportionately prosperous. To compound the problem, Landon supporters turned out to be more likely to mail back their answers. The combination of those two biases was enough to doom The Literary Digest’s poll. For each person George Gallup’s pollsters interviewed, The Literary Digest received 800 responses. All that gave them for their pains was a very precise estimate of the wrong answer.
The big data craze threatens to be The Literary Digest all over again. Because found data sets are so messy, it can be hard to figure out what biases lurk inside them – and because they are so large, some analysts seem to have decided the sampling problem isn’t worth worrying about. It is.
Professor Viktor Mayer-Schönberger of Oxford’s Internet Institute, co-author of Big Data, told me that his favoured definition of a big data set is one where “N = All” – where we no longer have to sample, but we have the entire background population. Returning officers do not estimate an election result with a representative tally: they count the votes – all the votes. And when “N = All” there is indeed no issue of sampling bias because the sample includes everyone.
But is “N = All” really a good description of most of the found data sets we are considering? Probably not. “I would challenge the notion that one could ever have all the data,” says Patrick Wolfe, a computer scientist and professor of statistics at University College London.
An example is Twitter. It is in principle possible to record and analyse every message on Twitter and use it to draw conclusions about the public mood. (In practice, most researchers use a subset of that vast “fire hose” of data.) But while we can look at all the tweets, Twitter users are not representative of the population as a whole. (According to the Pew Research Internet Project, in 2013, US-based Twitter users were disproportionately young, urban or suburban, and black.)
There must always be a question about who and what is missing, especially with a messy pile of found data. Kaiser Fung, a data analyst and author of Numbersense, warns against simply assuming we have everything that matters. “N = All is often an assumption rather than a fact about the data,” he says.
Consider Boston’s Street Bump smartphone app, which uses a phone’s accelerometer to detect potholes without the need for city workers to patrol the streets. As citizens of Boston download the app and drive around, their phones automatically notify City Hall of the need to repair the road surface. Solving the technical challenges involved has produced, rather beautifully, an informative data exhaust that addresses a problem in a way that would have been inconceivable a few years ago. The City of Boston proudly proclaims that the “data provides the City with real-time information it uses to fix problems and plan long term investments.”
Yet what Street Bump really produces, left to its own devices, is a map of potholes that systematically favours young, affluent areas where more people own smartphones. Street Bump offers us “N = All” in the sense that every bump from every enabled phone can be recorded. That is not the same thing as recording every pothole. As Microsoft researcher Kate Crawford points out, found data contain systematic biases and it takes careful thought to spot and correct for those biases. Big data sets can seem comprehensive but the “N = All” is often a seductive illusion.
. . .
Who cares about causation or sampling bias, though, when there is money to be made? Corporations around the world must be salivating as they contemplate the uncanny success of the US discount department store Target, as famously reported by Charles Duhigg in The New York Times in 2012. Duhigg explained that Target has collected so much data on its customers, and is so skilled at analysing that data, that its insight into consumers can seem like magic.
Duhigg’s killer anecdote was of the man who stormed into a Target near Minneapolis and complained to the manager that the company was sending coupons for baby clothes and maternity wear to his teenage daughter. The manager apologised profusely and later called to apologise again – only to be told that the teenager was indeed pregnant. Her father hadn’t realised. Target, after analysing her purchases of unscented wipes and magnesium supplements, had.
Statistical sorcery? There is a more mundane explanation.
“There’s a huge false positive issue,” says Kaiser Fung, who has spent years developing similar approaches for retailers and advertisers. What Fung means is that we didn’t get to hear the countless stories about all the women who received coupons for babywear but who weren’t pregnant.
Hearing the anecdote, it’s easy to assume that Target’s algorithms are infallible – that everybody receiving coupons for onesies and wet wipes is pregnant. This is vanishingly unlikely. Indeed, it could be that pregnant women receive such offers merely because everybody on Target’s mailing list receives such offers. We should not buy the idea that Target employs mind-readers before considering how many misses attend each hit.
In Charles Duhigg’s account, Target mixes in random offers, such as coupons for wine glasses, because pregnant customers would feel spooked if they realised how intimately the company’s computers understood them.
Fung has another explanation: Target mixes up its offers not because it would be weird to send an all-baby coupon-book to a woman who was pregnant but because the company knows that many of those coupon books will be sent to women who aren’t pregnant after all.
None of this suggests that such data analysis is worthless: it may be highly profitable. Even a modest increase in the accuracy of targeted special offers would be a prize worth winning. But profitability should not be conflated with omniscience.
. . .
In 2005, John Ioannidis, an epidemiologist, published a research paper with the self-explanatory title, “Why Most Published Research Findings Are False”. The paper became famous as a provocative diagnosis of a serious issue. One of the key ideas behind Ioannidis’s work is what statisticians call the “multiple-comparisons problem”.
It is routine, when examining a pattern in data, to ask whether such a pattern might have emerged by chance. If it is unlikely that the observed pattern could have emerged at random, we call that pattern “statistically significant”.
The multiple-comparisons problem arises when a researcher looks at many possible patterns. Consider a randomised trial in which vitamins are given to some primary schoolchildren and placebos are given to others. Do the vitamins work? That all depends on what we mean by “work”. The researchers could look at the children’s height, weight, prevalence of tooth decay, classroom behaviour, test scores, even (after waiting) prison record or earnings at the age of 25. Then there are combinations to check: do the vitamins have an effect on the poorer kids, the richer kids, the boys, the girls? Test enough different correlations and fluke results will drown out the real discoveries.
There are various ways to deal with this but the problem is more serious in large data sets, because there are vastly more possible comparisons than there are data points to compare. Without careful analysis, the ratio of genuine patterns to spurious patterns – of signal to noise – quickly tends to zero.
Worse still, one of the antidotes to the multiple-comparisons problem is transparency, allowing other researchers to figure out how many hypotheses were tested and how many contrary results are languishing in desk drawers because they just didn’t seem interesting enough to publish. Yet found data sets are rarely transparent. Amazon and Google, Facebook and Twitter, Target and Tesco – these companies aren’t about to share their data with you or anyone else.
New, large, cheap data sets and powerful analytical tools will pay dividends – nobody doubts that. And there are a few cases in which analysis of very large data sets has worked miracles. David Spiegelhalter of Cambridge points to Google Translate, which operates by statistically analysing hundreds of millions of documents that have been translated by humans and looking for patterns it can copy. This is an example of what computer scientists call “machine learning”, and it can deliver astonishing results with no preprogrammed grammatical rules. Google Translate is as close to theory-free, data-driven algorithmic black box as we have – and it is, says Spiegelhalter, “an amazing achievement”. That achievement is built on the clever processing of enormous data sets.
But big data do not solve the problem that has obsessed statisticians and scientists for centuries: the problem of insight, of inferring what is going on, and figuring out how we might intervene to change a system for the better.
“We have a new resource here,” says Professor David Hand of Imperial College London. “But nobody wants ‘data’. What they want are the answers.”
To use big data to produce such answers will require large strides in statistical methods.
“It’s the wild west right now,” says Patrick Wolfe of UCL. “People who are clever and driven will twist and turn and use every tool to get sense out of these data sets, and that’s cool. But we’re flying a little bit blind at the moment.”
Statisticians are scrambling to develop new methods to seize the opportunity of big data. Such new methods are essential but they will work by building on the old statistical lessons, not by ignoring them.
Recall big data’s four articles of faith. Uncanny accuracy is easy to overrate if we simply ignore false positives, as with Target’s pregnancy predictor. The claim that causation has been “knocked off its pedestal” is fine if we are making predictions in a stable environment but not if the world is changing (as with Flu Trends) or if we ourselves hope to change it. The promise that “N = All”, and therefore that sampling bias does not matter, is simply not true in most cases that count. As for the idea that “with enough data, the numbers speak for themselves” – that seems hopelessly naive in data sets where spurious patterns vastly outnumber genuine discoveries.
“Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever.
This article was first published in the FT Magazine, 29/30 March 2014. Read it in its original setting here.
The past decade has been a triumph for behavioural economics, the fashionable cross-breed of psychology and economics. First there was the award in 2002 of the Nobel Memorial Prize in economics to a psychologist, Daniel Kahneman – the man who did as much as anything to create the field of behavioural economics. Bestselling books were launched, most notably by Kahneman himself (Thinking, Fast and Slow , 2011) and by his friend Richard Thaler, co-author of Nudge (2008). Behavioural economics seems far sexier than the ordinary sort, too: when last year’s Nobel was shared three ways, it was the behavioural economist Robert Shiller who grabbed all the headlines.
Behavioural economics is one of the hottest ideas in public policy. The UK government’s Behavioural Insights Team (BIT) uses the discipline to craft better policies, and in February was part-privatised with a mission to advise governments around the world. The White House announced its own behavioural insights team last summer.
So popular is the field that behavioural economics is now often misapplied as a catch-all term to refer to almost anything that’s cool in popular social science, from the storycraft of Malcolm Gladwell, author of The Tipping Point (2000), to the empirical investigations of Steven Levitt, co-author of Freakonomics (2005).
Yet, as with any success story, the backlash has begun. Critics argue that the field is overhyped, trivial, unreliable, a smokescreen for bad policy, an intellectual dead-end – or possibly all of the above. Is behavioural economics doomed to reflect the limitations of its intellectual parents, psychology and economics? Or can it build on their strengths and offer a powerful set of tools for policy makers and academics alike?
A recent experiment designed by BIT highlights both the opportunity and the limitations of the new discipline. The trial was designed to encourage people to sign up for the Organ Donor Register. It was huge; more than a million people using the Driver and Vehicle Licensing Agency website were shown a webpage inviting them to become an organ donor. One of eight different messages was displayed at random. One was minimalist, another spoke of the number of people who die while awaiting donations, yet another appealed to the idea of reciprocity – if you needed an organ, wouldn’t you want someone to donate an organ to you?
BIT devoted particular attention to an idea called “social proof”, made famous 30 years ago by psychologist Robert Cialdini’s book Influence. While one might be tempted to say, “Too few people are donating their organs, we desperately need your help to change that”, the theory of social proof says that’s precisely the wrong thing to do. Instead, the persuasive message will suggest: “Every day, thousands of people sign up to be donors, please join them.” Social proof describes our tendency to run with the herd; why else are books marketed as “bestsellers”?
Expecting social proof to be effective, the BIT trial used three different variants of a social proof message, one with a logo, one with a photo of smiling people, and one unadorned. None of these approaches was as successful as the best alternatives at persuading people to sign up as donors. The message with the photograph – for which the teams had high hopes – was a flop, proving worse than no attempt at persuasion at all.
Daniel Kahneman, one of the fathers of behavioural economics, receiving an award from Barack Obama, November 2013
Three points should be made here. The first is that this is exactly why running trials is an excellent idea: had the rival approaches not been tested with an experiment, it would have been easy for well-meaning civil servants acting on authoritative advice to have done serious harm. The trial was inexpensive, and now that the most persuasive message is in use (“If you needed an organ transplant, would you have one? If so, please help others”), roughly 100,000 additional people can be expected to sign up for the donor register each year.
The second point is that there is something unnerving about a discipline in which our discoveries about the past do not easily generalise to the future. Social proof is a widely accepted idea in psychology but, as the donor experiment shows, it does not always apply and it can be hard to predict when or why.
This patchwork of sometimes-fragile psychological results hardly invalidates the whole field but complicates the business of making practical policy. There is a sense that behavioural economics is just regular economics plus common sense – but since psychology isn’t mere common sense either, applying psychological lessons to economics is not a simple task.
The third point is that the organ donor experiment has little or nothing to do with behavioural economics, strictly defined. “The Behavioural Insights Team is widely perceived as doing behavioural economics,” says Daniel Kahneman. “They are actually doing social psychology.”
. . .
The line between behavioural economics and psychology can get a little blurred. Behavioural economics is based on the traditional “neoclassical” model of human behaviour used by economists. This essentially mathematical model says human decisions can usefully be modelled as though our choices were the outcome of solving differential equations. Add psychology into the mix – for example, Kahneman’s insight (with the late Amos Tversky) that we treat the possibility of a loss differently from the way we treat the possibility of a gain – and the task of the behavioural economist is to incorporate such ideas without losing the mathematically-solvable nature of the model.
Why bother with the maths? Consider the example of, say, improving energy efficiency. A psychologist might point out that consumers are impatient, poorly-informed and easily swayed by what their neighbours are doing. It’s the job of the behavioural economist to work out how energy markets might work under such conditions, and what effects we might expect if we introduced policies such as a tax on domestic heating or a subsidy for insulation.
It’s this desire to throw out the hyper-rational bathwater yet keep the mathematically tractable baby that leads to difficult compromises, and not everyone is happy. Economic traditionalists argue that behavioural economics is now hopelessly patched-together; some psychologists claim it’s still attempting to be too systematic.
Nick Chater, a psychologist at Warwick Business School and an adviser to the BIT, is a sympathetic critic of the behavioural economics approach. “The brain is the most rational thing in the universe”, he says, “but the way it solves problems is ad hoc and very local.” That suggests that attempts to formulate general laws of human behaviour may never be more than a rough guide to policy.
This shift to radical incrementalism is so much more important than some of the grand proposals out there
The most well-known critique of behavioural economics comes from a psychologist, Gerd Gigerenzer of the Max Planck Institute for Human Development. Gigerenzer argues that it is pointless to keep adding frills to a mathematical account of human behaviour that, in the end, has nothing to do with real cognitive processes.
I put this critique to David Laibson, a behavioural economist at Harvard University. He concedes that Gigerenzer has a point but adds: “Gerd’s models of heuristic decision-making are great in the specific domains for which they are designed but they are not general models of behaviour.” In other words, you’re not going to be able to use them to figure out how people should, or do, budget for Christmas or nurse their credit card limit through a spell of joblessness.
Richard Thaler of the University of Chicago, who with Kahneman and Tversky is the founding father of behavioural economics, agrees. To discard the basic neoclassical framework of economics means “throwing away a lot of stuff that’s useful”.
For some economists, though, behavioural economics has already conceded too much to the patchwork of psychology. David K Levine, an economist at Washington University in St Louis, and author of Is Behavioral Economics Doomed? (2012), says: “There is a tendency to propose some new theory to explain each new fact. The world doesn’t need a thousand different theories to explain a thousand different facts. At some point there needs to be a discipline of trying to explain many facts with one theory.”
The challenge for behavioural economics is to elaborate on the neoclassical model to deliver psychological realism without collapsing into a mess of special cases. Some say that the most successful special case comes from Harvard’s David Laibson. It is a mathematical tweak designed to represent the particular brand of short-termism that leads us to sign up for the gym yet somehow never quite get around to exercising. It’s called “hyperbolic discounting”, a name that refers to a mathematical curve, and which says much about the way behavioural economists represent human psychology.
The question is, how many special cases can behavioural economics sustain before it becomes arbitrary and unwieldy? Not more than one or two at a time, says Kahneman. “You might be able to do it with two but certainly not with many factors.” Like Kahneman, Thaler believes that a small number of made-for-purpose behavioural economics models have proved their worth already. He argues that trying to unify every psychological idea in a single model is pointless. “I’ve always said that if you want one unifying theory of economic behaviour, you won’t do better than the neoclassical model, which is not particularly good at describing actual decision making.”
. . .
Meanwhile, the policy wonks plug away at the rather different challenge of running rigorous experiments with public policy. There is something faintly unsatisfying about how these policy trials have often confirmed what should have been obvious. One trial, for example, showed that text message reminders increase the proportion of people who pay legal fines. This saves everyone the trouble of calling in the bailiffs. Other trials have shown that clearly-written letters with bullet-point summaries provoke higher response rates.
None of this requires the sophistication of a mathematical model of hyperbolic discounting or loss aversion. It is obvious stuff. Unfortunately it is obvious stuff that is often neglected by the civil service. It is hard to object to inexpensive trials that demonstrate a better way. Nick Chater calls the idea “a complete no-brainer”, while Kahneman says “you can get modest gains at essentially zero cost”.
David Halpern, a Downing Street adviser under Tony Blair, was appointed by the UK coalition government in 2010 to establish the BIT. He says that the idea of running randomised trials in government has now picked up steam. The Financial Conduct Authority has also used randomisation to develop more effective letters to people who may have been missold financial products. “This shift to radical incrementalism is so much more important than some of the grand proposals out there,” says Halpern.
Not everyone agrees. In 2010, behavioural economists George Loewenstein and Peter Ubel wrote in The New York Times that “behavioural economics is being used as a political expedient, allowing policy makers to avoid painful but more effective solutions rooted in traditional economics.”
For example, in May 2010, just before David Cameron came to power, he sang the praises of behavioural economics in a TED talk. “The best way to get someone to cut their electricity bill,” he said, “is to show them their own spending, to show them what their neighbours are spending, and then show what an energy-conscious neighbour is spending.”
But Cameron was mistaken. The single best way to promote energy efficiency is, almost certainly, to raise the price of energy. A carbon tax would be even better, because it not only encourages people to save energy but to switch to lower-carbon sources of energy. The appeal of a behavioural approach is not that it is more effective but that it is less unpopular.
Thaler points to the experience of Cass Sunstein, his Nudge co-author, who spent four years as regulatory tsar in the Obama White House. “Cass wanted a tax on petrol but he couldn’t get one, so he pushed for higher fuel economy standards. We all know that’s not as efficient as raising the tax on petrol – but that would be lucky to get a single positive vote in Congress.”
Should we be trying for something more ambitious than behavioural economics? “I don’t know if we know enough yet to be more ambitious,” says Kahneman, “But the knowledge that currently exists in psychology is being put to very good use.”
Small steps have taken behavioural economics a long way, says Laibson, citing savings policy in the US. “Every dimension of that environment is now behaviourally tweaked.” The UK has followed suit, with the new auto-enrolment pensions, directly inspired by Thaler’s work.
Laibson says behavioural economics has only just begun to extend its influence over public policy. “The glass is only five per cent full but there’s no reason to believe the glass isn’t going to completely fill up.”
First published on FT.com, Life and Arts, 22 March 2014
While delivering his Nobel lecture in 2007, Al Gore declared: “Today, we dumped another 70 million tons of global-warming pollution into the thin shell of atmosphere surrounding our planet, as if it were an open sewer.”
It’s a powerful example of the way we tend to argue about the impact of the human race on the planet that supports us: statistical or scientific claims combined with a call to action. But the argument misses something important: if we are to act, then how? Who must do what, who will benefit and how will all this be agreed and policed?
To ask how people work together to deal with environmental problems is to ask one of the fundamental questions in social science: how do people work together at all? This is the story of two researchers who attacked the question in very different ways – and with very different results.
“The Tragedy of the Commons” is a seminal article about why some environmental problems are so hard to solve. It was published in the journal Science in 1968 and its influence was huge. Partly this was the zeitgeist: the late 1960s and early 1970s was an era of big environmental legislation and regulation in the US. Yet that cannot be the only reason that the “tragedy of the commons” has joined a very small group of concepts – such as the “prisoner’s dilemma” or the “selfish gene” – to have escaped from academia to take on a life of their own.
The credit must go to Garrett Hardin, the man who coined the phrase and wrote the article. Hardin was a respected ecologist but “The Tragedy of the Commons” wasn’t an ecological study. It wasn’t really a piece of original research at all.
“Nothing he wrote in there had not been said by fisheries economists,” says Daniel Cole, a professor at Indiana University and a scholar of Hardin’s research. The key idea, indeed, goes back to Aristotle. Hardin’s genius was in developing a powerful, succinct story with a memorable name.
The story goes as follows: imagine common pasture, land owned by everyone and no one, “open to all” for grazing livestock. Now consider the incentives faced by people bringing animals to feed. Each new cow brought to the pasture represents pure private profit for the individual herdsman in question. But the commons cannot sustain an infinite number of cows. At some stage it will be overgrazed and the ecosystem may fail. That risk is not borne by any individual, however, but by society as a whole.
With a little mathematical elaboration Hardin showed that these incentives led inescapably to ecological disaster and the collapse of the commons. The idea of a communally owned resource might be appealing but it was ultimately self-defeating.
It was in this context that Hardin deployed the word “tragedy”. He didn’t use it to suggest that this was sad. He meant that this was inevitable. Hardin, who argued that much of the natural sciences was grounded by limits – such as the speed of light or the force of gravity – quoted the philosopher Alfred North Whitehead, who wrote that tragedy “resides in the solemnity of the remorseless working of things”.
. . .
Lin Ostrom never believed in “the remorseless working of things”. Born Elinor Awan in Los Angeles in 1933, by the time she first saw Garrett Hardin present his ideas she had already beaten the odds.
Lin was brought up in Depression-era poverty after her Jewish father left her Protestant mother. She was bullied at school – Beverly Hills High, of all places – because she was half-Jewish. She divorced her first husband, Charles Scott, after he discouraged her from pursuing an academic career, where she suffered discrimination for years. Initially steered away from mathematics at school, Lin was rejected by the economics programme at UCLA. She was only – finally – accepted on a PhD in political science after observing that UCLA’s political science department hadn’t admitted a woman for 40 years.
She persevered and secured her PhD after studying the management of fresh water in Los Angeles. In the first half of the 20th century, the city’s water supply had been blighted by competing demands to pump fresh water for drinking and farming. By the 1940s, however, the conflicting parties had begun to resolve their differences. In both her PhD, which she completed in 1965, and subsequent research, Lin showed that such outcomes often came from private individuals or local associations, who came up with their own rules and then lobbied the state to enforce them. In the case of the Los Angeles water producers, they drew up contracts to share their resources and the city’s water supply stabilised.
It was only when Lin saw Hardin lecture that she realised that she had been studying the tragedy of the commons all along. It was 1968, the year that the famous article was published. Garrett Hardin was 53, in the early stages of a career as a campaigning public intellectual that would last the rest of his life. Lin was 35, now Ostrom: she had married Vincent Ostrom, a respected political scientist closer to Hardin’s age, and together they had moved to Indiana University. Watching Hardin lecture galvanised her. But that wasn’t because she was convinced he was right. It was because she was convinced that he was wrong.
In his essay, Hardin explained that there was no way to manage communal property sustainably. The only solution was to obliterate the communal aspect. Either the commons could be nationalised and managed by the state – a Leviathan for the age of environmentalism – or the commons could be privatised, divided up into little parcels and handed out to individual farmers, who would then look after their own land responsibly. The theory behind all this is impeccable and, despite coming from a biologist, highly appealing to anyone with an economics training.
But Lin Ostrom could see that there must be something wrong with the logic. Her research on managing water in Los Angeles, watching hundreds of different actors hammer out their messy yet functional agreements, provided a powerful counter-example to Hardin. She knew of other examples, too, in which common resources had been managed sustainably without Hardin’s black-or-white solutions.
The problem with Hardin’s logic was the very first step: the assumption that communally owned land was a free-for-all. It wasn’t. The commons were owned by a community. They were managed by a community. These people were neighbours. They lived next door to each other. In many cases, they set their own rules and policed those rules.
This is not to deny the existence of the tragedy of the commons altogether. Hardin’s analysis looks prescient when applied to our habit of pumping carbon dioxide into the atmosphere or overfishing the oceans. But the existence of clear counter-examples should make us hesitate before accepting Hardin’s argument that tragedy is unstoppable. Lin Ostrom knew that there was nothing inevitable about the self-destruction of “common pool resources”, as economists call them. The tragedy of the commons wasn’t a tragedy at all. It was a problem – and problems have solutions.
If Garrett Hardin and Lin Ostrom had reached different conclusions about the commons, perhaps that was because their entire approaches to academic research were different. Hardin wanted to change the world; Ostrom merely wanted to describe it.
That goal of description, though, was a vast project. Common pool resources could be found all over the planet, from the high meadows of Switzerland to the lobster fisheries of Maine, from forests in Sri Lanka to water in Nepal. Hardin’s article had sliced through the complexity with his assumption that all commons were in some sense the same. But they aren’t.
To describe even a single case study of governing a common resource is a challenge (Lin’s PhD was devoted to the West Basin water district of Los Angeles). Vincent Ostrom, Lin’s husband, had developed the idea of “polycentricity” in political science: polycentric systems have multiple, independent and overlapping sources of power and authority.
By their very nature, they are messy to describe and hard to compare with each other. Unfortunately for any tidy-minded social scientist, they are also everywhere.
Complicating the problem further was the narrow focus of academic specialities. Lin was encouraged that many people had been drawn, like her, to the study of common pool resources. But they were divided by discipline, by region and by subject: the sociologists didn’t talk to the economists; the India specialists didn’t talk to the Africanists; and the fishery experts didn’t know anything about forestry. As Ostrom and her colleagues at the University of Indiana looked into the problem they discovered more than a thousand separate case studies, each sitting in isolation.
Undeterred, they began to catalogue them, seeking to explain the difference between the successful attempts to manage environmental resources and the failures. There were the Swiss farmers of the village of Törbel, who had a system of rules, fines and local associations that dated from the 13th century to govern the use of scarce Alpine pastures and firewood. There were the fishermen of Alanya, in Turkey, who took part in a lottery each September to allocate fishing rights for the year ahead.
Over time, Ostrom developed a set of what she called “design principles” for managing common resources, drawn from what worked in the real world. She used the phrase hesitantly since, she argued, these arrangements were rarely designed or imposed from the top down; they usually evolved from the bottom up.
These principles included effective monitoring; graduated sanctions for those who break rules; and cheap access to conflict-resolution mechanisms (the fishermen of Alanya resolved their disputes in the local coffee house). There are several others. Ostrom wanted to be as precise as she could, to move away from the hand-waving of some social scientists. But there were limits to how reductive it was possible to be about such varied institutions. Lin’s only golden rule about common pool resources was that there are no panaceas.
Her work required a new set of intellectual tools. But for Ostrom, this effort was central to her academic life because knowledge itself – when you thought about it – was a kind of common pool resource as well. It could be squandered or it could be harvested for the public good. And it would only be harvested with the right set of rules.
Ostrom’s research project came to resemble one of the local, community-led institutions that she sought to explain. In 1973, the Ostroms established something called the “Workshop in Political Theory and Policy Analysis”. Why not a school or a centre or a department? It was partly to sidestep bureaucracy. “The university didn’t know what a workshop was,” says Michael McGinnis, a professor of political science at Indiana University and a colleague of the Ostroms. “They didn’t have rules for a workshop.”
But there was more behind the name than administrative guile. Vincent and Lin believed that the work they did was a kind of craft. (The couple had built their own home and made much of their own furniture, under the guidance of a local craftsman – the experience made an impression.) The students who attended didn’t call themselves students or researchers. They called themselves “workshoppers”.
The workshop under the Ostroms seems to have been a remarkable place, brightened up by Lin’s sparkling laugh and garish tops. (The laugh was a reliable sign that she was in the building, available to be buttonholed by students.) At reunions, Ostrom would lead the singing of folk songs; it was that kind of place. The Ostroms never had children but the workshoppers did – and those children called Lin “Grandma”.
. . .
The logic of Garrett Hardin’s 1968 essay is seductive but to read the text itself is a shock. Hardin’s policy proposals are extreme. He believed that the ultimate tragedy of the commons was overpopulation – and the central policy conclusion of the article was, to quote Hardin, that “freedom to breed is intolerable”.
In a 1974 essay, “Living on a Lifeboat”, he argued that it was pointless sending aid to starving people in Ethiopia. That would only make the real problem worse – the real problem being, of course, overpopulation.
Hardin robustly defended his views. In a 1987 interview with The New York Times, he opined, “There’s nothing more dangerous than a shallow-thinking compassionate person.
God, he can cause a lot of trouble.” But perhaps it was Hardin who was the one failing to think deeply enough. The logic of “The Tragedy of the Commons” worked well to frame a class of environmental problems. The danger was when Hardin leapt to drastic conclusions without looking at how other, similar-looking problems were being solved, again and again, by communities all over the world.
Nor has Hardin’s needle-sharp focus on overpopulation stood the test of time. When he published “The Tragedy of the Commons” in 1968, the growth rate of world population was higher than it had ever been – a rate at which population would double every 30 years. No wonder Hardin was alarmed. But birth rates have fallen dramatically. The world continues to face some severe environmental problems. However, it’s far from clear that “freedom to breed” is one of them.
There was no great public showdown between Lin Ostrom and Garrett Hardin, but Hardin did return to speak at Indiana University in 1976. The Ostroms invited him and some graduate students to dinner. Barbara Allen, now a professor at Carleton College, was one of them. She recalls that “the conversation was vigorous” as Hardin laid out his ideas for government-led initiatives to reduce the birth rate in the US, while Lin and Vincent worried about the unintended consequences of such top-down panaceas.
Allen recalls two other details: the way that Lin made space for her students to enter the argument and her joy in a new kitchen gadget she was using to make hamburgers for everyone. She loved “the odd delights of everyday life”, Allen later wrote, and loved to celebrate what worked.
Hardin, by contrast, seems to have been more of a pessimist about technology. “Technology does solve problems,” he told an interviewer in 1990, “but always at a cost.”
Lin Ostrom was a more optimistic character altogether. When she won the Nobel memorial prize for economics in 2009, she was the first woman to do so. She was quick to comment: “I won’t be the last.”
Some of her most recent research addressed the problem of climate change. Scientifically speaking, greenhouse gas emissions are a global pollutant, and so efforts have focused on establishing global agreements. That, said Ostrom, is a mistake. Common pool problems were usually too complex to solve from the top down; a polycentric approach was necessary, with people developing ideas and enforcing behaviour at a community, city, national and regional level.
Ostrom barely slowed down when she was diagnosed with pancreatic cancer in 2011. She kept going until the final days, leaving voicemail messages for Vincent who, at the age of 90, was deaf and beginning to become confused. (Her students would type them up and print them out in large fonts for him to read.) When Lin died last June, at the age of 78, she was reviewing a student’s PhD thesis. She’d been annotating the text, which lay on the table beside her hospital bed. Vincent died two weeks later. The couple left almost everything to the workshop.
Garrett Hardin and his wife Jane also died together, in September 2003. After 62 years of marriage, and both suffering from very poor health, they killed themselves. Perhaps strangely for a man who thought overpopulation was the world’s ultimate problem, Garrett Hardin had four children. But there may be a certain kind of logic in that. Hardin always felt that overpopulation was inevitable. He died the way he lived – a resolute believer in the remorseless working of things.
First published in the FT Magazine.
When the world’s richest countries were booming, few people worried overmuch that the top 1 per cent were enjoying an ever-growing share of that prosperity. In the wake of a depression in the US, a fiscal chasm in the UK and an existential crisis in the eurozone – and the shaming of the world’s bankers – worrying about inequality is no longer the preserve of the far left.
There should be no doubt about the facts: the income share of the top 1 per cent has roughly doubled in the US since the early 1970s, and is now about 20 per cent. Much the same trend can be seen in Australia, Canada and the UK – although in each case the income share of the top 1 per cent is smaller. In France, Germany and Japan there seems to be no such trend. (The source is the World Top Incomes Database, summarised in the opening paper of a superb symposium in this summer’s Journal of Economic Perspectives.)
But should we care? There are two reasons we might: process and outcome. We might worry that the gains of the rich are ill-gotten: the result of the old-boy network, or fraud, or exploiting the largesse of the taxpayer. Or we might worry that the results are noxious: misery and envy, or ill-health, or dysfunctional democracy, or slow growth as the rich sit on their cash, or excessive debt and thus financial instability.
Following the crisis, it might be unfashionable to suggest that the rich actually earned their money. But knee-jerk banker-bashers should take a look at research by Steven Kaplan and Joshua Rauh, again in the JEP symposium. They simply compare the fate of the top earners across different lines of business. Worried that chief executives are filling their boots thanks to the weak governance of publicly listed companies? So am I, but partners in law firms are also doing very nicely, as are the bosses of privately owned companies, as are the managers of hedge funds, as are top sports stars. Governance arrangements in each case are different.
Perhaps, then, some broad social norm has shifted, allowing higher pay across the board? If so, we would expect publicly scrutinised salaries to be catching up with those who have more privacy – for instance, managers of privately held corporations. The reverse is the case.
The uncomfortable truth is that market forces – that is, the result of freely agreed contracts – are probably behind much of the rise in inequality. Globalisation and technological change favour the highly skilled. In the middle of the income distribution, a strong pair of arms, a willingness to work hard and a bit of common sense used to provide a comfortable income. No longer. Meanwhile at the very top, winner-take-all markets are emerging, where the best or luckiest entrepreneurs, fund managers, authors or athletes hoover up most of the gains. The idea that the fat cats simply stole everyone else’s cream is emotionally powerful; it is not entirely convincing.
In a well-functioning market, people only earn high incomes if they create enough economic value to justify those incomes. But even if we could be convinced that this was true, we do not have to let the matter drop.
This is partly because the sums involved are immense. Between 1993 and 2011, in the US, average incomes grew a modest 13.1 per cent in total. But the average income of the poorest 99 per cent – that is everyone up to families making about $370,000 a year – grew just 5.8 per cent. That gap is a measure of just how much the top 1 per cent are making. The stakes are high.
I set out two reasons why we might care about inequality: an unfair process or a harmful outcome. But what really should concern us is that the two reasons are not actually distinct after all. The harmful outcome and the unfair process feed each other. The more unequal a society becomes, the greater the incentive for the rich to pull up the ladder behind them.
At the very top of the scale, plutocrats can shape the conversation by buying up newspapers and television channels or funding political campaigns. The merely prosperous scramble desperately to get their children into the right neighbourhood, nursery, school, university and internship – we know how big the gap has grown between winners and also-rans.
Miles Corak, another contributor to the JEP debate, is an expert on intergenerational income mobility, the question of whether rich parents have rich children. The painful truth is that in the most unequal developed nations – the UK and the US – the intergenerational transmission of income is stronger. In more equal societies such as Denmark, the tendency of privilege to breed privilege is much lower.
This is what sticks in the throat about the rise in inequality: the knowledge that the more unequal our societies become, the more we all become prisoners of that inequality. The well-off feel that they must strain to prevent their children from slipping down the income ladder. The poor see the best schools, colleges, even art clubs and ballet classes, disappearing behind a wall of fees or unaffordable housing.
The idea of a free, market-based society is that everyone can reach his or her potential. Somewhere, we lost our way.
‘The Undercover Economist Strikes Back’ by Tim Harford is published this month in the UK and in January in the US.
First published in the Financial Times, 16 August 2012.
Portrait by James Ferguson
Cory Doctorow should be too busy for lunch. He’s co-editor of, and a prolific contributor to, one of the most influential blogs in the world, Boing Boing. Over the past decade the Canadian-born writer has published 16 books, mostly science fiction novels. He campaigns vigorously on the politics of the digital age. His speaking schedule for the two months following our lunch requires three return trips from his home in east London to North America. He has almost 300,000 followers on Twitter. He is an impeccably prompt email correspondent.
More remarkable to me than any of this is that he claims to prepare for himself, his wife and five-year-old daughter “a three-to-four-course, hot/cold tailor-made breakfast every morning, in 20 minutes flat, with handmade coffees”. And although I arrive at Hawksmoor 10 minutes early, he’s there already, sipping sparkling water at the bar and reading a book. He’s wearing thick-rimmed spectacles worthy of Eric Morecambe, a Disney “Haunted Mansion” T-shirt, and a jacket; he’s 41 but looks younger. Did I mention that I have a tiny crush on Cory Doctorow?
As we’re shown to our table at the window, I feel compelled to ask about the breakfasts. How does he do it? After reading about this quotidian feat of fatherhood, I had tried to make my wife a fancy breakfast in bed, with eggs and honey-drizzled yoghurt and other trimmings. It took long enough that well before I was finished she had surfaced to investigate what was going on. I ask Doctorow for tips, confessing that I can scarcely produce a gourmet breakfast for my family once a fortnight.
“That’s your problem,” he says, in a bright, brisk Toronto accent. “You don’t do it often enough. If you did it every day, you’d get very good at it. It would become a habit, and habits are free.” His own breakfasts are prepared the night before – porridge measured out, yoghurt and berries mixed, served and in the fridge, eggs in the saucepan ready to be boiled. It’s obsessive, precise, carefully optimised – and, it seems, highly effective.
The waiter arrives to discuss steak with us. Hawksmoor is a hipsterish steakhouse near Spitalfields market, all dark wood and brick. The menu is unconventional, with steaks priced by the gramme and particular weights of pre-cut steak chalked up on the board. Doctorow – who had sent me a list of places he’d be happy to eat – seems to be a regular, and quizzes the waiter about when the meat is delivered and why, when he comes in the evenings, certain cuts have already been crossed off. The waiter assures us that the meat is delivered fresh every day and never frozen, even though it’s harder to carve the steak in an unfrozen state.
“Unless you have a laser,” offers Doctorow, at which point the waiter, rather curiously, begins to discuss whether certain cuts cooked rare present a risk of food poisoning.
“We’re not really supposed to talk about food poisoning,” admits the waiter. “You’ve got to come up with another name for food poisoning,” suggests Doctorow. “Like, er, ‘exotic gut flora experience’.”
I explain to Doctorow that he can choose whatever he likes and the FT will pay, but the world gets to see the bill. “That’s a very funny little bit of behavioural economics,” he replies.
Some of the steaks are sized for two, and I indicate that I’m willing to share one. Doctorow selects a large porterhouse for us, and we’re persuaded for reasons of flavour rather than safety to go for medium rare rather than rare. Doctorow chooses bone marrow and onion to start, with creamed spinach to accompany the steak. I start with a Caesar salad, and order triple-cooked chips. I press him to order some wine and he reluctantly agrees to drink half a glass, but refuses to choose.
I express surprise that he claims to know nothing about wine although he is obsessive about, for example, coffee. “I specialise,” he explains, adding that he rarely drinks much. I choose two glasses of the cheapest red. It’s not bad, and he downs his swiftly.
. . .
Doctorow’s fiction champions technology, while warning of how easily it can be used by repressive states or corporations. His own life provides an example of how to live with freedom in a technological age – he’s a man with no particular title, no hierarchical authority, no corner office and no secretary, who somehow manages to keep the plates spinning. Is it the same relentless, nerdy optimisation that gets those breakfasts on the table? He quotes from Brian Eno’s collection of not-quite-aphorisms Oblique Strategies , “Be the first to not do what nobody has ever thought of not doing before.”
Boing Boing, a marvellously eclectic blog, is a case in point – it’s a stripped-down vision of a 21st-century media outlet. Founded in 1988 as a print magazine, it went digital in the mid-1990s, and became one of the first blogs to attract a mass audience. Doctorow started writing for it as a favour for the blog’s founder Mark Frauenfelder, who was going on holiday, and never stopped. It’s incorporated as a business and is funded by sponsors, advertising and merchandise. It has a wide reach and yet, by the standards of a newspaper, is produced by a tiny team, with four main writers, three of whom live in California.
“We are spectacularly lean,” says Doctorow. “We have one phone call a year if we need it. We have one meeting a year.” He’s saving on air fare by tacking this year’s editorial meeting – in Los Angeles – on to a pre-existing trip. It’s usually at the Magic Castle, a private club for magicians. This is a typical touch of whimsy; Doctorow is also seriously smitten by Disney theme parks (his first novel, Down and Out in the Magic Kingdom (2003), imagines what Walt Disney World might be in the 22nd century).
Is this “spectacularly lean” operation the future of newspaper publishing, I ask? “It’s a future of publishing. One of the things that newspapers obscured was that they weren’t a medium, they were a collection of media bodged together.” Newspapers are like books, he says, a format that encompasses “anything from actuarial tables to Mein Kampf”.
But I haven’t quite tired of the topic of getting things done. Doctorow says he’s published 16 books in the past decade. How?
“I figure out how much time I have to write a book. I figure out how many words I need to write. I convert that into a daily rate and I write that many words every day come hell or high water.” Before I can raise the question of quality, he goes on to explain that there’s very little correlation with what he thinks is good writing while he’s at the keyboard, and what later turns out to be good writing – and so he might as well just get the words down and sort it all out later. Lest that process sound like pure hackwork, Doctorow novels have won or been nominated for most of the science-fiction awards that count.
The “write it now and fix it later” approach sounds perfectly reasonable to me, but then Doctorow pushes it to an extreme. “For instance, I wrote Homeland  while I was touring Germany to publicise Little Brother . I had a translator, we’d visit lots of schools, and so I’d be speaking English half the time and he’d be speaking German half the time, and I’d write the book while he was speaking German.”
I point out that he is describing a superpower. Didn’t people wonder what he was doing as he sat in front of an audience tapping away on his computer while his translator spoke?
“Yes, but that was fine. At the end of the talk someone would say,” – and Doctorow assumes a gentle German accent – “‘Herr Doctorow, what are you doing with your computer on the stage?’ and I’d explain that I was writing my next book. They’d love that.”
Science fiction is often a way of exploring issues of contemporary relevance, and Doctorow’s work is no exception. In For the Win (2010), a novel aimed at the “young adult” market, he describes a battle between internationally mobile capital and the attempts of the trade union movement to mobilise “virtual sweatshop” workers across international boundaries. The action moves between India, where anything goes in a deregulated environment, and China, where the state is powerful but allied with the corporations in suppressing workers’ rights. The book manages to explore some complex economics in the context of a well-paced thriller.
Doctorow is clearly fascinated by economic issues, and points out that most science fiction and fantasy economies make no logical sense. The exception, he declares, is when Marxists write science fiction or fantasy. Take the recent Hobbit movie, for example. “How can the goblins have a mine that’s so inefficient?” he laughs, as he pauses from ripping the soft flesh from the marrowbones on his plate with his bare hands.
The porterhouse steak arrives, pre-sliced. It’s very good, charred on the outside but soft and pink beneath the surface. Doctorow has asked for horseradish while I am dipping my steak and chips into béarnaise sauce. The conversation is animated enough to slow our progress, and neither of us raises an eyebrow when a waiter noisily drops something fragile on the other side of the dining room.
So, I ask, if only Marxists get economics right in their novels, does that make Doctorow a Marxist? There’s a tension there, somehow – he’s a successful player in the market economy and fluently speaks the language of business; of profit, marketing reach, margins, and price discrimination. But his political activism seems squarely on the left – pro-labour, pro-equality, pro-rights.
“Marxists and capitalists agree on one thing: they agree that the economy is important. Once we’ve agreed on that we’re arguing over the details,” he says. But no, he’s not a Marxist. “I always missed the explanation of how the state is supposed to wither away.” In his novels and his blogging, the ruthless abuse of state power is just as much of a theme as the grasping amorality of large corporations.
Before long we’re talking about automation, and whether the rise of robots and algorithms is a threat to middle-class jobs. Doctorow’s next book will explore that territory in a suitably dystopian form, and he is keen to pick my brains about how things might play out. We discuss possible scenarios and I recommend an essay by John Maynard Keynes, “Economic Possibilities for our Grandchildren”. (Within hours he’s found it, read it and tweeted a recommendation.)
. . .
We’ve been making sufficiently slow progress through the meal that both of us have room for dessert. In fact, Doctorow effectively orders two – a crumble with cornflake ice-cream on the side. I order peanut butter caramel shortbread. After we both ask for double espresso, he pulls out a small plastic bottle that once contained mineral water. It’s half-full of a pale brown liquid. “I nearly forgot. I brought you some cold brew coffee.” I sniff at the concoction, the product of Doctorow’s latest coffee experiments. It’s made by steeping coffee in cold water overnight, and it smells sweet. When I try it later, the taste is mild but the caffeine jolt is fierce.
As we wait for dessert, I ask him about his recent speeches at technology conferences discussing the “war on general purpose computing”. He runs through the argument with practised fluency. Computers are by nature general-purpose machines. It’s impossible to make a computer that does all the kinds of things we want computers to do yet is somehow disabled from making copies of copyrighted material, or viewing child pornography, or sending instructions to a 3D printer to produce a gun.
“Oh my God, that’s good,” says Doctorow after his first mouthful of crumble. My peanut butter shortbread is fantastic too, if absurdly calorific. We are interrupted only by another waiter dropping a tray of glasses.
He continues with the argument. The impossibility of making limited-purpose computers won’t stop governments or corporations trying to put on the locks, or changing laws to try to make those locks effective. But the only way these limits can possibly work is subterfuge: computers therefore tend to contain concealed software that spies on what their users are trying to do. Such software is inevitably open to abuse and has often been abused in the past.
Digital rights management systems intended to prevent copying have been hijacked by virus-writers. In one notorious case, the Federal Trade Commission acted against seven computer rental companies and the software company that supplied them, alleging that the rental companies could activate hidden software to grab passwords, bank account details and even switch on the webcam to take photos of what the FTC coyly calls “intimate activities at home”. As computers surround us – in our cars, our homes, our pacemakers – Doctorow is determined to make people realise what’s at stake.
We polish off our coffee and desserts, and the conversation rolls on, covering digital media strategy, the future of book publishing, and Rupert Murdoch’s chances of keeping control of News Corp. I ask him about the FT’s business model. He approves of the use of the standard web language HTML5 in the FT app, which makes it less dependent on Apple or any single tablet format. “That’s a good idea,” he says. Then again, he adds, “selling a product that is well-liked by people who are price-insensitive is never a bad thing.”
We’ve been in the restaurant for three hours and are the only customers left. The staff wipe the tables around us and patiently bring flasks of tap water without being prompted. Yet another glass breaks, somewhere on the edge of my vision. “It’s not a good day for gravity,” says Doctorow.
I feel embarrassed that I’m the one who has to call things to a halt, but I’m going to be late for my next appointment. We admire the size of the bill, shake hands, and Doctorow heads off to the pool for a long swim.
That evening, I send him an email. His response is immediate.
157 Commercial Street
London E1 6BJ
Bone marrow & onions £7.00
Doddington Caesar £7.50
Porterhouse 900g £76.50
Triple-cooked chips £4.00
Creamed spinach £4.50
Apple crumble £6.75
Peanut butter shortbread £7.00
Cornflake ice-cream £3.00
Double espresso x2 £6.00
Sparkling water x2 £7.00
Moulin de Gassac x2 glasses £12.00
Total (incl service) £162.28
First published in the Financial Times, 13 July 2013