Generating a diverse set of possible solutions isn’t enough. The crowd also has to be able to distinguish the good solutions from the bad. We’ve already seen that groups seem to do a good job of making such distinctions. But does diversity matter to the group? In other words, once you’ve come up with a diverse set of possible solutions, does having a diverse group of decision makers make a difference?
It does, in two ways. Diversity helps because it actually adds perspectives that would otherwise be absent and because it takes away, or at least weakens, some of the destructive characteristics of group decision making. Fostering diversity is actually more important in small groups and in formal organizations than in the kinds of larger collectives—like markets or electorates—that we’ve already talked about for a simple reason: the sheer size of most markets, coupled with the fact that anyone with money can enter them (you don’t need to be admitted or hired), means that a certain level of diversity is almost guaranteed. Markets, for instance, are usually prima facie diverse because they’re made up of people with different attitudes toward risk, different time horizons, different investing styles, and different information, On teams or in organizations, by contrast, cognitive diversity needs to be actively selected, and it’s important to do so because in small groups it’s easy for a few biased individuals to exert undue influence and skew the group’s collective decision.
Scott Page is a political scientist at the University of Michigan who has done a series of intriguing experiments using computer- simulated problem-solving agents to demonstrate the positive effects of diversity. For instance, Page set up a series of groups of ten or twenty agents, with each agent endowed with a different set of skills, and had them solve a relatively sophisticated problem. Individually, some of the agents were very good at solving the problem while others were less effective. But what Page found was that a group made up of some smart agents and some not-so-smart agents almost always did better than a group made up just of smart agents. You could do as well or better by selecting a group randomly and letting it solve the problem as by spending a lot of time trying to find the smart agents and then putting them alone on the problem.
The point of Page’s experiment is that diversity is, on its own, valuable, so that the simple fact of making a group diverse makes it better at problem solving. That doesn’t mean that intelligence is irrelevant—none of the agents in the experiment were ignorant, and all the successful groups had some high-performing agents in them. But it does mean that, on the group level, intelligence alone is not enough, because intelligence alone cannot guarantee you different perspectives on a problem. in fact, Page speculates, grouping only smart people together dpesn’t work that well because the smart people (whatever that means) tend to resemble each other in what they can do. If you think about intelligence as a kind of toolbox of skills, the list of skills that are the “best” is relatively small, so that people who have them tend to be alike. This is normally a good thing, but it means that as a whole the group knows less than it otherwise might. Adding in a few people who know less, but have different skills, actually improves the group’s performance.
This seems like an eccentric conclusion, and it is. It just happens to be true. The legendary organizational theorist James G. March, in fact, put it like this: “The development of knowledge may depend on maintaining an influx of the naïve and the ignorant, and,. . competitive victory does not reliably go to the properly educated.” The reason, March suggested, is that groups that are too much alike find it harder to keep learning, because each member is bringing less and less new information to the table. Homogeneous groups are great at doing what they do well, but they become progressively less able to investigate alternatives. Or, as March has famously argued, they spend too much time exploiting and not enough time exploring Bringing new members into the organization, even if they’re less experienced and less capable, actually makes the group smarter simply because what little the new memhers do know is not redundant with what everyone else knows. As March wrote, “[The] effect does not come from the superior knowledge of the average new recruit. Recruits are, on average, less knowledgeable than the individuals they replace. The gains come from their diversity.”
Chapter Two, Part I
In 1899, Ransom E. Olds opened the Olds Motor Works in Detroit, Michigan. Olds had been in the automobile business since the midI 880s, when he built his first car, a steam-powered vehicle with three wheels. But success had remained elusive. After moving on to gasoline-powered cars, Olds started his own company in the early I 890s, but it floundered, leaving him nearly, destitute. He was only able to start the Motor Works, in fact, by convincing a financier named Samuel Smith to put up nearly all the money. Olds got his company, but he also got a boss to whom he had to answer. This was a problem, because the two did not agree on what the Olds Motor Works should be making. Smith thought the company should cater to the high end of the market, building large, expensive cars with all the trimmings. Olds, though, was more intrigued by the possibility of building a car that could be marketed to the middle class. In 1900, the auto market was still minuscule—there were fewer than 15,000 cars on the road that year. But it seemed plausible that an invention as revolutionary as the car would be able to find a mass audience, if you could figure out a way to make one cheaply enough.
Olds couldn’t commit himself to one idea, though. Instead, he dabbled, building eleven different prototypes in the company’s first year, including electric-powered cars in addition to steamers and internal-combustion-powered vehicles. It was a strategy that seemed destined for failure. But in March of 1901, bad luck lent a helping hand. Olds’s factory burned down, and all the prototypes went up in flames. All, that is, but one—which happened to be right near the door, and to be light enough that the lone man present could push it to safety. The prototype that survived, fortuitously enough, was the inexpensive, low-cost model that Olds had imagined could be sold to a much larger market. in the wake of the fire, Olds rushed the prototype into production. The vehicle he produced was known as the “curved-dash Olds,” since the floor curved up to form the dashboard. In design, it was an ungainly thing, a horseless carriage, started by a seat-side crank and steered by a tiller. It had two forward gears, one reverse, and a small, single-cylinder engine. It won no points for style. But at $600, it was within the reach of many Americans.
Though Olds was an engineer, he turned out to be something of a marketing whiz, too. He concocted elaborate publicity stunts— like sending a young driver eight hundred miles cross-country in an Olds to the Manhattan Auto Show—that won the attention of the press and of auto dealers while demonstrating to a still-skeptical public that the automobile was not just a gimmick. He drove a souped-up Olds in the first race at Daytona Beach. And in 1903, the company sold 4,000 vehicles, more than any other U.S. manufacturer, while two years later it stild 6,500 cars. Olds, it turned out, had designed the first mass-produced automobile in American history.
Olds’s success came in the face of fierce competition. In that first decade of the twentieth century, there were literally hundreds of companies trying to make automobiles. And because there was no firm definition of what a car should look like, or what kind of engine it should have, those companies offered a bewildering variety of vehicles, including the aforementioned steamers and battery- powered cars. The victory of the gasoline-powered engine was not a foregone conclusion. Thomas Edison, for instance, had designed a battery-powered vehicle, and in 1899 one sage had offered the prediction that “the whole of the United States will be sprinkled with electric changing stations.” At one point, a third of all the cars on U.S. roads were electric-powered. Similarly, steam-powered engines were seen by many as the most logical way to propel a vehicle, since steam obviously worked so well in propelling trains and boats. In the early part of the decade, there were more than a hundred makers of steam-powered cars, and the most successful of these, the Stanley Steamer, became legendary for its speed—in 1905, it went 127 miles per hour—and the comfort of its ride.
As the decade wore on, though, the contenders began to fade. Electric-powered cars couldn’t go far enough without a recharge. Steam-powered cars took a long time to heat up. More important, though, the makers of gasoline-powered cars were the first to invest heavily in mass-production techniques and to figure out a way to reach the mass market. Olds had been the first automaker to buy different parts from different manufacturers, instead of making them all itself. Cadillac became the first manufacturer successfully to use standardized components, which cut down on the time and cost of manufacturing. And Ford, of course, revolutionized the industry with the moving assembly line and a relentless focus on producing one kind of car as cheaply as possible. By the time of World War I, there were still more than a hundred automakers in America. But more than four hundred car companies had gone out of business or been acquired, including the Olds Motor Works, which had been bought by General Motors.
As for Olds himself, he never really got to enjoy the early success of his company since he left it after only a few years following a fight with Samuel Smith’s sons. He eventually started a new car company called REQ. But the moment had passed him by. What he had started, Henry Ford—who by World War I made almost half the cars in America—had finished. There was no more talk of steam- or electric-powered vehicles, and cars no longer came in a bewildering variety of shapes and sizes. Everyone knew what an automobile looked like. It looked like a Model T.
THE STORY OF THE early days of the U.S. auto industry is not an unusual one. In fact, if you look at the histories of most new industries in America, from the railroads to television to personal computers to, most recently, the Internet, you’ll see a similar pattern. In all these cases, the early days of the business are characterized by a profusion of alternatives, many of them dramatically different from each other in design and technology As time passes, the market winnows out the winners and losers, effectively choosing which technologies will flourish and which will disappear. Most of the companies fail, going bankrupt or getting acquired by other firms. At the end of the day, a few players are left standing and in control of most of the market.
This seems like a wasteful way of developing and selling new technologies. And, the experience of Google notwithstanding, there is no guarantee that.at the end of the process, the best technology will necessarily win (since the crowd is not deciding all at once, but rather over time). So why do it this way?
For an answer, consider a hive of bees. Bees are remarkably efficient at finding food. According to Thomas Seeley, author of The Wisdom of the Hive, a typical bee colony can search six or more kilometers from the hive, and if there is a flower patch within two kilometers of the hive, the bees have a better-than-half chance of finding it. How do the bees do this? They don’t sit around and have a collective discussion about where foragers should go. Instead, the hive sends out a host of scout bees to search the surrounding area. When a scout bee has found a nectar source that seems strong, he comes back and does a waggle dance, the intensity of which is shaped, in some way, by the excellence of the nectar supply at the site. The waggle dance attracts other forager bees, which follow the first forager, while foragers who have found less-good sites attract fewer followers and, in some cases, eventually abandon their sites entirely. The result is that bee foragers end up distributing themselves across different nectar sources in an almost perfect fashion, meaning that they get as much food as possible relative to the time and energy they put into searching. It is a collectively brilliant solution to the colony’s food problem.
What’s important, though, is the way the colony gets to that collectively intelligent solution. It does not get there by first rationally considering all the alternatives and then determining an ideal foraging pattern. It can’t do this, because it doesn’t have any idea what the possible alternatives—that is, where the different flower patches—are, So instead, it sends out scouts in many different directions and trusts that at least one of them will find the best patch, return, and do a good dance so that the hive will know where the food source is.
This is, it’s important to see, different from the kind of problem solving that we looked at earlier. In the case of the ox-weighing experiment, or the location of the Scorpion, or the betting markets, or the JEM, the group’s job was to decide among already defined choices or to solve a well-defined problem. In those cases, different members of the group could bring differe’nt pieces of information to bear on a problem, but the set of possible solutions was already, in a sense, determined. (Bush or Gore would become president; the Yankees or the Marlins would win the World Series.) In the case of problems like finding the most nectar-rich flower patches, though, the task is more complicated. It becomes a twofold process. First, uncover the possible alternatives. Then decide among them.
In the first stage of this process, the list of possible solutions is so long that the smart thing to do is to send out as many scout bees as possible. You can think of Ransom Olds and Henry Ford and the countless would-be automakers who tried and failed, then, as foragers. They discovered (in this case, by inventing) the sources of nectar—the gasoline-powered car, mass production, the moving assembly line—and then asked the crowd to render its verdict. You might even see Olds’s publicity stunts as a kind of equivalent to the waggle dance.
One key to this approach is a system that encourages, and funds, speculative ideas even though they have only slim possibilities of success. Even more important, though, is diversity—not in a sociological sense, but rather in a conceptual and cognitive sense. You want diversity among the entrepreneurs who are coming up with the ideas, so you end up with meaningful differences among those ideas rather than minor variations on the same concept. But you also want diversity among the people who have the money, too. If one virtue of a decentralized economy is that it diffuses decision- making power (at least on a small scale) throughout the system, that virtue becomes meaningless if all the people with power are alike (or if, as we’ll see in the next chapter, they become alike through imitation). The more similar they are, the more similar the ideas they appreciate will be, and so the set of new products and concepts the rest of us see will be smaller than possible. By contrast, if they are diverse, the chances that at least someone will take a gamble on a radical or unlikely idea obviously increases. Take the early days of radio, when three companies—American Marconi, NESCO, and De Forest Wireless Telegraphy—dominated the industry American Marconi relied on investment banks to raise its capital from large private investors; NESCO was funded by two rich men from Pittsburgh; and De Forest Wire’ess Telegraphy was owned by small stockholders looking for a speculative gain. The variety of possible funding sources encouraged a variety of technological approaches.
Of course, even with diverse sources of funding, most endeavors will end up as failures. This was nicely expressed by Jeff Bezos, the CEO of Amazon, when he compared the Internet boom to the Cambrian explosion, which was the period in evolutionary history that saw the birth and the extinction of more species than any other period. The point is that you cannot, or so at least it seems, have one without the other. It’s a familiar truism that governments can’t, and therefore shouldn’t try to, “pick winners.” But the truth is that no system seems all that good at picking winners in advance. After all, tens of thousands of new products are introduced every year, and only a small fraction ever become successes. The steam- powered car, the picturephone, the Edsel, the Betamax, pen computing: companies place huge bets on losers all the time. What makes a system successful is its ability to recognize losers and kill them quickly Or, rather, what makes a system successful is its ability to generate lots of losers and then to recognize them as such and kill them off. Sometimes the messiest approach is the wisest.
Olds couldn’t commit himself to one idea, though. Instead, he dabbled, building eleven different prototypes in the company’s first year, including electric-powered cars in addition to steamers and internal-combustion-powered vehicles. It was a strategy that seemed destined for failure. But in March of 1901, bad luck lent a helping hand. Olds’s factory burned down, and all the prototypes went up in flames. All, that is, but one—which happened to be right near the door, and to be light enough that the lone man present could push it to safety. The prototype that survived, fortuitously enough, was the inexpensive, low-cost model that Olds had imagined could be sold to a much larger market. in the wake of the fire, Olds rushed the prototype into production. The vehicle he produced was known as the “curved-dash Olds,” since the floor curved up to form the dashboard. In design, it was an ungainly thing, a horseless carriage, started by a seat-side crank and steered by a tiller. It had two forward gears, one reverse, and a small, single-cylinder engine. It won no points for style. But at $600, it was within the reach of many Americans.
Though Olds was an engineer, he turned out to be something of a marketing whiz, too. He concocted elaborate publicity stunts— like sending a young driver eight hundred miles cross-country in an Olds to the Manhattan Auto Show—that won the attention of the press and of auto dealers while demonstrating to a still-skeptical public that the automobile was not just a gimmick. He drove a souped-up Olds in the first race at Daytona Beach. And in 1903, the company sold 4,000 vehicles, more than any other U.S. manufacturer, while two years later it stild 6,500 cars. Olds, it turned out, had designed the first mass-produced automobile in American history.
Olds’s success came in the face of fierce competition. In that first decade of the twentieth century, there were literally hundreds of companies trying to make automobiles. And because there was no firm definition of what a car should look like, or what kind of engine it should have, those companies offered a bewildering variety of vehicles, including the aforementioned steamers and battery- powered cars. The victory of the gasoline-powered engine was not a foregone conclusion. Thomas Edison, for instance, had designed a battery-powered vehicle, and in 1899 one sage had offered the prediction that “the whole of the United States will be sprinkled with electric changing stations.” At one point, a third of all the cars on U.S. roads were electric-powered. Similarly, steam-powered engines were seen by many as the most logical way to propel a vehicle, since steam obviously worked so well in propelling trains and boats. In the early part of the decade, there were more than a hundred makers of steam-powered cars, and the most successful of these, the Stanley Steamer, became legendary for its speed—in 1905, it went 127 miles per hour—and the comfort of its ride.
As the decade wore on, though, the contenders began to fade. Electric-powered cars couldn’t go far enough without a recharge. Steam-powered cars took a long time to heat up. More important, though, the makers of gasoline-powered cars were the first to invest heavily in mass-production techniques and to figure out a way to reach the mass market. Olds had been the first automaker to buy different parts from different manufacturers, instead of making them all itself. Cadillac became the first manufacturer successfully to use standardized components, which cut down on the time and cost of manufacturing. And Ford, of course, revolutionized the industry with the moving assembly line and a relentless focus on producing one kind of car as cheaply as possible. By the time of World War I, there were still more than a hundred automakers in America. But more than four hundred car companies had gone out of business or been acquired, including the Olds Motor Works, which had been bought by General Motors.
As for Olds himself, he never really got to enjoy the early success of his company since he left it after only a few years following a fight with Samuel Smith’s sons. He eventually started a new car company called REQ. But the moment had passed him by. What he had started, Henry Ford—who by World War I made almost half the cars in America—had finished. There was no more talk of steam- or electric-powered vehicles, and cars no longer came in a bewildering variety of shapes and sizes. Everyone knew what an automobile looked like. It looked like a Model T.
THE STORY OF THE early days of the U.S. auto industry is not an unusual one. In fact, if you look at the histories of most new industries in America, from the railroads to television to personal computers to, most recently, the Internet, you’ll see a similar pattern. In all these cases, the early days of the business are characterized by a profusion of alternatives, many of them dramatically different from each other in design and technology As time passes, the market winnows out the winners and losers, effectively choosing which technologies will flourish and which will disappear. Most of the companies fail, going bankrupt or getting acquired by other firms. At the end of the day, a few players are left standing and in control of most of the market.
This seems like a wasteful way of developing and selling new technologies. And, the experience of Google notwithstanding, there is no guarantee that.at the end of the process, the best technology will necessarily win (since the crowd is not deciding all at once, but rather over time). So why do it this way?
For an answer, consider a hive of bees. Bees are remarkably efficient at finding food. According to Thomas Seeley, author of The Wisdom of the Hive, a typical bee colony can search six or more kilometers from the hive, and if there is a flower patch within two kilometers of the hive, the bees have a better-than-half chance of finding it. How do the bees do this? They don’t sit around and have a collective discussion about where foragers should go. Instead, the hive sends out a host of scout bees to search the surrounding area. When a scout bee has found a nectar source that seems strong, he comes back and does a waggle dance, the intensity of which is shaped, in some way, by the excellence of the nectar supply at the site. The waggle dance attracts other forager bees, which follow the first forager, while foragers who have found less-good sites attract fewer followers and, in some cases, eventually abandon their sites entirely. The result is that bee foragers end up distributing themselves across different nectar sources in an almost perfect fashion, meaning that they get as much food as possible relative to the time and energy they put into searching. It is a collectively brilliant solution to the colony’s food problem.
What’s important, though, is the way the colony gets to that collectively intelligent solution. It does not get there by first rationally considering all the alternatives and then determining an ideal foraging pattern. It can’t do this, because it doesn’t have any idea what the possible alternatives—that is, where the different flower patches—are, So instead, it sends out scouts in many different directions and trusts that at least one of them will find the best patch, return, and do a good dance so that the hive will know where the food source is.
This is, it’s important to see, different from the kind of problem solving that we looked at earlier. In the case of the ox-weighing experiment, or the location of the Scorpion, or the betting markets, or the JEM, the group’s job was to decide among already defined choices or to solve a well-defined problem. In those cases, different members of the group could bring differe’nt pieces of information to bear on a problem, but the set of possible solutions was already, in a sense, determined. (Bush or Gore would become president; the Yankees or the Marlins would win the World Series.) In the case of problems like finding the most nectar-rich flower patches, though, the task is more complicated. It becomes a twofold process. First, uncover the possible alternatives. Then decide among them.
In the first stage of this process, the list of possible solutions is so long that the smart thing to do is to send out as many scout bees as possible. You can think of Ransom Olds and Henry Ford and the countless would-be automakers who tried and failed, then, as foragers. They discovered (in this case, by inventing) the sources of nectar—the gasoline-powered car, mass production, the moving assembly line—and then asked the crowd to render its verdict. You might even see Olds’s publicity stunts as a kind of equivalent to the waggle dance.
One key to this approach is a system that encourages, and funds, speculative ideas even though they have only slim possibilities of success. Even more important, though, is diversity—not in a sociological sense, but rather in a conceptual and cognitive sense. You want diversity among the entrepreneurs who are coming up with the ideas, so you end up with meaningful differences among those ideas rather than minor variations on the same concept. But you also want diversity among the people who have the money, too. If one virtue of a decentralized economy is that it diffuses decision- making power (at least on a small scale) throughout the system, that virtue becomes meaningless if all the people with power are alike (or if, as we’ll see in the next chapter, they become alike through imitation). The more similar they are, the more similar the ideas they appreciate will be, and so the set of new products and concepts the rest of us see will be smaller than possible. By contrast, if they are diverse, the chances that at least someone will take a gamble on a radical or unlikely idea obviously increases. Take the early days of radio, when three companies—American Marconi, NESCO, and De Forest Wireless Telegraphy—dominated the industry American Marconi relied on investment banks to raise its capital from large private investors; NESCO was funded by two rich men from Pittsburgh; and De Forest Wire’ess Telegraphy was owned by small stockholders looking for a speculative gain. The variety of possible funding sources encouraged a variety of technological approaches.
Of course, even with diverse sources of funding, most endeavors will end up as failures. This was nicely expressed by Jeff Bezos, the CEO of Amazon, when he compared the Internet boom to the Cambrian explosion, which was the period in evolutionary history that saw the birth and the extinction of more species than any other period. The point is that you cannot, or so at least it seems, have one without the other. It’s a familiar truism that governments can’t, and therefore shouldn’t try to, “pick winners.” But the truth is that no system seems all that good at picking winners in advance. After all, tens of thousands of new products are introduced every year, and only a small fraction ever become successes. The steam- powered car, the picturephone, the Edsel, the Betamax, pen computing: companies place huge bets on losers all the time. What makes a system successful is its ability to recognize losers and kill them quickly Or, rather, what makes a system successful is its ability to generate lots of losers and then to recognize them as such and kill them off. Sometimes the messiest approach is the wisest.
Intelligent Tolls
When a driver uses a busy road he is imposing a delay cost on all other drivers. However, as road users we do not take the costs we impose on others into account, thus roads like most common resources are over used and society is worse off.
Road pricing is a method to try and internalise the costs drivers impose on others and to encourage more efficient use of the road network. London use a congestion charge for cars entering the centre of London, and makes some mild claims about it's effectiveness. The problem with the London congestion charge is that it is too blunt a tool. It is a fixed charge that does not respond to demand and increased congestion. When roads are moving freely the congestion charge should be low, when roads are choked the charge should be high to encourage drivers to find alternatives. This is the basis of Vickrey congestion pricing.
Singapore is one of the best examples of Vickrey pricing in action. Here is a recent report from the Financial Times and the video below the fold gives a television news report on the charges.
Road pricing is a method to try and internalise the costs drivers impose on others and to encourage more efficient use of the road network. London use a congestion charge for cars entering the centre of London, and makes some mild claims about it's effectiveness. The problem with the London congestion charge is that it is too blunt a tool. It is a fixed charge that does not respond to demand and increased congestion. When roads are moving freely the congestion charge should be low, when roads are choked the charge should be high to encourage drivers to find alternatives. This is the basis of Vickrey congestion pricing.
Singapore is one of the best examples of Vickrey pricing in action. Here is a recent report from the Financial Times and the video below the fold gives a television news report on the charges.
Chapter Seven, Part I
In 2002, central London was, to all intents and puoses, a perpetual traffic jam. On a typical day, a quarter of a million vehicles would drive into the eight square miles of central London, ready to do battle with a million commuters who used public transportation. In place of long, wide boulevards, London has tightly packed, narrow, winding streets, which kept the average speed of traffic below ten miles an hour. On a bad day, it was more like three miles an hour. You can walk faster than that and not even break a sweat.
Traffic was so bad, in fact, that it turned the mayor of London, Ken Livingstone, from an avowed socialist into the advocate of a plan that warmed the hearts of capitalist economists everywhere. In February of 2003, London started charging people to drive into the centre. If you wanted to enter central London between 7 AM and 6:30 PM, you now had to pay £5. If you neglected to pay, and one of the 230 cameras the city had installed recorded your license plate, you got stuck with an £80 fine. In theory, the plan was supposed to raise £180 million a year for the city to invest in public transportation, and to cut traffic congestion by 20 percent.
The principle behind the London plan was a simple one: when someone drives into the city and makes traffic worse, he inflicts costs on everyone else that he never pays for. When you’re that driver and you’re sitting in bumper-to-bumper traffic while toddlers speed by you on the sidewalk, it feels like you’ve paid more than enough. But the mathematics of congestion suggest that you haven’t. The toll is an attempt to collect the bill.
“Congestion pricing” has been around as an idea since the 1920s, but its most important advocate was the Nobel Prize-winning economist William Vickrey. For Vickrey, road space was like any other scarce resource: if you wanted to allocate it wisely, you needed some way to make the costs and benefits of people’s decisions obvious to them. Because, say, the main road into the city is free, everyone chooses to drive on that road during rush hour even when it would be better for just about everybody if some of them drove earlier or later, some took public transportation instead, and some worked from home. If that same road had a toll on it, different people would make different choices, because they would have different answers to the question, “How much is this trip really worth to me?” And so, instead of everyone ending up on the same road at 6:30 PM, they’d leave work earlier or later, or take the train, or telecommute.
It’s a nice idea in theory, but putting it into practice has always required a very hard sell. Livingstone had to fight off massive lobbying efforts in opposition to his plan for London. In thç United States, meanwhile, congestion pricing has always been a non- starter. Americans don’t like paying highway and bridge tolls, but they hate the idea of having to pay more money to drive during rush hour. Most people feel as if they have no choice about when or how they commute, and the thought of the wealthy paying to zip along empty roads while everyone else takes the long way around grates. As a result, we’d rather suffer in traffic than allow some to pay for freedom. The authors of a study of a failed attempt to introduce congestion pricing on San Francisco’s Bay Bridge, for instance, concluded that both voters and politicians need to be convinced there are literally no other alternatives before they’ll accept a Vickrey scheme. There are a few exceptions to this rule, most notably New York City, where it costs more to use certain bridges and tunnels during rush hour. But there are only a few.
Oddly, we’ve happily adjusted to something like congestion pricing in other parts of our life. Long-distance calls are more expensive during the day, drinks are cheaper during happy hour, and it costs more to go to Las Vegas on the weekend than during the week. (And don’t forget the early-bird special, either.) All these are cases of price responding to demand—when demand is high, the price goes up, and when it’s low, the price goes down. But when it comes to driving, Americans seem to prefer it when there’s no price—at least in money terms—at all.
It’s not surprising, then, that the one place in the world that’s made an art out of congestion pricing is the antithesis of America in cultural terms, namely Singapore. Blessed with not having to worry about angry drivers’ groups or disgruntled voters, Singapore’s government put congestion pricing in place for the first time in 1975. The initial version of the plan looked a lot like London’s more recent scheme: you had to pay a toll if you wanted to get into the country’s central business district (CBD) during rush hour. As time went on, the plan expanded, until you had to pay if you wanted to get into the CBD at any time during the day. But the most important changes have been technological. Once upon a time, the system was enforced by meter maids who recorded the license plate numbers of rule breakers. Today, every car in Singapore has an electronic smart card attached to the dashboard, and as soon as you cross into a pay zone, you see the money disappear from your card. This has two advantages: it makes cheating impossible, and it makes the cost of your decision to drive immediately obvious to you. Singapore has also made its pricing rules more sophisticated. While there was once one price to drive during the morning rush hour, now there is “peak-within-peak” pricing (it’s half as expensive to drive between 7:30 and 8 AM as it is to drive between 8 and 9 AM), and evening pricing. Singapore even offers weekend-only cars (on which drivers get a tax break and price rebates). Not surprisingly, traffic in Singapore is much better than it is in London or New York, even though the country has more cars per mile of road than any Western country. (Of course, it is a very small country.)
The interesting thing about Singapore’s success is that for all of the country’s authoritarian ways, it has left the actual decision about whether or not to drive in the hands of the individual. One way to cure congestion, after all, would simply be to ban certain people from driving on certain days. And this, in fact, is exactly what Mexico did, albeit in an attempt to curb air pollution. If you live in Mexico City and your license plate ends with a 5 or 6, you can’t drive on Monday. (A or 8 means you’re out of luck on Tuesday, 3 or 4 on Wednesday, and so on. Everyone gets to drive on Saturday and Sunday.) But this hasn’t done much to reduce traffic, because drivers have no incentive to find alternatives to driving on the six days a week when they can drive, and because many Mexicans just bought second cars that they could use on their supposed off days. Singapore’s plan, by contrast, tells the drivers how much it’ll cost to use their cars, and then trusts that the sum of all those individual decisions about whether or not to drive will be smart.
Figuring out how much driving should cost, though, is a tough problem, and economists have spilled a lot of ink trying to solve it. One obvious challenge is t1at the wealthier you are, the easier it is to trade money for time and convenience (you’ll pay to drive into London because it’s easier than taking the tube). Poorer people can avoid the toll by not driving, but that doesn’t make them any better off than they were before. So any fair congestion-pricing plan has not only to charge tolls but also redistribute the revenue they raise. Singapore did that by building a hyper-modern mass rapid transit system, and Livingstone’s plan for London similarly involves spending hundreds of millions on public transportation. Another alternative, proposed by the traffic engineer Carlos Daganzo, is to allow people to drive for free on some days and charge them on others.
That keeps the right incentives in place, but also keeps the money- for-time crowd from dominating the highways.
An ideal pricing system would be considerably more sophisticated than London’s £5 all-day system. Vickrey, for instance, imagined a world in which traffic was governed by “responsive pricing,” so that how much you had to pay to use a road might vary depending on how heavy the traffic on that road was right then, or on the weather, or on the type of vehicle you were driving. If I-S between Sacramento and San Francisco suddenly became clogged with traffic because of a broken-down tractor trailer, it would cost you more to use it. That, presumably, would divert people to other routes, and keep the congestion from getting out of control. Today a system like this is actually technologically feasible. It is, of course, a political pipe dream, and hyper-responsivc pricing may in any case be more trouble than it’s worth. (Is it a good idea to have people carrying out complex price calculations while traveling at seventy miles an hour?) But the possibilities created by things like highways wired with traffic-detecting sensors and cars equipped with global positioning systems are endless.
Still, crude as it is, the London plan has been far more successful than most noneconomists thought it would he. Traffic has fallen by almost 20 percent, congestion has been significantly reduced, and, according to at least one study, cars are able to go 40 percent faster. (That still means they’re only going eleven miles an hour, but you take what you can get.) The biggest concern people have now is that the plan may have been too successful in curtailing driving. After all, the point of congestion pricing isn’t to stop people from driving, since from an economic perspective (and setting aside the environmental one), a highway that’s empty is hardly better than one that’s too full. The point of congestion pricing is to get people to coordinate their activities better by balancing the benefits they get from driving against the costs they inflict on everyone else. In the London case, the concerns about the traffic decline have been overblown. The roads are still full of cars.
They’re just moving more easily. More important, the flow of traffic is now a better reflection of the real value people place on driving. At least for the moment, London traffic is wiser.
Traffic was so bad, in fact, that it turned the mayor of London, Ken Livingstone, from an avowed socialist into the advocate of a plan that warmed the hearts of capitalist economists everywhere. In February of 2003, London started charging people to drive into the centre. If you wanted to enter central London between 7 AM and 6:30 PM, you now had to pay £5. If you neglected to pay, and one of the 230 cameras the city had installed recorded your license plate, you got stuck with an £80 fine. In theory, the plan was supposed to raise £180 million a year for the city to invest in public transportation, and to cut traffic congestion by 20 percent.
The principle behind the London plan was a simple one: when someone drives into the city and makes traffic worse, he inflicts costs on everyone else that he never pays for. When you’re that driver and you’re sitting in bumper-to-bumper traffic while toddlers speed by you on the sidewalk, it feels like you’ve paid more than enough. But the mathematics of congestion suggest that you haven’t. The toll is an attempt to collect the bill.
“Congestion pricing” has been around as an idea since the 1920s, but its most important advocate was the Nobel Prize-winning economist William Vickrey. For Vickrey, road space was like any other scarce resource: if you wanted to allocate it wisely, you needed some way to make the costs and benefits of people’s decisions obvious to them. Because, say, the main road into the city is free, everyone chooses to drive on that road during rush hour even when it would be better for just about everybody if some of them drove earlier or later, some took public transportation instead, and some worked from home. If that same road had a toll on it, different people would make different choices, because they would have different answers to the question, “How much is this trip really worth to me?” And so, instead of everyone ending up on the same road at 6:30 PM, they’d leave work earlier or later, or take the train, or telecommute.
It’s a nice idea in theory, but putting it into practice has always required a very hard sell. Livingstone had to fight off massive lobbying efforts in opposition to his plan for London. In thç United States, meanwhile, congestion pricing has always been a non- starter. Americans don’t like paying highway and bridge tolls, but they hate the idea of having to pay more money to drive during rush hour. Most people feel as if they have no choice about when or how they commute, and the thought of the wealthy paying to zip along empty roads while everyone else takes the long way around grates. As a result, we’d rather suffer in traffic than allow some to pay for freedom. The authors of a study of a failed attempt to introduce congestion pricing on San Francisco’s Bay Bridge, for instance, concluded that both voters and politicians need to be convinced there are literally no other alternatives before they’ll accept a Vickrey scheme. There are a few exceptions to this rule, most notably New York City, where it costs more to use certain bridges and tunnels during rush hour. But there are only a few.
Oddly, we’ve happily adjusted to something like congestion pricing in other parts of our life. Long-distance calls are more expensive during the day, drinks are cheaper during happy hour, and it costs more to go to Las Vegas on the weekend than during the week. (And don’t forget the early-bird special, either.) All these are cases of price responding to demand—when demand is high, the price goes up, and when it’s low, the price goes down. But when it comes to driving, Americans seem to prefer it when there’s no price—at least in money terms—at all.
It’s not surprising, then, that the one place in the world that’s made an art out of congestion pricing is the antithesis of America in cultural terms, namely Singapore. Blessed with not having to worry about angry drivers’ groups or disgruntled voters, Singapore’s government put congestion pricing in place for the first time in 1975. The initial version of the plan looked a lot like London’s more recent scheme: you had to pay a toll if you wanted to get into the country’s central business district (CBD) during rush hour. As time went on, the plan expanded, until you had to pay if you wanted to get into the CBD at any time during the day. But the most important changes have been technological. Once upon a time, the system was enforced by meter maids who recorded the license plate numbers of rule breakers. Today, every car in Singapore has an electronic smart card attached to the dashboard, and as soon as you cross into a pay zone, you see the money disappear from your card. This has two advantages: it makes cheating impossible, and it makes the cost of your decision to drive immediately obvious to you. Singapore has also made its pricing rules more sophisticated. While there was once one price to drive during the morning rush hour, now there is “peak-within-peak” pricing (it’s half as expensive to drive between 7:30 and 8 AM as it is to drive between 8 and 9 AM), and evening pricing. Singapore even offers weekend-only cars (on which drivers get a tax break and price rebates). Not surprisingly, traffic in Singapore is much better than it is in London or New York, even though the country has more cars per mile of road than any Western country. (Of course, it is a very small country.)
The interesting thing about Singapore’s success is that for all of the country’s authoritarian ways, it has left the actual decision about whether or not to drive in the hands of the individual. One way to cure congestion, after all, would simply be to ban certain people from driving on certain days. And this, in fact, is exactly what Mexico did, albeit in an attempt to curb air pollution. If you live in Mexico City and your license plate ends with a 5 or 6, you can’t drive on Monday. (A or 8 means you’re out of luck on Tuesday, 3 or 4 on Wednesday, and so on. Everyone gets to drive on Saturday and Sunday.) But this hasn’t done much to reduce traffic, because drivers have no incentive to find alternatives to driving on the six days a week when they can drive, and because many Mexicans just bought second cars that they could use on their supposed off days. Singapore’s plan, by contrast, tells the drivers how much it’ll cost to use their cars, and then trusts that the sum of all those individual decisions about whether or not to drive will be smart.
Figuring out how much driving should cost, though, is a tough problem, and economists have spilled a lot of ink trying to solve it. One obvious challenge is t1at the wealthier you are, the easier it is to trade money for time and convenience (you’ll pay to drive into London because it’s easier than taking the tube). Poorer people can avoid the toll by not driving, but that doesn’t make them any better off than they were before. So any fair congestion-pricing plan has not only to charge tolls but also redistribute the revenue they raise. Singapore did that by building a hyper-modern mass rapid transit system, and Livingstone’s plan for London similarly involves spending hundreds of millions on public transportation. Another alternative, proposed by the traffic engineer Carlos Daganzo, is to allow people to drive for free on some days and charge them on others.
That keeps the right incentives in place, but also keeps the money- for-time crowd from dominating the highways.
An ideal pricing system would be considerably more sophisticated than London’s £5 all-day system. Vickrey, for instance, imagined a world in which traffic was governed by “responsive pricing,” so that how much you had to pay to use a road might vary depending on how heavy the traffic on that road was right then, or on the weather, or on the type of vehicle you were driving. If I-S between Sacramento and San Francisco suddenly became clogged with traffic because of a broken-down tractor trailer, it would cost you more to use it. That, presumably, would divert people to other routes, and keep the congestion from getting out of control. Today a system like this is actually technologically feasible. It is, of course, a political pipe dream, and hyper-responsivc pricing may in any case be more trouble than it’s worth. (Is it a good idea to have people carrying out complex price calculations while traveling at seventy miles an hour?) But the possibilities created by things like highways wired with traffic-detecting sensors and cars equipped with global positioning systems are endless.
Still, crude as it is, the London plan has been far more successful than most noneconomists thought it would he. Traffic has fallen by almost 20 percent, congestion has been significantly reduced, and, according to at least one study, cars are able to go 40 percent faster. (That still means they’re only going eleven miles an hour, but you take what you can get.) The biggest concern people have now is that the plan may have been too successful in curtailing driving. After all, the point of congestion pricing isn’t to stop people from driving, since from an economic perspective (and setting aside the environmental one), a highway that’s empty is hardly better than one that’s too full. The point of congestion pricing is to get people to coordinate their activities better by balancing the benefits they get from driving against the costs they inflict on everyone else. In the London case, the concerns about the traffic decline have been overblown. The roads are still full of cars.
They’re just moving more easily. More important, the flow of traffic is now a better reflection of the real value people place on driving. At least for the moment, London traffic is wiser.
Chapter One, Part V
If allowing people to bet on sporting events effectively creates a kind of machine that’s good at predicting the outcome of those events, an obvious question follows: Wouldn’t people betting on other kinds of events be equally good, as a group, at predicting them? Why confine ourselves to knowing what the chances are of Los Angeles beating Sacramento if there’s a way we could know what the chances are of, say, George W. Bush beating John Kerry?
We do have a well-established way of knowing what George W. Bush’s chances are: the poll. If you want to know how people are going to vote, you just ask them. Polling is, relatively speaking, accurate. It has a solid methodology behind it, and is statistically rigorous. But there’s reason to wonder if a market such as the betting market—one that allowed the people participating in it to rely on many different kinds of information, including but not limited to polls—might at the very least offer a competitive alternative to Gallup. That’s why the Iowa Electronic Markets (IEM) project was created.
Founded in 1988 and run by the College of Business at the University of Iowa, the IEM features a host of markets designed to predict the outcomes of elections—presidential, congressional, gubernatorial, and foreign. Open to anyone who wants to participate, the IEM allows people to buy and sell futures contracts” based on how they think a given candidate will do in an upcoming election. While the IEM offers many different types of contracts, two are most common. One is designed to predict the winner of an election. In the case of the California recall in 2003, for instance, you could have bought an “Arnold Schwarzenegger to win” contract, which would have paid you $1 when Schwarzenegger won. Had he lost, you would have gotten nothing. The price you pay for this kind of contract reflects the market’s judgment of a candidate’s chances of victory If a candidate’s contract costs 50 cents, it means, roughly speaking, that the market thinks he has a 50 percent chance of winning. If it costs 80 cents, he has an 80 percent chance of winning, and so on.
The other major kind of IEM contract is set up to predict what percentage of the final popular vote a candidate will get. In this case, the payoffs are determined by the vote percentage: if you’d bought a George W Bush contract in 2000, you would have received 48 cents (he got 48 percent of the vote) when the election was over.
If the IEM’s predictions are accurate, the prices of these different contracts will be close to their true values. In the market to predict election winners, the favorite should always win, and bigger favorites should win by bigger margins. Similarly in the voteshare market, if George W Bush were to end up getting 49 percent of the vote in 2004, then the price of a George W. Bush contract in the run-up to the election should be close to 49 cents.
So how has the IEM done? Well, a study of the IEM’s performance in forty-nine different elections between 1988 and 2000 found that the election-eve prices in the IEM were, on average, off by just 1.37 percent in presidential elections, 3.43 percent in other U.S. elections, and 2.12 percent in foreign elections. (Those numbers are in absolute terms, meaning that the market would have been off by 1.37 percent if, say, it had predicted that Al Gore would get 48.63 percent of the vote when in reality he got 50 percent.) The IEM has generally outperformed the major national polls, and has been more accurate than them even months in advance of the actual election. Over the course of the presidential elections between 1988 and 2000, for instance, 596 different polls were released. Three-fourths of the time, the IEM's market price on the day each of those polls was released was more accurate. Polls tend to be very volatile, with vote shares swinging wildly up and down. But the IEM forecasts, though ever-changing, are considerably less volatile, and tend to change dramatically only in response to new information. That makes them more reliable as forecasts.
What’s especially interesting about this is that the IEM isn’t very big—there have never been more than eight hundred or so traders in the market—and it doesn’t, in any way, reflect the makeup of the electorate as a whole. The vast majority of traders are men, and a disproportionate—though shrinking—number of them are from Iowa. So the people in the market aren’t predicting their own behavior. But their predictions of what the voters of the country will do are better than the predictions you get when you ask the voters themselves what they’re going to do.
The IEM’s success has helped inspire other similar markets, including the Hollywood Stock Exchange (HSX), which allows people to wager on box-office returns, opening-weekend performance, and the Oscars. The HSX enjoyed its most notable success in March of 2000. That was when a team of twelve reporters from The Wall Street Journal assiduously canvassed members of the Academy of Motion Pictures Arts and Sciences in order to find out how they had voted. The Academy was not happy about this. The organization’s president publicly attacked the Journal for trying to scoop us before Oscar night,” and the Academy urged members not to talk to reporters. But with the Journal promising anonymity more than a few people—356, or about 6 percent of all members—disclosed how they had filled out their ballots. The Friday before the ceremony, the Journal published its results, forecasting the winners in the six major Oscar categories—Best Picture, Best Director, Best Actor and Best Actress, Best Supporting Actor and Best Supporting Actress. And when the envelopes were opened, the Journal’s predictions--—-much to the Academy’s dismay—turned out to he pretty much on target, with the paper picking five of the six winners. The HSX, though, had done even better, getting all six of the six right. In 2002, the exchange, perhaps even more impressively picked thirty-five of the eventual forty Oscar nominees.
The HSX’s box-office forecasts are not as impressive or as accurate as the IBM’s election forecasts. But Anita Elberse, a professor of marketing at Harvard Business School, has compared the HSX’s forecasts to other Hollywood prediction tools, and found that the HSX’s closing price the night before a movie opens is the single best available forecast of its weekend box office. As a result, the HSX’s owner Cantor Index Holdings, is now marketing its data to Hollywood studios.
One of the interesting things about markets like the IEM and the HSX is that they work fairly well without much—or any—money at stake. The IEM is a real-money market, but the most you can invest is $500, and the average trader has only $50 at stake. In the HSX, the wagering is done entirely with play money. All the evidence we have suggests that people focus better on a decision when there are financial rewards attached to it (which may help explain why the IEM’s forecasts tend to be more accurate). But David Pennock—a researcher at Overture who has studied these markets closely—found that, especially for active traders in these markets, status and reputation provided incentive enough to encourage a serious investment of time and energy in what is, after all, a game.
As the potential virtues of these decision markets have become obvious, the range of subjects they cover has grown rapidly. At the Newsfutures and TradeSports exchanges, people could bet, in the fall of 2003 on whether or not Kobe Bryant would he convicted of sexual assault, on whether and when weapons of mass destruction would be found in Iraq, and on whether Arid Sharon would remain in power longer than Yassir Arafat. Ely Dahan, a professor at UCLA, has experimented with a classroom-decision market in which students bought and sold securities representing a variety of consumer goods and services, including SUVs, ski resorts, and personal digital assistants. (In a real-life market of this kind, the value of a security might depend on the first-year sales of a particular SUV) The market’s forecasts were eerily similar to the predictions that conventional market research had made (but the classroom research was much cheaper). In the fall of 2003, meanwhile, MITs Technology Review set up a site called Innovation Futures, where people could wager on future technological developments. And Robin Hanson, an economics professor at George Mason University who was one of the first to write about the possibility of using decision markets in myriad contexts, has suggested that such markets could be used to guide scientific research and even as a tool to help governments adopt better policies.
Some of these markets will undoubtedly end up being of little use, either because they’ll fail to attract enough participants to make intelligent forecasts or because they’ll be trying to predict the unpredictable. But given the right conditions and the right problems, a decision market’s fundamental characteristics—diversity, independence, and decentralization—are guaranteed to make for good group decisions. And because such markets represent a relatively simple and quick means of transforming many diverse opinions into a single collective judgment, they have the chance to improve dramatically the way organizations make decisions and think about the future.
In that sense, the most mystifying thing about decision markets is how little interest corporate America has shown in them. Corporate strategy is all about collecting information from many different sources, evaluating the probabilities of potential outcomes, and making decisions in the face of an uncertain future. These are tasks for which decision markets are tailor-made. Yet companies have remained, for the most part, indifferent to this source of potentially excellent information, and have been surprisingly unwilling to improve their decision making by tapping into the collective wisdom of their employees. We’ll look more closely at people’s discomfort with the idea of the wisdom of crowds, but the problem is simple enough: just because collective intelligence is real doesn’t mean that it will be put to good use.
A DECISION MARKET is an elegant and well-designed method for capturing the collective wisdom. But the truth is that the specific method that one uses probably doesn’t matter very much. In this chapter, we’ve looked at a host of different ways of tapping into what a group knows: stock prices, votes, point spreads, pari-mutuel odds, computer algorithms, and futures contracts. Some of these methods seem to work better than others, but in the end there’s nothing about a futures market that makes it inherently smarter than, say, Google or a pari-mutuel pool. These are all attempts to tap into the wisdom of the crowd, and that’s the reason they work The real key it turns out, is not so much perfecting a particular method, but satisfying the conditions—diversity, independence, and decentralization—that a group needs to be smart. As well see in the chapters that follow that’s the hardest, but also perhaps the most interesting, part of the story.
Previous section
We do have a well-established way of knowing what George W. Bush’s chances are: the poll. If you want to know how people are going to vote, you just ask them. Polling is, relatively speaking, accurate. It has a solid methodology behind it, and is statistically rigorous. But there’s reason to wonder if a market such as the betting market—one that allowed the people participating in it to rely on many different kinds of information, including but not limited to polls—might at the very least offer a competitive alternative to Gallup. That’s why the Iowa Electronic Markets (IEM) project was created.
Founded in 1988 and run by the College of Business at the University of Iowa, the IEM features a host of markets designed to predict the outcomes of elections—presidential, congressional, gubernatorial, and foreign. Open to anyone who wants to participate, the IEM allows people to buy and sell futures contracts” based on how they think a given candidate will do in an upcoming election. While the IEM offers many different types of contracts, two are most common. One is designed to predict the winner of an election. In the case of the California recall in 2003, for instance, you could have bought an “Arnold Schwarzenegger to win” contract, which would have paid you $1 when Schwarzenegger won. Had he lost, you would have gotten nothing. The price you pay for this kind of contract reflects the market’s judgment of a candidate’s chances of victory If a candidate’s contract costs 50 cents, it means, roughly speaking, that the market thinks he has a 50 percent chance of winning. If it costs 80 cents, he has an 80 percent chance of winning, and so on.
The other major kind of IEM contract is set up to predict what percentage of the final popular vote a candidate will get. In this case, the payoffs are determined by the vote percentage: if you’d bought a George W Bush contract in 2000, you would have received 48 cents (he got 48 percent of the vote) when the election was over.
If the IEM’s predictions are accurate, the prices of these different contracts will be close to their true values. In the market to predict election winners, the favorite should always win, and bigger favorites should win by bigger margins. Similarly in the voteshare market, if George W Bush were to end up getting 49 percent of the vote in 2004, then the price of a George W. Bush contract in the run-up to the election should be close to 49 cents.
So how has the IEM done? Well, a study of the IEM’s performance in forty-nine different elections between 1988 and 2000 found that the election-eve prices in the IEM were, on average, off by just 1.37 percent in presidential elections, 3.43 percent in other U.S. elections, and 2.12 percent in foreign elections. (Those numbers are in absolute terms, meaning that the market would have been off by 1.37 percent if, say, it had predicted that Al Gore would get 48.63 percent of the vote when in reality he got 50 percent.) The IEM has generally outperformed the major national polls, and has been more accurate than them even months in advance of the actual election. Over the course of the presidential elections between 1988 and 2000, for instance, 596 different polls were released. Three-fourths of the time, the IEM's market price on the day each of those polls was released was more accurate. Polls tend to be very volatile, with vote shares swinging wildly up and down. But the IEM forecasts, though ever-changing, are considerably less volatile, and tend to change dramatically only in response to new information. That makes them more reliable as forecasts.
What’s especially interesting about this is that the IEM isn’t very big—there have never been more than eight hundred or so traders in the market—and it doesn’t, in any way, reflect the makeup of the electorate as a whole. The vast majority of traders are men, and a disproportionate—though shrinking—number of them are from Iowa. So the people in the market aren’t predicting their own behavior. But their predictions of what the voters of the country will do are better than the predictions you get when you ask the voters themselves what they’re going to do.
The IEM’s success has helped inspire other similar markets, including the Hollywood Stock Exchange (HSX), which allows people to wager on box-office returns, opening-weekend performance, and the Oscars. The HSX enjoyed its most notable success in March of 2000. That was when a team of twelve reporters from The Wall Street Journal assiduously canvassed members of the Academy of Motion Pictures Arts and Sciences in order to find out how they had voted. The Academy was not happy about this. The organization’s president publicly attacked the Journal for trying to scoop us before Oscar night,” and the Academy urged members not to talk to reporters. But with the Journal promising anonymity more than a few people—356, or about 6 percent of all members—disclosed how they had filled out their ballots. The Friday before the ceremony, the Journal published its results, forecasting the winners in the six major Oscar categories—Best Picture, Best Director, Best Actor and Best Actress, Best Supporting Actor and Best Supporting Actress. And when the envelopes were opened, the Journal’s predictions--—-much to the Academy’s dismay—turned out to he pretty much on target, with the paper picking five of the six winners. The HSX, though, had done even better, getting all six of the six right. In 2002, the exchange, perhaps even more impressively picked thirty-five of the eventual forty Oscar nominees.
The HSX’s box-office forecasts are not as impressive or as accurate as the IBM’s election forecasts. But Anita Elberse, a professor of marketing at Harvard Business School, has compared the HSX’s forecasts to other Hollywood prediction tools, and found that the HSX’s closing price the night before a movie opens is the single best available forecast of its weekend box office. As a result, the HSX’s owner Cantor Index Holdings, is now marketing its data to Hollywood studios.
One of the interesting things about markets like the IEM and the HSX is that they work fairly well without much—or any—money at stake. The IEM is a real-money market, but the most you can invest is $500, and the average trader has only $50 at stake. In the HSX, the wagering is done entirely with play money. All the evidence we have suggests that people focus better on a decision when there are financial rewards attached to it (which may help explain why the IEM’s forecasts tend to be more accurate). But David Pennock—a researcher at Overture who has studied these markets closely—found that, especially for active traders in these markets, status and reputation provided incentive enough to encourage a serious investment of time and energy in what is, after all, a game.
As the potential virtues of these decision markets have become obvious, the range of subjects they cover has grown rapidly. At the Newsfutures and TradeSports exchanges, people could bet, in the fall of 2003 on whether or not Kobe Bryant would he convicted of sexual assault, on whether and when weapons of mass destruction would be found in Iraq, and on whether Arid Sharon would remain in power longer than Yassir Arafat. Ely Dahan, a professor at UCLA, has experimented with a classroom-decision market in which students bought and sold securities representing a variety of consumer goods and services, including SUVs, ski resorts, and personal digital assistants. (In a real-life market of this kind, the value of a security might depend on the first-year sales of a particular SUV) The market’s forecasts were eerily similar to the predictions that conventional market research had made (but the classroom research was much cheaper). In the fall of 2003, meanwhile, MITs Technology Review set up a site called Innovation Futures, where people could wager on future technological developments. And Robin Hanson, an economics professor at George Mason University who was one of the first to write about the possibility of using decision markets in myriad contexts, has suggested that such markets could be used to guide scientific research and even as a tool to help governments adopt better policies.
Some of these markets will undoubtedly end up being of little use, either because they’ll fail to attract enough participants to make intelligent forecasts or because they’ll be trying to predict the unpredictable. But given the right conditions and the right problems, a decision market’s fundamental characteristics—diversity, independence, and decentralization—are guaranteed to make for good group decisions. And because such markets represent a relatively simple and quick means of transforming many diverse opinions into a single collective judgment, they have the chance to improve dramatically the way organizations make decisions and think about the future.
In that sense, the most mystifying thing about decision markets is how little interest corporate America has shown in them. Corporate strategy is all about collecting information from many different sources, evaluating the probabilities of potential outcomes, and making decisions in the face of an uncertain future. These are tasks for which decision markets are tailor-made. Yet companies have remained, for the most part, indifferent to this source of potentially excellent information, and have been surprisingly unwilling to improve their decision making by tapping into the collective wisdom of their employees. We’ll look more closely at people’s discomfort with the idea of the wisdom of crowds, but the problem is simple enough: just because collective intelligence is real doesn’t mean that it will be put to good use.
A DECISION MARKET is an elegant and well-designed method for capturing the collective wisdom. But the truth is that the specific method that one uses probably doesn’t matter very much. In this chapter, we’ve looked at a host of different ways of tapping into what a group knows: stock prices, votes, point spreads, pari-mutuel odds, computer algorithms, and futures contracts. Some of these methods seem to work better than others, but in the end there’s nothing about a futures market that makes it inherently smarter than, say, Google or a pari-mutuel pool. These are all attempts to tap into the wisdom of the crowd, and that’s the reason they work The real key it turns out, is not so much perfecting a particular method, but satisfying the conditions—diversity, independence, and decentralization—that a group needs to be smart. As well see in the chapters that follow that’s the hardest, but also perhaps the most interesting, part of the story.
Previous section
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