There are surely many possible ways to design a system of government. But what about a government in which many important decisions — from legislation to administrative rule making to court decisions — are made through prediction markets?
Such is the long-shot thought experiment behind Predictocracy, Michael Abramowicz’s intriguing field guide to the wide and sometimes wild world of prediction-market applications for government and business.
At a basic level, prediction markets operate somewhat like stock markets. In the stock market, the weight of investors’ bets drive a company’s share prices up or down. In a prediction market, the weight of investors’ bets indicate the likelihood of a given event outcome. For example, at prediction markets Web site Intrade, you can bet on the likelihood of the U.S. taking military action against North Korea (currently low) or unemployment hitting 10 percent by December (currently high).
The idea is rather than taking a survey to get the average opinion and relying on the wisdom of crowds, the prediction market identifies the wisdom in crowds because the market only attracts participants who feel confident enough in their predictions that they are willing to put money on the line. Prediction markets give participants a financial incentive to get things right.
“We all know intuitively that often the average person doesn’t know much about something,” explained Abramowicz, a professor of law at George Washington University. “And there are cases where you want to know what experts think, and who is most genuinely confident.”
Prediction markets gained some popularity as a tool for forecasting the presidential election in 2008. But when government officials proposed using such a market — a DARPA program to forecast terrorist activities in the Middle East — negative publicity forced them to abandon the project.
Still, one day a few years back, Abramowicz, skeptical about the efficiency of the current legal system, began wondering whether it would be possible to imagine a market mechanism for conducting adjudication. “I thought about it and said, ‘No, you couldn’t do it,'” he said. “And then the next morning, I woke up and said, ‘Yeah, you could.'”
When he woke up, Abramowicz had an idea: If you set up a market so that individuals had to announce a price and then make a commitment to either buy or sell a contract at that price, all participants would have an incentive to pick a price that they thought others would also announce. In such a way, you could establish a focal point coordination process, a very efficient way to aggregate collective judgment. (This is one of the many permutations of the basic prediction market idea throughout the book.)
Regarding courts, Predictocracy argues that one of the main reasons why litigation is so costly is that it’s often expensive and time consuming to properly value and negotiate lawsuits. “Clients,” he wrote, “spend too much on lawyers who gather information and advocate for them.”
So Abramowicz argues for a subsidized market where people could place bets on what a randomly selected judge or jury would decide. Market participants (probably a small group with enough legal experience to make informed decisions that would allow them to make money in such a market) could see all the evidence, even perhaps watch videotaped witnesses and cross-examinations over the Internet to make their assessments. Ultimately, the market would come to a prediction on the likelihood of a case succeeding. This prediction could help litigants to decide if they really wanted to go forward, or could even be linked to a formula for assessing civil damages.
Another potential advantage of such a system is that it could likely reduce idiosyncratic judgments, which in turn might reduce the likelihood of frivolous lawsuits. As the current legal system stands, judge and jury assignments can sometimes be a roll of the dice for litigants, which can make it worth gambling if the payoff is big enough. But if results were more predictable, litigants might be more likely to settle in advance.
In the realm of lawmaking, Abramowicz makes a case for something he calls predictive cost-benefit analysis. Say, for example, that you are trying to assess a budget bill. You could, for example, take a random selection of lines from that budget bill and have people place bets on whether a randomly selected member of Congress, 10 years hence, would say that the money was well-spent.
One of the main advantages of this approach is that it takes the politics and ideology out of the process because the predictions would be based on what a randomly selected lawmaker (who could be either a Democrat or a Republican) would think. Such an approach could make lawmaking less partisan and more consistent over time.
“For $10 (million) or $20 million, one could have a powerful tool for rating budget items,” he said. “Right now, all we have are sound bites about bridges to nowhere.”
In the area of regulation, Abramowicz argues that prediction markets can offer a third way between the rule-based command-and-control approach often favored by Democrats and the leave-it-up-to-the-market approach favored by Republicans. For example, third parties could place bets on whether or not individual workplaces would meet mandated workplace safety targets, sending signals as to whether workplace safety standards are up to the mark. Government then could assess fines or set up a workplace safety credit-trading scheme based on the prediction market assessments.
“It’s interesting that the arguments conservatives have generally put forth to something like OSHA is self-regulation,” Abramowicz said. “Simply letting market actors choose on their own could be right, but it would also be that workers have poor information, and that could lead to poor results. What conservatives haven’t put forward are mechanisms for achieving a given level of safety.”
Abramowicz is also intrigued by the idea of linked prediction markets. For example, you could have an election prediction market that was linked to an economy prediction market that was in turn linked to an energy price prediction market, and so on, since the price of energy affects the economy, and the economy affects elections. “You could break problems down so more people could work on discrete problems,” Abramowicz said. “What would propagate up would be very powerful.”
Though there are many more proposals and applications in the book, the whole idea of prediction markets begs several questions.
First and foremost: Will prediction markets actually be accurate? After all, the recent stock market gyrations might give some pause to putting too much faith in any kind of market.
Abramowicz remains optimistic. “There’s much less likely to be a systematic problem in prediction markets because arbitrage is much easier,” he said. “In the history of prediction markets, it’s hard to find times when the value of the market was driven by psychological factors different from reality.”
Moreover, prediction markets don’t necessarily need to be binding. They could just be used as a tool to aggregate and evaluate information, a kind of “weak crystal ball” that could help lawmakers make more informed decisions.
OK. But government by prediction markets? How democratic is that?
“If one defines democracy as what the masses on average prefer, then for the government to make decision by prediction market would be a mistake,” Abramowicz said. “But if we think of democracy as a set of institutions that aggregate our collective preferences given widespread individual ignorance about many issues, then prediction markets perform a lot better.”
And wouldn’t people with vested interests have an incentive to skew the market? Predictocracy argues that while this is certainly a risk, this is probably overweighed by the desire of participants to get the prediction right and make the most money.
Though prediction markets are still a fringe idea for policymaking, they are gaining traction in the private sector as more and more big companies experiment with them. For example, a number of companies have set up internal prediction markets for specific sales or stock price targets to help them to plan better.
Abramowicz believes prediction markets will continue to prove their worth and maybe some of the experiments will bubble over into the public sector on a small-scale test basis. But he’s not betting on a real predictocracy anytime soon.
“The point of radical ideas is not to advocate those ideas,” Abramowicz said. “It’s to illustrate how a mechanism could do things you wouldn’t think it could do, and once you recognize, wow, it can do all these things, you might think about smaller things it could actually do.”
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