I’m a TED Talk junkie. In case you’re not familiar with TED, it’s an annual event composed of 5-15 minute “talks” by some of the world’s leading thinkers. Notable past TED speakers include Bill Clinton, Malcolm Gladwell, Billy Graham, Jeffrey Katzenberg and Bill Gates.
I just watched an intriguing TED Talk by Esther Duflo about how we can better address poverty in the third world. She contends that countries and organizations aren’t doing everything they can to fight disease and promote education in poor countries because they just aren’t sure of the best way to go about it.
As an example, we know that bednets reduce the spread of maria, but what’s the best way to distribute them? Should you ask people to pay for them so they’re more likely to value them? Are people as likely to use free bednets and subsidized ones?
These are difficult questions, or at least that’s how they appear at first blush. Ms. Duflo argues that they shouldn’t be. She encourages us to invoke the scientific method to answer these questions, just as we would to answer first-world medical questions like which drug is most effective against a given ailment. She can’t understand why humanitarian organizations haven’t already applied this powerful method, so she takes matters into her own hands and runs some experiments. She finds, for example, that giving away bednets for free doesn’t reduce people’s likelihood to use or repurchase them, and that bribing mothers with a kilo of lentils is an effective (and surprisingly inexpensive) way to encourage mothers to get their children immunized.
If the scientific method can be applied to goals as important as ending world poverty and disease, why haven’t governments already applied it? Why is the US government willing to send billions of dollars in aid overseas, but not run these relatively low-cost experiments to see how these billions can be most effectively deployed?
The answer, I believe, is fear of failure.
Most humans are loathe to experiment because most experiments are destined to fail. We’re astonished by those exceptional characters who aren’t afraid of failure, like Thomas Edison, who created over 3,000 different prototypes of the light bulb before he stumbled upon one that actually worked. (In Edison’s view, he was ecstatic to have discovered over 3,000 ways that an electric light bulb would not work… he knew success was near.)
If the average person is afraid of failure, you can bet that the average politician is even more so. There’s always an election around the corner and voters have little tolerance for poor decisions. No wonder politicians like to play it safe.
So what’s the answer? Brave and passionate souls like Mr. Duflo are a great start. She’s willing to experiment and fail where the governments and donors who sponsor her efforts are not. But she has limited time and resources, and her experiments require a a good deal of both.
I’d like to propose another solution: a prediction market.
A prediction market is a “virtual stock market” where traders can buy and sell shares in potential future outcomes. Traders put their money where their mouth is, investing more cash in those outcomes that they feel are more likely to occur. If they invest wisely, they can make a return on their investments. Yes, I know it sounds a bit like horse betting or stock market speculating, but bear with me for a moment. Prediction markets can help save lives.
You see, any economist will tell you that markets are not only places of exchange, but also excellent information gathering tools. Markets soak up opinions and data from traders and reflect that information, quickly and accurately, in a price. In a prediction market, prices reflect the market’s consensus probability of a certain outcome occurring. And the market is usually right.
I’d like to propose an alternative to Ms. Duflo’s experiments: an inexpensive, quick and accurate prediction market specifically designed to answer questions of interest to inform public policy, particularly in relation to humanitarian aid.
Public policy prediction markets aren’t a new idea. In fact, DARPA created one in 2001 called PAM (Policy Analysis Market) that was focused on predicting future outcomes in the Middle East. PAM allowed traders to bet real money on such events as coups d’état, assassinations, and terrorist attacks. It probably would have worked, but Senators Byron L. Dorgan (D-ND) and Ron Wyden (D-OR) denounced the idea, stating, “The idea of a federal betting parlor on atrocities and terrorism is ridiculous and it’s grotesque.” Funding was quickly withdrawn.
It’s a shame that PAM was shut down so quickly, because despite the understandable moral objections that the Senators raised, it probably would have been quite accurate in its predictions. Prediction markets have a great track record for assigning accurate probabilities to future outcomes. A few examples:
- The Iowa Electronic Markets has outperformed political polls when it comes to prediction election winners 74% of the time since 1988
- The Hollywood Stock Exchange can predict movie box office receipts with a .945 correlation (a 1.0 correlation is perfection)
- An internal prediction market setup by Google was found to assign near perfect probabilities to various business outcomes of interest to the company’s management
We can see why DARPA was so anxious to launch a prediction market focused on the future outcomes of greatest concerns to Americans at that point in time. They work.
The moral objections raised by Senators Dorgan and Wyden are understandable. What they failed to recognize though is that PAM could have just changed its scope to allow traders to bet on other topics of interest to policymakers. For example, it could have included markets like:
- What types of humanitarian foreign aid (food, water, logistics, medicine, etc.) will have the greatest impact on Haitian earthquake victims, as measured by lives saved per dollar invested?
- Which country will be the next to default on its foreign loan repayment obligations to the IMF?
- What will be the death toll of US troops in Iraq for the last 6 months of this year?
It’s not hard to see how the answers to these questions could be used to inform sound public policy, especially when it comes to humanitarian endeavors. If prediction markets be used to predict election winners, movie box of receipts, or how many Gmail subscribers there will be in a few months, why not use them to save lives?