By Robin Hanson
When I first got into prediction markets twenty five years ago, I called them “idea futures”, and I focused on using them to reform how we deal with controversies in science and academia (see here, here, here, here). Lately I’ve focused on what I see as the much higher value application of advising decisions and reforming governance (see here, here, here, here). I’ve also talked a lot lately about what I see as the main social functions of academia (see here, here, here, here). Since prediction markets don’t much help to achieve these functions, I’m not optimistic about the demand for using prediction markets to reform academia.
But periodically people do consider using prediction markets to reform academia, as did Andrew Gelman a few months ago. And a few days ago Scott Alexander, who I once praised for his understanding of prediction markets, posted a utopian proposal for using prediction markets to reform academia. These discussions suggest that I revisit the issue of how one might use prediction markets to reform academia, if in fact enough people cared enough about gaining accurate academic beliefs. So let me start by summarizing and critiquing Alexander’s proposal.
Alexander proposes prediction markets where anyone can post any “theory” broadly conceived, like “grapes cure cancer.” (Key quotes below.) Winning payouts in such market suffer a roughly 10% tax to fund experiments to test their theories, and in addition some such markets are subsidized by science patron orgs like the NSF. Bettors in each market vote on representatives who then negotiate to pick someone to pay to test the bet-on theory. This tester, who must not have a strong position on the subject, publishes a detailed test design, at which point bettors could leave the market and avoid the test tax. “Everyone in the field” must make a public prediction on the test. Then the test is done, winners paid, and a new market set up for a new test of the same question. Somewhere along the line private hedge funds would also pay for academic work in order to learn where they should bet.
That was the summary; here are some critiques…
Read the full post at Overcoming Bias.