Why don't more businesses use prediction markets? - Marginal REVOLUTION
Tyler Cowenmarginalrevolution.com
Saved by King
Why don't more businesses use prediction markets? - Marginal REVOLUTION
In a prediction market, it is relatively more difficult: maybe you are trading with someone who has natural exposure to a Russian invasion and is just looking for a hedge, but more likely your counterparty is, like, a guy who speaks Russian and is in dozens of Telegram groups waiting for just the right moment to trade against your resting order.
Tanner Hoke • Let there be flow - Tanner Hoke
As a good rule of thumb, proprietary technology must be at least 10 times better than its closest substitute in some important dimension to lead to a real monopolistic advantage. Anything less than an order of magnitude better will probably be perceived as a marginal improvement and will be hard to sell, especially in an already crowded market.
Peter Thiel, Blake Masters • Zero to One
The Promise of Prediction Markets | Science
science.org
Decision Markets for Policy Advice
The text discusses decision markets as innovative tools for improving policy decision-making by aggregating information about the expected consequences of various policy options, highlighting their advantages, challenges, and a case study on the Policy Analysis Market.
mason.gmu.edu
A preference for unbiased predictions is justified if all errors of prediction are treated alike, regardless of their direction. But there are situations in which one type of error is much worse than another. When a venture capitalist looks for “the next big thing,” the risk of missing the next Google or Facebook is far more important than the risk
... See moreDaniel Kahneman • Thinking, Fast and Slow
Statement on Prediction Markets
The paper advocates for reduced regulatory barriers to prediction markets, proposing a safe harbor for small stakes markets to enhance decision-making and information gathering in both public and private sectors.
users.nber.orgBecause of the possible destabilizing effects of taking inaccurate predictive data too seriously, it is sometimes advantageous to omit prediction entirely, relying wholly on feedback, unless the quality of the predictions is high.