Seer
  • ๐Ÿง™Introduction to Seer
  • Prediction Market Basics
    • ๐Ÿช„Creating Tokens
    • ๐Ÿ”„Trading Tokens
    • ๐Ÿ”ฅRedeeming Tokens
    • โž•Scalar Markets
  • ๐Ÿ˜ตWhy did previous prediction markets fail?
    • โ—พA method other than a prediction market produces the best predictions
    • โ‰๏ธAn article shows a method over performing prediction market
    • ๐Ÿ”ŽIt's not ethical
    • ๐ŸฆRegulatory Attacks
    • ๐Ÿ’งLack of Liquidity
  • ๐ŸชกSeer Solution
    • ๐Ÿ”˜Seer DAO
    • ๐Ÿ‡ง๐Ÿ‡ฎRetroactive Public Good Funding
    • ๐Ÿ‘จโ€๐Ÿ‘จโ€๐Ÿ‘ฆโ€๐Ÿ‘ฆPrediction Market Actors
    • ๐ŸฅšThe Chicken and the egg problem
    • ๐Ÿ’ฐToken Incentives
    • โ„น๏ธInformation Seekers
    • ๐Ÿ“ˆExchange Integration
    • ๐Ÿ’ฆLiquidity Management
    • ๐Ÿ”’Safe Liquidity Provision
    • โžก๏ธPractical Implementation = AMM + Auctions
    • ๐Ÿ’กReasoning
    • โš•๏ธCapital Efficency
  • โš™๏ธApplications
    • ๐Ÿ•ด๏ธPolitical Predictions
    • ๐ŸฒFood and Drugs Market
    • ๐ŸŒMacro Studies
    • โบ๏ธMicro Recommendation
    • ๐Ÿ’ขFutarchy
    • ๐Ÿค–AI Markets
  • ๐ŸคConclusion
  • ๐Ÿ“„Reference
  • App
Powered by GitBook
On this page
  1. Applications

Macro Studies

We would take a cohort of patients and split them into a test group (which would take the food, drug or receive the medical procedure) and a control group (which would be given a placebo). Professionals or even the general public (note that this is not a problem for unskilled people to participate, by adding noise and losing money on those markets, they make professional forecasting more profitable leading to better results) can bet on the outcome metrics of the study.

Outcome metric can be:

  • Difference of survival rate between the test group and the control group.

  • Reported quality of life between the test group and the control group.

In the short term, we expect those data points to be used in order to decide on which kind of treatment to focus research on and by sophisticated (i.e. patient doing their own research when different treatments are proposed) to make decisions which concern them. In the medium term, those data points could be reported in the literature and be used as insight by medical professionals when it comes to proposing treatments. In the long term, we could even have state health authorities adopting prediction markets in their process of approval of drugs and medical procedures (requiring higher predicted survival rate and quality of life for patients undertaking the procedures).

In some specific cases, it may be very difficult to have a control group (as patients wouldnโ€™t accept an important life decision to be just taken at random depending on which group they end up in). In this case, patients would choose whether or not to undertake the procedure and the metric being predicted would be their rate of regret. This would be the case for predicting the outcome of gender dysphoria treatment. This would be an extremely interesting market to start with as this is such a polarising topic that itโ€™s impossible to distinguish between medical and ideological information on the topic. And the polarising nature of the problem would draw a large amount of attention (thus ideological betting) making it an interesting market for people with serious insight on the topic.

PreviousFood and Drugs MarketNextMicro Recommendation

Last updated 1 year ago

โš™๏ธ
๐ŸŒ