from Hacker News

Design Patterns in Googles Prediction Market on Google Cloud

by aleyan on 12/20/21, 8:13 PM with 7 comments

• by ddp26 on 12/21/21, 9:06 PM

  See also Scott Alexander's comments on this, https://astralcodexten.substack.com/p/mantic-monday-let-me-g...

  Edit: it's a separate HN post, https://news.ycombinator.com/item?id=29642210

• by squidproquo on 12/21/21, 9:14 PM

  Interesting, I built an app that does something similar with respect to converting forecasts to trades, allowing forecasters to trade their predictions (in hidden market) for points (not real money).

  https://www.unitarity.com/app/

• by PeterCorless on 12/21/21, 9:34 PM

  The problem with prediction markets is that information is not equally distributed amongst participants, and vote "weight" needs to be adjusted based upon proximity to information as well as rational, objective decision-making criteria.

  Otherwise you can run into self-fulfilling prophesies, where fascination with gloom-and-doom scenarios become rampant. Imagine the "QAnon-ization" of a "predictive market." Or, conversely, the positive feedback loop that would result in a tulipmania, or the kind of crypto-coin goldbugs' "Going to the moon."

  In other words, you have to account for bad faith actors in a predictive market, manipulations to it, and inherent biases.

  "Incentivizing" the "right" behavior doesn't work if people are unswayable from preset biases and presumptions.

  Recall the debacle with Policy Analysis Market (PAM) from 2003:

  • News: https://www.nbcnews.com/id/wbna3072985

  • Paper on the PAM: https://watermark.silverchair.com/itgg.2007.2.3.73.pdf?token...

  (EDIT) Last note: Predictive markets tend to fail worst when trying to model for stochastic events — like terrorist attacks — which are inherently probabilistic, but not predictable. I use the analogy: "Monday, Tuesday, Wednesday, BANANA!" How would you have predicated that based on prior data?