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Design Patterns in Googles Prediction Market on Google Cloud

64 pointsby aleyanover 3 years ago

3 comments

ddp26over 3 years ago
See also Scott Alexander&#x27;s comments on this, <a href="https:&#x2F;&#x2F;astralcodexten.substack.com&#x2F;p&#x2F;mantic-monday-let-me-google-that" rel="nofollow">https:&#x2F;&#x2F;astralcodexten.substack.com&#x2F;p&#x2F;mantic-monday-let-me-g...</a><p>Edit: it&#x27;s a separate HN post, <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=29642210" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=29642210</a>
squidproquoover 3 years ago
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).<p><a href="https:&#x2F;&#x2F;www.unitarity.com&#x2F;app&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.unitarity.com&#x2F;app&#x2F;</a>
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PeterCorlessover 3 years ago
The problem with prediction markets is that information is not equally distributed amongst participants, and vote &quot;weight&quot; needs to be adjusted based upon proximity to information as well as rational, objective decision-making criteria.<p>Otherwise you can run into self-fulfilling prophesies, where fascination with gloom-and-doom scenarios become rampant. Imagine the &quot;QAnon-ization&quot; of a &quot;predictive market.&quot; Or, conversely, the positive feedback loop that would result in a tulipmania, or the kind of crypto-coin goldbugs&#x27; &quot;Going to the moon.&quot;<p>In other words, you have to account for bad faith actors in a predictive market, manipulations to it, and inherent biases.<p>&quot;Incentivizing&quot; the &quot;right&quot; behavior doesn&#x27;t work if people are unswayable from preset biases and presumptions.<p>Recall the debacle with Policy Analysis Market (PAM) from 2003:<p>• News: <a href="https:&#x2F;&#x2F;www.nbcnews.com&#x2F;id&#x2F;wbna3072985" rel="nofollow">https:&#x2F;&#x2F;www.nbcnews.com&#x2F;id&#x2F;wbna3072985</a><p>• Paper on the PAM: <a href="https:&#x2F;&#x2F;watermark.silverchair.com&#x2F;itgg.2007.2.3.73.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAq0wggKpBgkqhkiG9w0BBwagggKaMIIClgIBADCCAo8GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMYjCTOo3FrUKEXmzHAgEQgIICYOtE0d1Jd_0gJlU8lq_Th31X47PkUByxoayFQgXG30ksSJ8OmefR2ywo7wBDmgOv-Evs83a-O-L6yj7gWle17ocqSMCWRsXMt1dYsickk58Pbkf5rC8brpJRwt08PQ5FqozOAioX5P9P8NzdIPPvElNiElhlN_JA65JWVaWrQkFwPeTqMVc9jwyZPAgVjV4RKcKkw-bZ8P-vhnVIX987NZebmB2hQYjAWnwjv2dJhtne9Ke-HBUsiBIA5PUxDnBG8zZYanTZhvrZ12c9gxN7rX2o2yzOqV60OdX6pQPZwIG0_WguhFCygXSFYY8q3zwMlltQnnwEElyRtBMB1wbUFV6haeA77CXQdqt-kX2LDl7VlNlqRE_ct4OujuV8-iv5LhNF6KBok5DwdIL6BhmzG8QNnwMtnm8Xrhf6QNXOLYjeS6pD8KerlcNjZDAl2VjRwEgXNOopMGwDb3xW2sYl4kG4WWn3muob731mYsTnfPe1kc0SPsruMUiPlb_udhDcTdid_vPZHQ4zT6hF3M6xabs7NJtr51Vwfpx7gLIXQjDStRbFtSOqEDv7iFULE9pgYKtiSfa299Iub07sK9oOcQSTA4peXO9J6sioyT6e0jikc778JQaW7YId5LhytlzQRSwQKraiGKCdxwcmJPnKE6JTex4pARbAtOUfq10E_-5j1RGNvuFZRp-gwZDkRZIB1-b2CwvK6aBGFmnEkcx_KwkJMoUVDQY6Fkh4pk_jkw1Chd6rXoSMPn5nndhqnocOZ_DdXpml_Key7BjR79CKEMz92wcpNo5PUCPYqIFCdFFQ" rel="nofollow">https:&#x2F;&#x2F;watermark.silverchair.com&#x2F;itgg.2007.2.3.73.pdf?token...</a><p>(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: &quot;Monday, Tuesday, Wednesday, BANANA!&quot; How would you have predicated that based on prior data?
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