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cover of episode Prediction Markets and Beyond

Prediction Markets and Beyond

2024/11/22
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AI Deep Dive AI Chapters Transcript
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A
Alex Taborrok
S
Scott Kominers
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Alex Taborrok认为预测市场是一种有效的预测工具,其核心在于汇集市场参与者的信息,并通过价格发现机制得出预测结果。其优势在于优于统计模型和民意调查,并且能够通过投注机制不断逼近真实情况。Scott Kominers补充说明预测市场是一种信息聚合机制,其价格反映了参与者对事件概率的估计,市场规模会影响信息收集的激励。Sonal Chokshi则强调了预测市场中价格机制的重要性,即参与者需要投入资金,从而使其预测更接近真实情况。

Deep Dive

Chapters
The episode begins with a discussion on the effectiveness of prediction markets compared to polls, particularly in the context of recent elections. The guests explore how prediction markets aggregate dispersed information more accurately than polls, and how incentives in prediction markets push the market closer to the truth.
  • Prediction markets tend to be more accurate than polls or complicated statistical models.
  • Incentives in prediction markets encourage participants to reveal their true beliefs, pushing the market closer to the truth.
  • The recent election saw prediction markets predicting outcomes more accurately than public polls.

Shownotes Transcript

with @atabarrok @skominers @smc90

We've heard a lot about the premise and the promise of prediction markets for a long time, but they finally hit the main stage with the most recent election. So what worked (and didn't) this time? Are they really better than pollsters, is polling dead? 

So in this conversation, we tease apart the hype from the reality of prediction markets, from the recent election to market foundations... going more deeply into the how, why, and where these markets work. We also discuss the design challenges and opportunities (including implications for builders throughout). And we also cover other information aggregation mechanisms -- from peer prediction to others -- given that prediction markets are part of a broader category of information-elicitation and information-aggregation mechanisms. Where do domain experts, superforecasters, pollsters, and journalists come in (and out)? Where do (and don't) blockchain and crypto technologies come in -- and what specific features (decentralization, transparency, real-time, open source, etc.) matter most, and in what contexts?  Finally, we discuss applications for prediction and decision markets -- things we could do right away to in the near-future to sci-fi -- touching on trends like futarchy, AI entering the market, DeSci, and more.  

Our special expert guests are Alex Taborrok, professor of economics at George Mason University and Chair in Economics at the Mercatus Center; and Scott Duke Kominers, research partner at a16z crypto, and professor at Harvard Business School  -- both in conversation with Sonal Chokshi. 

As a reminder: None of the following should be taken as business, investment, legal, or tax advice; please see a16z.com/disclosures for more important information. 

RESOURCES(from links to research mentioned to more on the topics discussed)