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 better than pollsters, journalists, domain experts, superforecasters?

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 (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-to distant future -- touching on everything from corporate decisions and scientific replication to trends like AI, DeSci, futarchy/ governance, and more?

Our special expert guests are Alex Tabarrok, 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.

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

The Use of Knowledge in Society • by Friedrich Hayek (American Economic Review, 1945) Everything is priced in • by rsd99 (r/wallstreetbets, 2019) Idea Futures (aka prediction markets, information markets) • by Robin Hanson (1996) Auctions: The Social Construction of Value • by Charles Smith Social value of public information • by Stephen Morris and Hyun Song Shin (American Economic Review, December 2002) Using prediction markets to estimate the reproducibility of scientific research • by Anna Dreber, Thomas Pfeiffer, Johan Almenberg, Siri Isaksson, Brad Wilson, Yiling Chen, Brian Nosek, and Magnus Johannesson (Proceedings of the National Academy of Sciences (November 2015) A solution to the single-question crowd wisdom problem • by Dražen Prelec, Sebastian Seung, and John McCoy (Nature, January 2017) Targeting high ability entrepreneurs using community information: Mechanism design in the field • by Reshmaan Hussam, Natalia Rigol, and Benjamin Roth (American Economic Review, March 2022) Information aggregation mechanisms: concept, design, and implementation for a sales forecasting problem • by Charles Plott and Kay-Yut Chen, Hewlett Packard Laboratories (March 2002) If I had a million • [on deciding to dump the CEO or not] by Robin Hanson (2008) Futarchy: Vote values, but bet beliefs • by Robin Hanson (2013) From prediction markets to info finance • by Vitalik Buterin (November 2024) Composability is innovation • by Linda Xie (June 2021) Composability is to software as compounding interest is to finance • by Chris Dixon (October 2021) resources & research on DAOs • , a16z crypto

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