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)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 SmithSocial 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 Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.