Can we still accurately model elections in such chaotic times? Are prediction markets the future of news? Nate Silver thinks so - Nate is a renowned election analyst, author and former professional poker player. He's the founder of FiveThirtyEight, whose statistical models revolutionized election forecasting, earning him national acclaim. His two books "On The Edge: The Art of Risking Everything." and "Signal in Noise" are inspirations for today's Win-Win conversation with Liv and Igor. We discuss polling accuracy, the importance of prediction markets like Polymarket, poker thinking, solutions to political polarization, and of course his latest predictions on Trump vs Kamala. We also hear his theory on "The River" and "The Village" communities, defined by their opposing perspectives on risk - and how these contrasting worldviews shape approaches to everything from public health policy to technology. Chapters

00:01:34 - Predicting Elections 00:06:49 - Trump Assassination Attempt 00:11:45 - Prediction Markets 00:21:13 - Nate’s New Book on Risk 00:25:53 - Institutional screwups on COVID 00:33:30 - Why are People so Averse to Probabilities? 00:35:27 - Silicon Valley’s Blind Spots 00:40:43 - Excessively Risky Behavior 00:44:29 - Regulations 00:48:55 - Finding Common Ground 01:00:18 - Alternative Voting Structures 01:06:50 - Signal vs Noise in the AI age 01:12:15 - Advice for Sharpening Your Models 01:22:25 - Nate’s Maxims For A Win-Win Future Links: ♾️ Nate’s Blog: https://www.natesilver.net/ ♾️ Nate’s Twitter: https://x.com/NateSilver538/ Credits: ♾️ Hosted and Produced by Liv Boeree and Igor Kurganov ♾️ Post-Production by Ryan Kessler The Win-Win Podcast: Poker champion Liv Boeree takes to the interview chair to tease apart the complexities of one of the most fundamental parts of human nature: competition. Liv is joined by top philosophers, gamers, artists, technologists, CEOs, scientists, athletes and more to understand how competition manifests in their world, and how to change seemingly win-lose games into Win-Wins.

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