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I love Bayesian modeling. Not only because it allows me to model interesting phenomena and learn about the world I live in. But because it’s part of a broader epistemological framework that confronts me with deep questions — how do you make decisions under uncertainty? How do you communicate risk and uncertainty? What does being rational even mean?

Thankfully, Gerd Gigerenzer is there to help us navigate these fascinating topics. Gerd is the Director of the Harding Center for Risk Literacy of the University of Potsdam, Germany.

Also Director emeritus at the Max Planck Institute for Human Development, he is a former Professor of Psychology at the University of Chicago and Distinguished Visiting Professor at the School of Law of the University of Virginia.

Gerd has written numerous awarded articles and books, including Risk Savvy, Simple Heuristics That Make Us Smart, Rationality for Mortals, and How to Stay Smart in a Smart World.

As you’ll hear, Gerd has trained U.S. federal judges, German physicians, and top managers to make better decisions under uncertainty.

But Gerd is also a banjo player, has won a medal in Judo, and loves scuba diving, skiing, and, above all, reading.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau and Luis Fonseca.

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Links from the show:

Abstract

by Christoph Bamberg

In this episode, we have no other than Gerd Gigerenzer on the show, an expert in decision making, rationality and communicating risk and probabilities.

Gerd is a trained psychologist and worked at a number of distinguished institutes like the Max Planck Institute for Human Development in Berlin or the University of Chicago. He is director of the Harding Center for Risk Literacy in Potsdam.

One of his many topics of study are heuristics, a term often misunderstood, as he explains. We talk about the role of heuristics in a world of uncertainty, how it interacts with analysis and how it relates to intuition.

Another major topic of his work and this episode are natural frequencies and how they are a more natural way than conditional probabilities to express information such as the probability of having cancer after a positive screening.

Gerd studied the usefulness of natural frequencies in practice and contributed to them being taught in high school in Bavaria, Germany, as an important tool to navigate the real world.

In general, Gerd is passionate about not only researching these topics but also seeing them applied outside of academia. He taught thousands of medical doctors how to understand and communicate statistics and also worked on a number of economical decision making scenarios.

In the end we discuss the benefits of simpler models for complex, uncertain situations, as for example in the case of predicting flu seasons.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Episode 272: The 4th Fugee