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Degrees of Maybe: How We Can All Make Better Predictions

From sports, to politics, to the stock market, we love to make (and hear) predictions. This week, Hidden Brain explores why the so-called experts are so often wrong, and how we can avoid the common pitfalls of telling the future.
Elise Amendola
/
AP
From sports, to politics, to the stock market, we love to make (and hear) predictions. This week, Hidden Brain explores why the so-called experts are so often wrong, and how we can avoid the common pitfalls of telling the future.

Turn on the TV, and you'll find no shortage of people who claim to know what's going to happen: who's going to get picked for the NBA draft, who will win the next election, which stocks will go up or down.

These pundits and prognosticators all have an air of certainty. And why shouldn't they? We, as the audience, like to hear the world's complexity distilled into simple, pithy accounts. It doesn't help that these commentators rarely pay a serious price when their predictions don't pan out.

Lurking in the background are scores of ordinary people who do a much better job of predicting the future than the so-called experts. They're the subject of the book, Superforecasting: The Art and Science of Prediction, co-authored by psychologist Phil Tetlock and journalist Dan Gardner.

For years, Tetlock and his team of non-experts — among them, a retired irrigation specialist and former ballroom dancer — competed against the government's top intelligence officers in a forecasting tournament. The people tapping at their keyboards at their public libraries or in their homes while their kids played nearby did better on questions about whether Greece would leave the Eurozone or whether Russia would invade Ukraine — questions that were literally all over the map.

Tetlock discovered important ways these "superforecasters" differed from the rest of us. They're not all members of Mensa or polymaths. Their feats of prediction are more attainable than that: They view prediction as a skill that can be cultivated. They fall prey to a range of biases less often. They're humble. And they're willing to change their minds when faced with new information.

This week on Hidden Brain, our fascination with predictions, and why we may need a revolution in the way we make them.

The Hidden Brain Podcast is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Renee Klahr, Rhaina Cohen, and Parth Shah. Our supervising producer is Tara Boyle. You can also follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.

Copyright 2021 NPR. To see more, visit https://www.npr.org.

Shankar Vedantam is the host and creator of Hidden Brain. The Hidden Brain podcast receives more than three million downloads per week. The Hidden Brain radio show is distributed by NPR and featured on nearly 400 public radio stations around the United States.
Parth Shah is a producer and reporter in the Programming department at NPR. He came to NPR in 2016 as a Kroc Fellow.
Tara Boyle is the supervising producer of NPR's Hidden Brain. In this role, Boyle oversees the production of both the Hidden Brain radio show and podcast, providing editorial guidance and support to host Shankar Vedantam and the shows' producers. Boyle also coordinates Shankar's Hidden Brain segments on Morning Edition and other NPR shows, and oversees collaborations with partners both internal and external to NPR. Previously, Boyle spent a decade at WAMU, the NPR station in Washington, D.C. She has reported for The Boston Globe, and began her career in public radio at WBUR in Boston.
Jennifer Schmidt is a senior producer for Hidden Brain. She is responsible for crafting the complex stories that are told on the show. She researches, writes, gathers field tape, and develops story structures. Some highlights of her work on Hidden Brain include episodes about the causes of the #MeToo movement, how diversity drives creativity, and the complex psychology of addiction.
Rhaina Cohen is a producer and editor for NPR's Enterprise Storytelling unit, working across Embedded, Invisibilia, and Rough Translation.
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