Olivier Sibony, consultant et professeur à HEC Paris, excelle dans l'étude des biais cognitifs et de la prise de décision. Il explore comment notre instinct peut parfois nous tromper, en abordant des biais comme le biais de confirmation et la paralysie d'analyse. Sibony insiste sur l'importance de distinguer une bonne décision de son résultat final. Il rappelle que le succès ne réside pas dans l'imitation de célébrités, mais dans l'apprentissage de ses propres erreurs tout en expérimentant pour mieux saisir ses véritables intérêts.

This episode is part two of our live event with Psychologist Daniel Kahneman and Olivier Sibony, Professor of Strategy and Business Policy at HEC Paris. In conversation with host Ritula Shah they present their argument of how we can all make more intelligent decisions in an increasingly chaotic world.Part three of this event is available ad free, for subscribers now. We’d love to hear your feedback and what you think we should talk about next, who we should have on and what our future debates should be. Send us an email or voice note with your thoughts to podcasts@intelligencesquared.com or Tweet us @intelligence2. And if you’d like to get ad-free access to all Intelligence Squared podcasts, including exclusive bonus content, early access to new episodes and much more, become a supporter of Intelligence Squared today for just £4.99, or the equivalent in your local currency . Just visit intelligencesquared.com/membership to find out more.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this podcast, Daniel Kahneman, Cass R. Sunstein, and Olivier Sibony discuss the detrimental effects of noise in human judgment in various fields such as medicine, law, and economic forecasting. They highlight the importance of reducing bias and noise for better decision-making. They explore the challenges of noise in evaluating CEO candidates and the complexity of human judgment in group decision-making processes. The hosts also discuss a study on noise in group rankings and provide principles for minimizing noise in decision-making.