Gerd Gigerenzer - Less Is More: Decision Making Under Uncertainty | Nudgestock 2019
Ғылым және технология
Gerd Gigerenzer's talk 'Less is more: Decision making under uncertainty' from Nudgestock 2019.
---
Nudgestock is the can't-miss festival where creativity and behavioral science intersect. It's a day to think differently.
The annual festival, curated by Ogilvy Consulting, features a wonderfully eclectic mix of talks and case studies from some of the leading thinkers and practitioners in the worlds of behavioral science, marketing and creativity.
Curated by Ogilvy Consulting.
---
Nudgestock 2021 Playlist:
• Nudgestock 2021
Nudgestock 2020 Playlist:
• Nudgestock 2020
Nudgestock 2019 Playlist:
• Nudgestock 2019
Nudgestock 2018 Playlist:
• Nudgestock 2018
Nudgestock 2017 Playlist:
• Nudgestock 2017
Nudgestock 2016 Playlist:
• Nudgestock 2016
Nudgestock 2015 Playlist:
• Nudgestock 2015
Nudgestock 2014 Playlist:
• Nudgestock 2014
Rory Sutherland @Nudgestock Playlist:
• Rory Sutherland @ Nudg...
---
Introducing Ogilvy Consulting’s Behavioural Science Practice
At Ogilvy Consulting, we work to creatively apply behavioural science insights to solve the world's most pressing and interesting challenges.
Whether tasked with improving the security of Europe's busiest airports, changing how people interact with the world's most popular social media or leading sustainability projects across the globe, we create effective interventions, communications and experiences for public, private, and third sector clients.
A magical combination of science and creativity, we are a unique global team of psychologists and behavioural economists embedded within the Ogilvy network. We bring proprietary tools, proven experience, expert facilitation and the power of Ogilvy creativity to unlock the hidden 'psychological power' within our partners' brands and channels.
We create unseen opportunities that generate giant impact.
---
#NudgeStock2019 #NudgeStock #GerdGigerenzer
---
Пікірлер: 2
Main takeaway - instinct/heuristics are what we use for big decisions we just don't like to admit it
He's clearly a genius, but not great at explaining himself. Almost the entire talk can be boiled down to: Often simpler models with fewer factors can be better at predicting the future, because very complex models overfit the data. When the world is stable, complex machine learning models are great. Problem is, mostly the world is not stable. This means that many factors which your model tells you are important are not useful for predicting the future. He shows how sometimes a single factor can give surprisingly good predictions