Brain for Business Series 1, Episode 7: Decision making under conditions of radical uncertainty, with Professor Mark Fenton-O'Creevy, The Open University
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Jul 28, 2020 Professor Mark Fenton-O'Creevy, an expert in decision-making at The Open University, dives into the complexities of making choices during radical uncertainty. He contrasts radical uncertainty with normal risk, highlighting how traditional risk models failed during the financial crisis. Mark emphasizes the intertwined nature of emotion and cognition in decision-making and discusses the limitations of big data and AI in unpredictable situations. He advocates for scenario planning to enhance resilience and cautions against comforting narratives that may blind organizations to real issues.
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What Radical Uncertainty Really Means
- Radical uncertainty means we don't know all relevant outcomes, probabilities aren't meaningful, and framing often fails.
- Social and biological systems change so past data often can't predict the future in the way physical systems can.
Radical Uncertainty Is Often Hidden
- Radical uncertainty is common but often hidden by stable social facts that we take for granted.
- Events that shake those social facts expose the fragility of our assumptions about the future.
Financial Models Failed Big In 2008
- Mark uses the 2007–08 financial crisis to show models failed because they relied on bad assumptions about volatility.
- Goldman Sachs' '25 sigma' claim revealed those models couldn't capture real-world variation.
