Radical Uncertainty by John Kay & Mervyn King

Summary

'Radical Uncertainty' by John Kay and Mervyn King challenges the dominance of probabilistic thinking in economics, business, and policy-making. The authors argue that the world is far more ambiguous and unpredictable than models suggest, and that decision-making must embrace this inherent uncertainty. Instead of relying on statistical probabilities, they advocate for narrative reasoning and understanding context. By highlighting the limits of forecasting, Kay and King urge leaders to focus on resilience and adaptability rather than false precision.

Life-Changing Lessons

1. Embrace uncertainty rather than trying to eliminate it; not everything can be quantified or predicted.

2. Use narratives and stories to guide decision-making when data and models fall short, allowing for flexibility in complex situations.

3. Robust strategies and resilience often outperform attempts to optimize outcomes based on unreliable forecasts.

Publishing year and rating

The book was published in: 2020

AI Rating (from 0 to 100): 90

Practical Examples

1. The 2008 Financial Crisis

   Kay and King analyze the failures of financial models during the 2008 crash. Bankers and regulators placed excessive faith in value-at-risk models that did not account for rare, unpredictable events, leading to systemic collapse. The authors argue for humility and skepticism toward models in finance.

2. Pandemic Preparedness

   The book discusses how governments and institutions were often unprepared for pandemics like Covid-19, as risk assessments failed to account for genuine uncertainty. The event demonstrated the need for robust, adaptable planning instead of reliance on point forecasts.

3. The Case of Knightian Uncertainty

   Kay and King revive Frank Knight's concept of uncertainty that distinguishes measurable risks from true uncertainties that cannot be easily assigned probabilities. They urge decision-makers to recognize the limits of quantification in complex domains like climate change or technological innovation.

4. The Boeing 737 Max Crisis

   The authors explore how Boeing's focus on engineering optimization and cost-cutting overlooked the radical uncertainty posed by new software systems. The result was two fatal crashes that defied the company's risk models, demonstrating the dangers of ignoring non-quantifiable threats.

5. Brexit Decision-Making

   The book examines the UK's decision to leave the EU, noting how expert forecasts and models failed to capture the true complexity and unpredictability of such a major political event. Kay and King use Brexit to illustrate the need for narrative-based decision processes.

6. Insurance Against the Unknown

   Kay and King highlight how insurance works not by predicting events precisely but by pooling and sharing risks across many people. They stress that true uncertainty requires systems that can absorb shocks rather than predict them.

7. Artificial Intelligence Limitations

   The authors warn that AI and machine learning, despite their power, are still based on historical data and assumptions. They argue that in the face of radical uncertainty, human judgment and contextual reasoning remain essential.