Radical Uncertainty

1. Radical uncertainty defines our world, not probabilistic risk

We simply do not know.

Uncertainty is pervasive. In most real-world situations, we face radical uncertainty - a state where we cannot enumerate all possible outcomes or assign meaningful probabilities to them. This is fundamentally different from probabilistic risk, where outcomes and their likelihoods are known.

Examples abound:

• Historical events: Napoleon's invasion of Russia, Columbus's voyage to the Americas
• Business decisions: The evolution of the computer industry, the rise of smartphones
• Political choices: The Cuban Missile Crisis, Brexit

Implications:

• Traditional decision-making models based on probabilities often fail in the face of radical uncertainty
• We must develop alternative approaches to navigate an unpredictable world

2. Narratives, not probabilities, guide decision-making under uncertainty

No one ever made a decision because of a number. They need a story.

Narratives shape understanding. Humans naturally construct and rely on narratives to make sense of complex situations and guide their decisions. These stories provide a framework for interpreting events and anticipating potential outcomes.

Key aspects of narrative thinking:

• Helps organize and prioritize information
• Allows for the integration of diverse perspectives and experiences
• Facilitates communication and collective decision-making

Real-world applications:

• Business strategy: Steve Jobs waiting for "the next big thing" at Apple
• Political leadership: Churchill's wartime speeches and decision-making
• Scientific discovery: The development of germ theory in medicine

3. Small-world models offer insights but can't fully capture real-world complexity

All models are wrong, but some are useful.

Models have limits. While small-world models in economics and finance can provide valuable insights, they often fail to capture the full complexity of real-world situations. Recognizing these limitations is crucial for effective decision-making.

Characteristics of small-world models:

• Well-defined parameters and outcomes
• Based on simplifying assumptions
• Often rely on probabilistic reasoning

Dangers of over-reliance:

• The 2008 financial crisis: Risk models failed to account for systemic risks
• Economic forecasting: Consistently poor track record in predicting major events

Balanced approach:

• Use models to gain insights and frame problems
• Complement model-based analysis with narrative reasoning and real-world experience

4. Effective decision-making requires challenging prevailing narratives

The fact that we were able to talk, debate, argue, disagree, and then debate some more was essential in choosing our ultimate course.

Challenge fosters better decisions. Good decision-makers actively seek out diverse perspectives and encourage challenges to prevailing narratives. This process helps identify blind spots and strengthens the final decision.

Techniques for fostering productive challenge:

• Devil's advocacy: Assign someone to argue against the prevailing view
• Red team/blue team exercises: Create competing groups to analyze a problem
• Diverse expertise: Include people with varied backgrounds and experiences

Historical examples:

• Kennedy's handling of the Cuban Missile Crisis
• Lincoln's "Team of Rivals" cabinet during the Civil War

Organizational implications:

• Create a culture that values open debate and dissenting opinions
• Avoid groupthink by actively seeking out alternative viewpoints

5. Evolutionary rationality trumps axiomatic rationality in large worlds

Humans are successful at adapting to the environment in which they find themselves, and have not evolved to perform rapid calculations of well-defined problems at which computers excel.

Human reasoning is adaptive. Our cognitive processes have evolved to deal with the complexities and uncertainties of the real world, rather than to solve abstract mathematical problems. This "evolutionary rationality" is often more effective in navigating radical uncertainty than formal logical approaches.

Key aspects of evolutionary rationality:

• Relies on heuristics and pattern recognition
• Integrates emotions and intuition into decision-making
• Adapts to changing environments and contexts

Contrasts with axiomatic rationality:

• Axiomatic approach assumes complete information and well-defined problems
• Often fails to account for real-world complexities and ambiguities

Implications:

• Decision-making processes should leverage human intuition and experience
• Formal models and algorithms should complement, not replace, human judgment

6. Collective intelligence is key to navigating radical uncertainty

Human intelligence is collective intelligence, and that is the source of the extraordinary human economic achievement.

Collaboration enhances decision-making. In a world of radical uncertainty, no single individual possesses all the necessary knowledge and insights. Collective intelligence - the combined knowledge and skills of a group - is crucial for effective problem-solving and innovation.

Benefits of collective intelligence:

• Diverse perspectives lead to more robust solutions
• Shared knowledge reduces individual blind spots
• Group processes can challenge and refine ideas

Examples of collective intelligence in action:

• Open-source software development
• Scientific research collaborations
• Crowd-sourced problem-solving platforms like InnoCentive

Fostering collective intelligence:

• Create environments that encourage open communication
• Develop systems for knowledge sharing and collaboration
• Value and integrate diverse expertise and experiences

7. Financial models are useful but dangerous when taken too literally

The numbers which were used in these calculations are invented.

Models have limitations. While financial models can provide valuable insights, they often rely on simplifying assumptions and invented numbers. Taking these models too literally can lead to dangerous misconceptions about risk and value.

Common pitfalls:

• Overconfidence in model predictions
• Neglecting factors not captured by the model
• Assuming past patterns will continue indefinitely

Notable failures:

• Long-Term Capital Management collapse in 1998
• Underestimation of systemic risk leading to the 2008 financial crisis

Balanced approach:

• Use models as one input among many in decision-making
• Continuously question and update model assumptions
• Complement quantitative analysis with qualitative judgment

8. Robustness and resilience, not certainty, are the keys to managing uncertainty

Radical uncertainty means that it is rarely possible to know the complete set of possible explanations.

Embrace uncertainty. Instead of seeking illusory certainty, focus on building systems and strategies that are robust and resilient in the face of unexpected events. This approach acknowledges the limitations of our knowledge and prepares us for a range of potential outcomes.

Key principles:

• Diversification: Spread risks across multiple domains
• Flexibility: Build in the ability to adapt to changing circumstances
• Redundancy: Create backup systems and contingency plans

Applications:

• Investment strategy: Broad diversification across asset classes and geographies
• Business planning: Scenario planning and stress-testing
• Public policy: Designing regulations that can withstand various economic conditions

Mindset shift:

• Move from prediction to preparation
• Focus on building capabilities rather than forecasting specific outcomes
• Cultivate adaptability and continuous learning

Last updated: October 23, 2024