16/12/2024
Good morning and compliment of the season. I recently read an interesting post that offered a thoughtful approach to addressing Black Swans in risk management. Unfortunately, I don’t have the link to the post—my apologies.
Since my research focuses on the interplay between dynamics, fragility, resilience, and antifragility, alongside the challenges of navigating unknowns, making decisions, executing plans, and achieving outcomes, I have identified areas where such discussions might unintentionally mislead or oversimplify key concepts, particularly regarding low-probability, high-impact events.
Below are my thoughts (which is open to your feedback), based on both experience and research, to help you critically evaluate content about Black Swans and gain better guidance when encountering similar discussions:
Dealing with Black Swans: Common Missteps to Avoid
● Fat-Tailed Distributions ≠ Black Swans
Extreme risks in fat-tailed models are known unknowns. Black Swans are entirely outside our predictive models. Do not confuse the two.
● Stress Testing ≠ Black Swan Preparedness
Stress tests assume predefined scenarios. Black Swans demand resilience and adaptability, not just modeled extremes.
● Models Have Limits
Even the best quantitative tools—which I value greatly, like Monte Carlo etc—are excellent for handling uncertainty and can even produce surprising results. However, these surprises can always be traced back to the assumptions and structure of the model. In contrast, the truly unimaginable and confounding events we increasingly face today cannot be traced back to any equation or model. These disruptions lie outside the boundaries of our frameworks, making Black Swans fundamentally different.
● Leadership Requires More Than Contingencies
Fallback plans are essential, but flexibility, decentralization, localization, and continuous learning are what truly drive survival during major disruptions. Interestingly, these qualities are not just arts—they are rooted in science, and can actually be gauged through frameworks and data-driven approaches.
■ The Key? Resilience and Antifragility Over 'Prediction'
You cannot “model” Black Swans. You can build and gauge systems that thrive in unpredictability.
What is your strategy for balancing the known, the unknown, and the unknowable? Let’s discuss below. 👇- Dr Apanisile Temitope Samuel, PhD (Chief Global Faculty @ www.pmconsultings.com)