Bridging the Knowledge Gap: How AI Helps—and Fails—to Quantify Black Swan Risks

When it comes to risk management, “Black Swan” events capture our imagination. These are rare, high-impact occurrences that no one sees coming—precisely because our knowledge is incomplete.

Recently, I explored whether we should prepare for Black Swans at all and how Artificial Intelligence (AI) can help quantify risk.

My conclusion: while AI enables faster, more approachable modeling, it can’t magically turn unpredictable events into predictable ones.

In this newsletter, I’ll explain why even the best models fail when knowledge is incomplete, why we shouldn’t give up on quantification altogether, and how AI can help bridge some of our knowledge gaps.

Black Swans: An Issue of Incomplete Knowledge

Let’s start with the concept of a Black Swan, popularized by Nassim Nicholas Taleb. A Black Swan is something that defies our ability to predict because it arises from areas where our understanding is drastically limited.

When we look back at these events—such as catastrophic natural disasters, sudden market collapses, or unprecedented system failures—they might seem explainable in hindsight, but beforehand, our models rarely capture them.

The big question:

If these events come from incomplete knowledge, why even try to model them at all?

The answer is twofold.

First, we need to acknowledge that truly Black Swan events are, by definition, outside the scope of our present models—no matter how advanced (but than again, for some it may be no Black Swan, since it is observer dependent)

Second, risk quantification still matters because it reduces our knowledge gap around the less extreme but still significant high-impact, low-probability (HILP) events.

By better understanding the risks we can see, we free up resources to focus on strengthening overall resilience, making us more robust in the face of unknowns.

The Limitations of Risk Quantification for Extreme Events

One of the key points I made in my LinkedIn post is that when we try to quantify extreme risks (like an extended airport power outage), our standard models can miss crucial details.

The more extreme the event, the less room we have to overlook even a single variable, because that one variable might completely change the outcome.

For example, imagine modeling a rare, once-in-15-years power outage at Heathrow.

If we forget to include a critical factor—say, the effect of overlapping disruptions with air traffic control systems or international security protocols—we may underestimate the true cost or probability.

In a regular scenario, omitting a small factor might not hurt your model’s accuracy too much.

But for extremely low-frequency, high-impact events, that small oversight can balloon into a major blind spot.

The key reason lies in the distinction between fat-tailed (often governing extreme events) and thin-tailed (e.g., Gaussian) distributions.

In a Gaussian world, every additional data point refines the overall picture incrementally, so missing one observation typically does not drastically alter the model.

However, in a fat-tailed environment where extreme events dominate, you might see that 90% of all observations are relatively minor, but a single outlier is extraordinarily large.

If that critical outlier is absent from your dataset, the entire risk profile can be wildly off, because just one massive event can define the tail behavior and lead to a significant underestimation of real-world risk.

This highlights a critical truth:

As the potential impact of an event grows, so does the need for complete, high-quality data—and we’re never guaranteed we have everything we need. That’s why, no matter how sophisticated our tools are, we must remain humble about what our models can and cannot tell us.

How AI Helps—And How It Doesn’t

AI can sift through vast amounts of data, automate manual searches, and even self-correct as it learns from new information. When I researched historic airport outages, I relied on AI tools to:

• Aggregate Global Data Quickly I prompted different AI tools, so I get a good understanding about the historic data. At the end, i just needed it in a polished format to work with.

• Test Multiple Distributions AI-driven algorithms can compare how different probability distributions fit the data, making it easier to identify which model best captures observed outcomes (e.g., Poisson, Weibull, or Lognormal).

• Run Fast Simulations Once a distribution was chosen, AI could rapidly simulate thousands of future scenarios, giving me a richer understanding of potential outcomes under various assumptions.

However, AI doesn’t make risk quantification a better science—at least not for the most extreme and unpredictable threats.

It makes modeling more approachable, allowing us to ask more “what if?” questions and rapidly iterate on different assumptions.

This is a step forward because it encourages more people to engage with risk data and develop a more intuitive grasp of uncertainty.

But we can’t let the promise of AI lull us into believing we now have a crystal ball.

Why Quantify Risks if Models Can Fail?

If we accept that truly extreme events stem from incomplete knowledge, and thus our models can fail, why bother? Here’s why:

1. Reducing Our Knowledge Gap Even if we can’t predict everything, we can learn more about the risks we do know. By modeling known threats—like a moderate power outage—we develop benchmarks for budgeting, resource allocation, and potential mitigation measures.
2. Improving Decision-Making Executives and risk managers often need numbers to justify spending on mitigations or resilience measures. While no model is perfect, quantification offers a structured framework for discussion. It’s better to have some estimate than to debate blindly.
3. Guiding Resilience Efforts If the data tells us that a once-in-15-years outage could cost tens of millions, we might decide that full mitigation (costing hundreds of millions) is excessive. But we might still invest in partial redundancies or contingency planning, striking a better balance between cost and risk.
4. Focusing on What’s Not Modeled By acknowledging the gaps—places our models aren’t reliable—we can direct more attention to scenario planning, stress testing, and building general resilience. This ensures we don’t become overconfident in our neat, AI-driven charts and forget that unknowns still lurk off the radar.

Black Swans, by definition, live in the blind spots of our current knowledge.

Risk quantification, even aided by AI, can’t eliminate those blind spots—but it can shrink them.

By leveraging AI’s data-processing power, we make modeling more accessible, faster, and more collaborative. This lets us explore a broader array of “what if” scenarios with relative ease.

Yet we must remain aware that the more extreme the event, the less confident we can be in any single model’s predictions.

AI is a valuable ally, but it’s no magic bullet for incomplete knowledge. Our best strategy is to use AI and risk quantification techniques to learn as much as we can about the risks within our grasp—while staying vigilant and building flexible resilience for the surprises that still lie beyond our horizons.

Thank you for reading!

I hope this deep dive clarifies why AI is both a powerful tool and a reminder of our inevitable limits.

If you have questions or want to explore these ideas further, feel free to reach out or leave a comment. And if you haven’t already, make sure to subscribe for future insights on risk management, AI, and more.

Stay curious, stay prepared, and—above all—stay resilient.

Marco

P.S. If you want to learn how to do all of the above—including risk quantification—even without prior technical knowledge, my course is perfect for you.

You’ll gain all the essential skills to start your journey in under 6 hours.

Join the waitlist for my April cohort!

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