Starting with absurd estimates helps you spot values that simply aren’t possible in risk analysis.

Title: Why start with something wildly off? A simple trick that sharpens FAIR risk thinking

If you’ve ever looked at a risk model and felt the numbers spinning out of control, you’re not alone. In information-risk work, there’s a small, almost playful technique that people use to get traction: start with an absurd estimate. Not because you believe it, but because it helps you see what isn’t possible and where your assumptions may be drifting.

Let me explain what this looks like in plain terms, then I’ll show you how to put it to work without getting stuck in the weeds.

What is the point of starting with absurd estimates?

• It helps you recognize values that are clearly not possible

That’s the core idea. By putting forward numbers that are obviously ridiculous, you force a reality check. If a requested probability or loss value can’t possibly fit the real world, you’ve got a built-in signal that something in the setup is off. It’s like pulling a loose thread—once you pull, you start to see where the fabric is frayed.

• It acts as a sanity filter for assumptions

In any risk calculation, you have inputs: asset value, exposure, threat frequency, vulnerability, and more. Some of these are soft numbers, born from judgement rather than precise data. An absurd input makes the team pause and ask, “What assumption is letting us go here, and is that assumption even plausible?”

• It helps establish boundaries for more realistic estimates

Extreme values aren’t just curiosity. They create a boundary around which you can corral your thinking. When you compare the absurd value to the range you actually accept, you can better judge what’s reasonable and what’s not. The result is a more grounded, defensible analysis.

• It sparks constructive discussion rather than blind consensus

When everyone agrees too quickly on a number, you may miss blind spots. A deliberately absurd estimate invites critique: why is it absurd, where does it fail, which factors did we overlook? The conversation moves from “Tin numbers” to “Why this matters.”

• It broadens the view without blowing up the model

You don’t replace realism with hyperbole. You use the absurd to widen the lens just enough to see gaps. Think of it as a creative check that keeps you from getting trapped in a single line of thought.

How to use this technique without losing your grip

If you’re exploring risk in the FAIR framework (Factor Analysis of Information Risk), here’s a practical, no-fluff way to apply the idea.

1. Start with an obviously absurd estimate

Pick a parameter and push it to an extreme. For example, suppose you’re estimating the annual probability of a data breach. A value like 99.9% might be absurd for most contexts. Or take potential loss: imagine an incident that wipes out 100% of a critical asset’s value for a single year—that’s probably unrealistic in many settings.

2. State why it’s absurd

Don’t just write the number and move on. Say why it’s ridiculous in the given context. Is it because historical data never shows that level of frequency? Is the asset's safeguards or controls designed to break at that point? Ground the absurdity in real-world constraints.

3. Use the absurd value to challenge assumptions

Ask pointed questions:

• Which assumption would have to be true for this absurd value to hold?

• What would have to change about the threat landscape, controls, or data quality for this to make sense?

• Are there data sources or benchmarks that contradict this extreme value?

4. Narrow the range to a realistic band

From the absurd number, work toward a plausible interval. You’ll often end up with a high, a low, and a most-likely estimate. The goal isn’t precision at this step; it’s to avoid foggy, unexamined guesses. You’re setting the boundaries so the rest of the analysis has something solid to stand on.

5. Cross-check with sensitivity thinking

Run a quick sensitivity check: if your most-likely estimate moves a little, does the conclusion change a lot? If yes, you know that your result is highly dependent on a narrow assumption—so you tighten that input or gather more data.

A quick mental model you can borrow

Think of risk estimation like planning a road trip with a GPS that sometimes misreads. You wouldn’t trust the first suggested route if the fuel gauge is questionable or if the weather report is dubious. You’d test a few alternate routes, check the fuel range, and ask about road conditions. Starting with an absurd estimate is a similar mental nudge: it asks you to test the edges before you settle on a single path.

Real-world analogies that help the idea land

• Weather forecasts: forecasters often run extreme scenario models to see what would happen in a storm’s worst case. Those edges aren’t predictions; they’re stress tests that inform warnings and precautions.

• Insurance underwriters: when evaluating a claim, an absurd scenario can reveal gaps in coverage, limits, or policy language. It’s not about having a number you’ll ever use, but about knowing where the cracks are.

• Sports analytics: analysts might push a player’s stats to ridiculous extremes to understand performance ceilings and the quality of the data. If the extreme isn’t plausible, it signals data quality issues or model mis-specification.

Common pitfalls to dodge

• Don’t let absurd numbers become the default

The aim is clarity, not theatrics. If every discussion slides into “what if we hit 100% loss?” you’re losing focus. Use the absurd as a cue, then quickly pivot back to plausible ranges.

• Don’t ignore context

A value that’s absurd in one sector might be more reasonable in another. Always tie the input to the specific environment, controls, and threat profile you’re analyzing.

• Don’t abandon rigor for novelty

Absurd estimates should lead to better questions and better checks, not to a carnival of improbable scenarios. Preserve a disciplined workflow: define, test, narrow, validate.

• Don’t pretend data is perfect

If the input data quality is vague, labeling a number as absurd can help you avoid pretending precision you don’t have. It’s a reminder to fetch better data or to be explicit about uncertainty.

A few practical touchpoints you’ll likely use

• Distribution shaping: once you’ve jailed the absurd estimate, you’ll often reframe inputs as a distribution (for example, a lognormal or beta distribution) that reflects both reality and uncertainty. It’s a natural step in FAIR-style thinking.

• Documentation matters: record why the absurd value was proposed, what it revealed, and how you adjusted. Clear notes save you from revisiting the same questions later and help teammates follow the logic.

• Stakeholder dialogue: a few well-placed questions can keep the conversation grounded. People appreciate when you show you’ve tested the edges and aren’t just chasing numbers.

A concise takeaway

Starting with absurd estimates isn’t about making risk numbers look dramatic. It’s a practical move to separate what’s possible from what isn’t and to keep assumptions honest. When you use this approach, you’re not chasing precision for its own sake. You’re building a sturdier understanding of the risk landscape, so you can focus on what truly matters: what needs protection, where safeguards are strongest or weakest, and how the model behaves under real-world conditions.

If you’re curious to try this, here’s a tiny, friendly checklist you can carry into your next analysis:

• Name the input you’ll test with an absurd value.

• State plainly why that value is absurd in your context.

• Identify the assumption(s) that would need to hold for the absurd value to matter.

• Narrow back to a plausible range and compare results.

• Document the process and the final chosen range, with a short justification.

Before long, you’ll notice a subtle but powerful shift: your estimates feel less like guesswork and more like a measured exploration. And when the team negotiates risk tradeoffs, you’ll have a clearer map to guide the conversation.

In the end, the value of starting with the absurd isn’t about validating extreme numbers; it’s about sharpening judgment, tightening reasoning, and making sure the risk picture you present isn’t stuffed with fantasy. It’s a small, practical trick that keeps your FAIR-style thinking sane, focused, and ready to adapt as the world changes.