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Rademeldor – From Vague Outcomes to Explicit Conditions

How to build a scenario that is actually useful for your research
2025-05-28

Most people who follow markets already think in scenarios, even if they would not describe it that way. When you read a piece of news and find yourself wondering whether things will get better or worse, you are already sketching the outlines of a scenario in your head. The problem is that these mental sketches tend to be vague in a very particular way: they describe destinations without describing roads. You imagine a world in which a company thrives, or a world in which it struggles, but you leave unspecified the exact conditions that would need to hold for either world to come about. A properly constructed scenario corrects this by forcing you to be explicit. Instead of asking "what if things go well?", you ask "what would need to be true — about the economy, about the competitive landscape, about consumer behaviour, about regulation — for things to go well?" That shift from a direction of travel to a set of conditions is where scenario thinking becomes genuinely useful rather than merely comforting.

The next step is to identify which variables matter most and to be honest about which of those variables you can actually observe. Not every factor that influences an outcome is equally important, and not every important factor is equally measurable. Some things — such as the general direction of interest rates or the broad health of employment — tend to leave visible traces in publicly available data over time. Others, such as the internal strategy of a private competitor or the precise timing of a regulatory decision, are far harder to track from the outside. A well-built scenario separates these two categories clearly. It names the variables that are genuinely decisive, acknowledges which ones are opaque, and focuses your ongoing attention on those that are both important and observable. This discipline prevents a common mistake in private research, which is spending enormous energy monitoring things that are interesting but ultimately peripheral, while the variables that would actually change your view sit unexamined.

Once you have your conditions and your key variables, the most valuable thing you can do is define your signals in advance. A signal, in this context, is a piece of observable evidence that would shift the probability you assign to a given scenario. Defining signals before you see them matters because of a well-documented tendency in human reasoning: we are far better at finding evidence that confirms what we already believe than at noticing evidence that contradicts it. If you decide ahead of time that a certain kind of news would be a meaningful challenge to your preferred scenario, you are much less likely to dismiss it when it actually arrives. You might also find it useful to think about signals in pairs — one that would strengthen your conviction in a scenario, and one that would weaken it. This creates a kind of internal accountability. If you find yourself repeatedly updating towards your preferred outcome while the weakening signals accumulate unacknowledged, that asymmetry is itself a warning worth heeding.

Finally, it is worth treating your scenarios not as predictions but as tools for stress-testing your own assumptions. The purpose of writing down a scenario is not to prove that you have correctly foreseen the future; it is to make your current thinking legible enough that you can examine it critically and revise it honestly. One practical way to do this is to take your most confident scenario and ask what would have to be true about the world for it to be completely wrong. This is sometimes called a pre-mortem: imagining that your thesis has failed and working backwards to understand why. The conditions that emerge from that exercise are often the very conditions you had quietly assumed away when you were building the scenario in the first place. Investing research conducted this way — through explicit conditions, observable signals, and deliberate stress-testing — does not eliminate uncertainty, but it does mean that when uncertainty resolves, you are in a far better position to understand what it is telling you.

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