Predicting how markets, policies, and one-off events are likely to resolve.
Markets and events share an awkward property: everyone has an opinion and almost no one writes their reasoning down. Prediction forces the reasoning into the open. When you ask MiroFish how a policy change, a product announcement, or a contested event is likely to resolve, it doesn't hand you a single confident number — it lays out the plausible resolutions, weights them, and shows the assumptions each one rests on. That structure is what lets you tell a real edge from a loud guess.
These scenarios are also where prediction has to be most honest about its limits. The further you get from repeatable, well-documented situations, the thinner the analogues become, and a responsible predictor says so. The posts in this cluster are explicit about where AI prediction is genuinely useful — reactions to announcements, the directional impact of a policy, the range of outcomes for an event with known mechanics — and where it's closer to fiction, like rare shocks with no precedent.
You'll find walk-throughs of predicting market reactions to product news, the directional effects of policy changes, what AI can and can't tell you about crypto scenarios, and how to predict an event's outcome when the data is incomplete. Each one models the discipline that separates a useful prediction from a horoscope: state the assumptions, branch the outcomes, weight them, and name the signal that would tell you which branch you're in.
Treat everything here as a way to think more clearly, not a tip sheet. The value of predicting a market or an event isn't the headline probability — it's the moment you realize the whole thing pivots on one variable you can actually watch. Read the relevant post, then run your own scenario through the predictor and see what it pivots on.
Describe your scenario and MiroFish predicts the likely outcomes — with probabilities and the reasoning behind each one.
You rarely have complete data when you need a prediction most. Here's how to predict an event's outcome with partial information — and why naming what you don't know makes the prediction better.
AI can't predict crypto prices, and anyone selling that is lying. But it can predict scenarios — conditional, structural outcomes. Here's the honest line between the two.
Markets overreact to announcements, then revert. Here's how to predict the real reaction to a product or competitor announcement — separating the noise spike from the structural shift.
Policy changes have second-order effects that surprise everyone. Here's how AI predicts the likely outcomes of a policy shift — the directional effects it gets right, and the limits it should admit.