Forecasting is about assigning probabilities to future events.
Falsification is about testing whether an idea can survive clearly defined attempts to prove it false.
Forecasting asks, how likely is this to happen?
Falsification asks, what would prove this wrong, and has that happened?
This matters because not every meaningful idea resolves cleanly into a forecastable event.
For example, “UBI reduces crime” or “MoND is a better fit than dark matter at low accelerations” are not yes-or-no outcomes with clean resolution dates. They are explanatoryclaims that require careful, falsifiable framing and rigorous testing—not just a probability score.
Scientists, institutions, startups, or EA orgs could publish hypotheses with explicit bounties for refutation. For example:
“We offer $500 to anyone who can provide a reproducible counterexample to this published claim under defined criteria.”
This flips the incentive structure. Instead of just publishing or forecasting, you’re paying to be proven wrong, and rewarding others for helping you find errors early.
For startups, this means posting falsifiable assumptions about product-market fit, growth loops, or user retention, and inviting outsiders to challenge them.
For EA orgs, it means exposing theories of change to public scrutiny, backed by incentives for constructive falsification.
It turns falsification into a public good, not just a peer review ritual. And it introduces a new tool for intellectual quality control: pay to test your beliefs.
Forecasting tells you what might happen.
Falsification tells you whether your thinking can survive contact with reality.
Both are valuable, but they answer different questions, and serve different parts of the truth-seeking stack.
Thanks what’s the core difference between this and forecasting?
Good question. The core difference is this:
Forecasting is about assigning probabilities to future events.
Falsification is about testing whether an idea can survive clearly defined attempts to prove it false.
Forecasting asks, how likely is this to happen?
Falsification asks, what would prove this wrong, and has that happened?
This matters because not every meaningful idea resolves cleanly into a forecastable event.
For example, “UBI reduces crime” or “MoND is a better fit than dark matter at low accelerations” are not yes-or-no outcomes with clean resolution dates. They are explanatory claims that require careful, falsifiable framing and rigorous testing—not just a probability score.
Scientists, institutions, startups, or EA orgs could publish hypotheses with explicit bounties for refutation. For example:
“We offer $500 to anyone who can provide a reproducible counterexample to this published claim under defined criteria.”
This flips the incentive structure. Instead of just publishing or forecasting, you’re paying to be proven wrong, and rewarding others for helping you find errors early.
For startups, this means posting falsifiable assumptions about product-market fit, growth loops, or user retention, and inviting outsiders to challenge them.
For EA orgs, it means exposing theories of change to public scrutiny, backed by incentives for constructive falsification.
It turns falsification into a public good, not just a peer review ritual. And it introduces a new tool for intellectual quality control: pay to test your beliefs.
Forecasting tells you what might happen.
Falsification tells you whether your thinking can survive contact with reality.
Both are valuable, but they answer different questions, and serve different parts of the truth-seeking stack.