Update beliefs frequently and in small increments
When new evidence arrives, adjust your probability estimate — even if the change is small.
Key takeaways
- What it is: When new evidence arrives, adjust your probability estimate — even if the change is small.
- Why it works: Superforecasters update more often and in smaller increments than average forecasters. The mechanism is Bayesian: each new piece of evidence has a diagnostic weight, and updating in proportion to that weight produces calibration. The two failure modes are under-updating (anchoring on the original estimate) and over-updating (overweighting recent salient events). Small, frequent updates resist both by keeping the estimate close to current evidence.
- Evidence: Backed by observational / correlational evidence.
- Avoid: Waiting until you’re "sure" about a change before updating — waiting for certainty means updating too late and by too much, which is just the anchoring failure.
Why it works
Superforecasters update more often and in smaller increments than average forecasters. The mechanism is Bayesian: each new piece of evidence has a diagnostic weight, and updating in proportion to that weight produces calibration. The two failure modes are under-updating (anchoring on the original estimate) and over-updating (overweighting recent salient events). Small, frequent updates resist both by keeping the estimate close to current evidence.
How to do it
- 1Set a regular review cadence for active forecasts: weekly for short-horizon, monthly for long.
- 2At each review, ask: "What evidence arrived since last time, and which direction does it push my estimate?"
- 3Make a numerical update — even ±3 percentage points — to log the decision.
- 4If evidence is consistent with the prior, record that explicitly rather than leaving the number unchanged.
What the evidence says
ObservationalIn the Good Judgment Project, superforecasters updated their predictions more than twice as frequently as the average participant and maintained better calibration as a result. Sequential Bayesian updating is the formal framework; the tournament data confirm it in practice.
Honest caveat: The update frequency that improved accuracy in forecasting tournaments may not apply identically to personal decision-making contexts with lower signal density.
- — Tetlock & Gardner (2015), Superforecasting
Common mistake
Waiting until you’re "sure" about a change before updating — waiting for certainty means updating too late and by too much, which is just the anchoring failure.
IX Coach prompts a brief evidence-check at each session and logs whether your confidence in current goals should shift, building a visible updating record over time.
Practice this with IX Coach →