Publish the Weights: Make Optimism a Number You Can Discount

Confidence: hypothesis. The transparency practice below is drawn from published incident-review and forecasting-calibration literature, not firsthand deployment. The internal-publication discipline is an argument I have not shipped at scale. Argue with it.

Every team that tracks forecast accuracy eventually learns the same uncomfortable fact: some of their inputs are systematically optimistic. A site's self-reported enrollment capacity, a sales team's pipeline estimate, a vendor's delivery date: the same source, missing in the same direction, forecast after forecast. The rare team that notices this quietly discounts that source in the next model. The much rarer team writes the discount down where the person who submitted the optimistic number can see it.

That second move, publishing the weight and not just applying it, is the difference between a calibration practice that improves the next forecast and one that also improves the next submission.

Here's why the private version fails quietly. A planning team notices, over several cycles, that one vendor's delivery estimates run about 30% optimistic. They start applying a 0.7x haircut in their internal model. The forecast gets better. The vendor's next estimate does not. The vendor never saw the correction, has no reason to believe their estimating process needs fixing, and will submit the same kind of number next quarter. The team has built a permanent patch around a problem instead of fixing the problem, and the patch has to be maintained forever, quietly, by whoever remembers it exists.