# Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration

arXiv: https://arxiv.org/abs/2011.07476 · HTML: https://mlsanity.com/paper/2011.07476/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration](https://mlsanity.com/paper/2107.05719/) (2021)
- [Low-Degree Multicalibration](https://mlsanity.com/paper/2203.01255/) (2022)
- [Deep Probability Estimation](https://mlsanity.com/paper/2111.10734/) (2021)
- [Scoring Rules and Calibration for Imprecise Probabilities](https://mlsanity.com/paper/2410.23001/) (2024)
- [Explicit Abstention Knobs for Predictable Reliability in Video Question Answering](https://mlsanity.com/paper/2601.00138/) (2025)
- [No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions](https://mlsanity.com/paper/2306.01860/) (2023)
- [Paired Exact-Reset Evaluation of a Prediction-Derived Medium-to-Full World-Model Cascade](https://mlsanity.com/paper/2608.14650/) (2026)

## Abstract (arXiv)

Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individual forecasts and the corresponding utilities. To convey confidence about individual predictions to decision-makers, we propose a compensation mechanism ensuring that the forecasted utility matches the actually accrued utility. While a naive scheme to compensate decision-makers for prediction errors can be exploited and might not be sustainable in the long run, we propose a mechanism based on fair bets and online learning that provably cannot be exploited. We demonstrate an application showing how passengers could confidently optimize individual travel plans based on flight delay probabilities estimated by an airline.