arXiv:2406.15680 · v3

Calibrated Forecasting and Persuasion

首次提交 2024-06-22 07:05

版本更新 2026-10-29 23:52

雷达观测 2026-10-30 13:28

摘要

We study a dynamic game where an expert sends probabilistic forecasts to a decision-maker. The decision-maker verifies these forecasts using a calibration test based on past data. How should the expert send forecasts to maximize her payoff while passing the test? For a stationary ergodic process, we characterize the optimal forecasting strategy by reducing the dynamic game to a static persuasion problem. Calibration restricts long-run forecast distributions to mean-preserving contractions of the distribution of truthful forecasts. We compare the payoffs attainable by an informed and uninformed expert, providing a benchmark for the value of information. Finally, we consider a decision-maker who uses a mean-based learning strategy and show that the expert can guarantee at least the calibration benchmark and sometimes strictly more.

作者备注：The conference version of this work has been accepted to the Twenty-Fifth ACM Conference on Economics and Computation (EC'24)