Calibrated Forecasting and Persuasion
<|reference_start|>Calibrated Forecasting and Persuasion: How should an expert send forecasts to maximize her utility subject to passing a calibration test? We consider a dynamic game where an expert sends probabilistic forecasts to a decision maker. The decision maker uses a calibration test based on past outcomes to verify the expert's forecasts. We characterize the optimal forecasting strategy by reducing the dynamic game to a static persuasion problem. A distribution of forecasts is implementable by a calibrated strategy if and only if it is a mean-preserving contraction of the distribution of conditionals (honest forecasts). We characterize the value of information by comparing what an informed and uninformed expert can attain. Moreover, we consider a decision maker who uses regret minimization, instead of the calibration test, to take actions. We show that the expert can achieve the same payoff against a regret minimizer as under the calibration test, and in some instances, she can achieve strictly more.<|reference_end|>
@article{jain2024calibrated,
    title={Calibrated Forecasting and Persuasion},
    author={Atulya Jain, Vianney Perchet},
    journal={arXiv preprint arXiv:2406.15680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15680},
    primaryClass={cs.GT econ.TH}
}