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Furthermore, we can characterize the set of outcomes that result from calibrated strategies and solve for the optimal forecasting strategy

to induce the distribution πœ‚ is given by 𝜎 (𝑓 | Λœπœ”, Λœπ‘“ ) =  ο£²  πœ‚ ( Λœπ‘“ , Λœπœ”,𝑓 ) πœ‚ ( Λœπ‘“ , Λœπœ” ) if πœ‚ ( Λœπ‘“ , Λœπœ”) > 0 πœ‚ (𝑓 ) if πœ‚ ( Λœπ‘“ , Λœπœ”) = 0 β–‘ The sender’s maximization problem is given by the following linear program: max πœ‡ ∈ F βˆ‘οΈ 𝑓 πœ‡ ( · 2011

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Calibrated Forecasting and Persuasion

econ.TH Β· 2024-06-21 Β· unverdicted Β· novelty 6.0

For stationary ergodic processes the set of calibration-passing forecast distributions equals the mean-preserving contractions of the conditional distribution, allowing the dynamic game to be solved via static persuasion.

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  • Calibrated Forecasting and Persuasion econ.TH Β· 2024-06-21 Β· unverdicted Β· none Β· ref 36

    For stationary ergodic processes the set of calibration-passing forecast distributions equals the mean-preserving contractions of the conditional distribution, allowing the dynamic game to be solved via static persuasion.