An easier way to calibrate.

DOI10.1006/game.1999.0726zbMath1131.91310OpenAlexW2108889573MaRDI QIDQ1818288

David K. Levine, Drew Fudenberg

Publication date: 4 January 2000

Published in: Games and Economic Behavior (Search for Journal in Brave)

Full work available at URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:3203773

Mathematics Subject Classification ID

Decision theory for games (91A35)

Related Items (16)

Online calibrated forecasts: memory efficiency versus universality for learning in games ⋮ Calibrated learning and correlated equilibrium ⋮ Testing theories with learnable and predictive representations ⋮ Indistinguishable predictions and multi-group fair learning ⋮ Learning, hypothesis testing, and Nash equilibrium. ⋮ Approachability, regret and calibration: implications and equivalences ⋮ Regret minimization in repeated matrix games with variable stage duration ⋮ Deterministic calibration and Nash equilibrium ⋮ Mostly calibrated ⋮ Merging and testing opinions ⋮ A nonmanipulable test ⋮ Regret in the on-line decision problem ⋮ A proof of calibration via Blackwell's approachability theorem. ⋮ Conditional universal consistency. ⋮ Calibrated forecasting and merging ⋮ Good Randomized Sequential Probability Forecasting is Always Possible

Cites Work

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• Consistency and cautious fictitious play
• An analog of the minimax theorem for vector payoffs
• A proof of calibration via Blackwell's approachability theorem.
• Calibrated forecasting and merging
• Self-Calibrating Priors Do Not Exist
• Merging of Opinions with Increasing Information
• The Well-Calibrated Bayesian

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