Calibrated forecasting and merging

DOI10.1006/game.1998.0608zbMath0954.62109OpenAlexW2058195458MaRDI QIDQ1818290

Rann Smorodinsky, Ehud Lehrer, Ehud Kalai

Publication date: 4 January 2000

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

Full work available at URL: http://www.dklevine.com/archive/refs4584.pdf

zbMATH Keywords

learning modelsself-calibrating forecasts

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Prediction theory (aspects of stochastic processes) (60G25)

Related Items (14)

Calibrated learning and correlated equilibrium ⋮ Testing theories with learnable and predictive representations ⋮ Regret minimization in repeated matrix games with variable stage duration ⋮ A prequential test for exchangeable theories ⋮ Deterministic calibration and Nash equilibrium ⋮ Learning in Games ⋮ Entropy bounds on Bayesian learning ⋮ Learning the fundamentals in a stationary environment ⋮ Note on universal conditional consistency ⋮ Mostly calibrated ⋮ Conditional universal consistency. ⋮ An easier way to calibrate. ⋮ Opportunistic Approachability and Generalized No-Regret Problems ⋮ Calibration and Bayesian learning.

Cites Work

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• Weak and strong merging of opinions
• Bayesian learning leads to correlated equilibria in normal form games
• Belief affirming in learning processes
• Calibrated learning and correlated equilibrium
• Conditional universal consistency.
• An easier way to calibrate.
• Rational Learning Leads to Nash Equilibrium
• Self-Calibrating Priors Do Not Exist
• Merging of Opinions with Increasing Information
• Time to Build and Aggregate Fluctuations
• The Well-Calibrated Bayesian
• Compatible Measures and Merging
• Self-Confirming Equilibrium

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