Calibration and Bayesian learning

In a repeated game of incomplete information, myopic players form beliefs on next-period play and choose strategies to maximize next-period payoffs. Beliefs are treated as forecast of future plays. Forecast accuracy is assessed using calibration tests, which measure asymptotic accuracy of beliefs against some realizations. Beliefs are calibrated if they pass all calibration tests. For a positive Lebesgue measure of payoff vectors, beliefs are not calibrated. But, if payoff vector and calibration test are drawn from a suitable product measure, beliefs pass the calibration test almost surely.

• Publication year

  2002

• Venue

  Games Econ. Behav.

• Publication date

  2002-10-01

• Fields of study

  Mathematics, Computer Science, Economics

• Identifiers
  DOI 10.1016/S0899-8256(02)00010-6
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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• No concepts are published for this paper.

• Any Inspection is Manipulable

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