
According to the author, in a repeated game of incomplete information, myopic players form beliefs on next-period play and choose strategies to maximize next-period payoffs, and their beliefs are treated as forecast of future plays. The forecast accuracy is assessed using calibration tests, which measure asymptotic accuracy of beliefs against some reliazations; the beliefs are calibrated if they pass all calibration tests. For a positive Lebesgue measure of payoff vectors, beliefs are not calibrated; but if a payoff vector and a calibration test are drawn from a suitable product measure, beliefs pass the calibration test almost surely.
Bayesian learning, Repeated games, Calibration, Multistage and repeated games, Forecast, Rationality and learning in game theory, Forecasting
Bayesian learning, Repeated games, Calibration, Multistage and repeated games, Forecast, Rationality and learning in game theory, Forecasting
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