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If You’re So Smart, Why Aren’t You Rich? Belief Selection in Complete and Incomplete Markets
, 2001
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Building a Reputation under Frequent Decisions
, 2005
"... I study reputation games with frequent decisions and persistently imperfect monitoring. In these games, as the period length tends to zero, the monitoring structure approaches a continuoustime limit, and, further, the limit monitoring is nontrivially imperfect. I show that for any prior probabilit ..."
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I study reputation games with frequent decisions and persistently imperfect monitoring. In these games, as the period length tends to zero, the monitoring structure approaches a continuoustime limit, and, further, the limit monitoring is nontrivially imperfect. I show that for any prior probability on the longrun player’s types that assigns positive probability to commitment types, if the longrun player’s instantaneous discount rate is close enough to zero, there is a lower bound on the set of Nash equilibrium payoffs of the longrun player which converges, as the period length tends to zero, to a number arbitrarily close to the longrun player’s commitment payoff. 1
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"... this paper we show that there is no chance. More formally, we show that under a very unrestrictive definition of what it means to draw priors "randomly," the probability that two priors have any chance of ..."
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this paper we show that there is no chance. More formally, we show that under a very unrestrictive definition of what it means to draw priors "randomly," the probability that two priors have any chance of
Uncertainty and Disagreement in Equilibrium Models ∗
, 2012
"... Leading equilibrium concepts require agents ’ beliefs to coincide with the model’s true probabilities and thus be free of systematic errors. This implicitly assumes a criterion that tests beliefs against the observed outcomes generated by the model. We formalize this requirement in stationary enviro ..."
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Leading equilibrium concepts require agents ’ beliefs to coincide with the model’s true probabilities and thus be free of systematic errors. This implicitly assumes a criterion that tests beliefs against the observed outcomes generated by the model. We formalize this requirement in stationary environments. We show that there is a tension between the requirements that beliefs can be tested against systematic errors, on the one hand, and allowing agents to disagree or be uncertain about the longrun fundamentals. We discuss the implications of