.png){kind=icon}
.png){kind=icon}
Return
Naive calibration
.png){kind=icon}
PRE
AI
DOI：10.3982/TE5315.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We develop a model of non-Bayesian decision-making in which an agent obtains a signal about a relevant economic fundamental and subsequently takes an action. To interpret the signal, the agent calibrates a simple prediction rule based on a data set that consists of previous signals and state realizations. Her subsequent action affects the probability with which the current signal and the corresponding state realization will be observed and recorded in the data set that will be used in future decisions. We show that this procedure converges to a steady state and that it results in a seemingly pessimistic behavior that is exacerbated by feedback loops. We apply our model to project selection problems and second-price internet protocol version auctions.
Keywords:
Bounded rationality
misspecified models
selection bias
D81
D83
D91
Journal
T
IF:
1.3
Papers:
31
Citations:
0