Naive Calibration

Speaker
Benjamin Bachi, University of Haifa
Date
14/06/2022 - 13:00 - 11:30Add To Calendar 2022-06-14 11:30:00 2022-06-14 13:00:00 Naive Calibration We develop a  model of non-Bayesian decision-making in which an agent obtains an estimate of the state of a relevant economic fundamental but does not know the joint distribution of the two. To make use of the estimate, she relies on an endogenously generated dataset that consists of previous estimates and state realizations.  She attributes a systematic difference between the estimates and state realizations in her dataset to a systematic bias in the estimate and naively calibrates it. Her subsequent action affects the probability with which the estimate and the corresponding state realization will be recorded in the dataset that will be used in future decisions.  We investigate the steady state of the naive calibration procedure and show 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 IPV auctions.   Joint with Yair Antler     A link to a draft of the paper can be found here. BIU Economics common room and will be Zoomed on https://us02web.zoom.us/j/82536086839 אוניברסיטת בר-אילן - Department of Economics Economics.Dept@mail.biu.ac.il Asia/Jerusalem public
Place
BIU Economics common room and will be Zoomed on https://us02web.zoom.us/j/82536086839
Affiliation
https://sites.google.com/econ.haifa.ac.il/benjaminbachi/home
Abstract

We develop a  model of non-Bayesian decision-making in which an agent obtains an estimate of the state of a relevant economic fundamental but does not know the joint distribution of the two. To make use of the estimate, she relies on an endogenously generated dataset that consists of previous estimates and state realizations.  She attributes a systematic difference between the estimates and state realizations in her dataset to a systematic bias in the estimate and naively calibrates it. Her subsequent action affects the probability with which the estimate and the corresponding state realization will be recorded in the dataset that will be used in future decisions.  We investigate the steady state of the naive calibration procedure and show 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 IPV auctions.

 
Joint with Yair Antler
 
 

A link to a draft of the paper can be found here.

Last Updated Date : 14/06/2022