Screening multiple potentially false experts
Francisco Barreras () and
Alvaro Riascos ()
No 15075, Monografías from Quantil
Abstract:
A decision maker is presented with a theory from a self proclaimed expert about the probability of occurrence of certain events. The decision maker faces the possibility that the expert is completely ignorant about the data generating process and so she’s interested in mechanisms that allow her to screen informed experts from uninformed ones. The decision maker needs to control for type I error, however, since she’s also uncertain about the true stochastic process, this gives room for uninformed experts to make strategic forecasts and ignorantly pass tests and profit from contracts. We present an original multiple expert model where a contract achieves screening of informed and uninformed experts by means of pitting experts’ predictions against each other. Additionally, we present a theoretical review of the main findings in two branches of literature that attempt to solve the expert screening problem. Namely models about testing experts and models in contract theory that pursue screening of experts.
Keywords: Testing of multiple experts; manipulation; adverse selection (search for similar items in EconPapers)
Pages: 43
Date: 2016-09-10
New Economics Papers: this item is included in nep-cta and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://richter.quantil.co/wp-content/uploads/2017/08/sme-1.pdf
Our link check indicates that this URL is bad, the error code is: 500 Can't connect to richter.quantil.co:80 (No such host is known. )
Related works:
Working Paper: Screening multiple potentially false experts (2016) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:col:000509:015075
Access Statistics for this paper
More papers in Monografías from Quantil
Bibliographic data for series maintained by Administrador ().