Belief Elicitation with Multiple Point Predictions
Markus Eyting () and
Schmidt Patrick ()
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Markus Eyting: Johannes Gutenberg-University
Schmidt Patrick: HITS gGmbH Heidelberg
No 1818, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz
We propose a simple, incentive compatible procedure based on binarized linear scoring rules to elicit beliefs about real-valued outcomes - multiple point predictions. Simultaneously eliciting multiple point predictions with linear incentives reveals the subjective probability distribution without pre-defined intervals or probabilistic statements. We show that the approach is theoretically as robust as existing methods, while adapting flexibly to different beliefs. In a laboratory experiment, we compare our procedure to the standard approach of eliciting discrete probabilities on pre-defined intervals. We find that elicitation with multiple point predictions is faster, perceived as less difficult and more consistent with a subsequent decision. We further find that multiple point predictions are more accurate if beliefs vary between participants. Finally, we provide experimental evidence that pre-defined intervals anchor reports.
Keywords: elicitation of subjective expectations; partial identification; quantiles; overconfidence; experiment (search for similar items in EconPapers)
Pages: 58 pages
Date: 2019-04-10, Revised 2020-11-16
New Economics Papers: this item is included in nep-exp
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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1818.pdf First version, 2018 (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:jgu:wpaper:1818
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