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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

Abstract: We propose a simple, incentive compatible elicitation mechanism 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 heterogeneous beliefs. In a laboratory experiment, we compare our method to the standard approach of eliciting discrete probabilities on pre-defined intervals. We find that elicitation with multiple point predictions is faster, more convenient and more predictive of subsequent behavior. We further find that multiple point predictions are more accurate if participants have heterogeneous beliefs. 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: 54 pages
Date: 2019-04-10, Revised 2020-05-17
New Economics Papers: this item is included in nep-exp
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