Lossed in Translation: An Off-the-Shelf Method to Recover Probabilistic Beliefs from Loss-Averse Agents

50 Pages Posted: 15 Nov 2013 Last revised: 27 Jul 2015

See all articles by Theo Offerman

Theo Offerman

University of Amsterdam - Faculty of Economics & Econometrics (FEE)

Asa Palley

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Date Written: March 11, 2015

Abstract

Strictly proper scoring rules are designed to truthfully elicit subjective probabilistic beliefs from risk neutral agents. Previous experimental studies have identified two problems with this method: (i) risk aversion causes agents to bias their reports towards the probability of 1/2, and (ii) for moderate beliefs agents simply report 1/2. Applying a prospect theory model of risk preferences, we show that loss aversion can explain both of these behavioral phenomena. Using the insights of this model, we develop a simple off-the-shelf probability assessment mechanism that encourages loss-averse agents to report true beliefs. In an experiment, we demonstrate the effectiveness of this modification in both eliminating uninformative reports and eliciting true probabilistic beliefs.

Keywords: Scoring Rule, Subjective Probability Assessment, Loss Aversion, Prospect Theory

JEL Classification: C81, C91, D03, D81

Suggested Citation

Offerman, Theo and Palley, Asa, Lossed in Translation: An Off-the-Shelf Method to Recover Probabilistic Beliefs from Loss-Averse Agents (March 11, 2015). Experimental Economics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2353500 or http://dx.doi.org/10.2139/ssrn.2353500

Theo Offerman

University of Amsterdam - Faculty of Economics & Econometrics (FEE) ( email )

Roetersstraat 11
Amsterdam, 1018 WB
Netherlands
+31 20 525 4294 (Phone)
+31 20 525 5283 (Fax)

Asa Palley (Contact Author)

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Hodge Hall 4100
1275 E 10th St.
Bloomington, IN 47405
United States

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