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

Although strictly proper rules are designed to truthfully elicit subjective probabilistic beliefs, experimental results have shown that risk aversion causes agents to bias their reports towards the rule's baseline probability of 1/2, and a conservative preference for certain outcomes leads agents with moderate beliefs to simply report 1/2. While both of these distortions make recovery of true beliefs difficul, the second effect is particularly pernicious because it leaves the assessor unable to discriminate amongst a broad range of moderate probabilities. 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 modified off-the-shelft probability assessment mechanism that corrects these distortions and allows the assessor to revocer an accurate estimate of an agent's true beliefs. In an experiment, we demonstrate the effectiveness of this modification in both eliminating uninformative reports and eliciting true probabilistic beliefs.