Eliciting subjective real-valued beliefs
Greg Leo & Sam Stelnicki
Abstract
We present a simple and robustly incentive-compatible price list methodology to elicit quantiles of a subjective real-valued belief. These elicited quantiles can be employed to approximate a subject’s complete subjective distribution, and we establish that the distribution maximizing entropy while adhering to the elicited quantiles is piecewise linear. Using this approach, our methodology extends to estimating arbitrary unobserved attributes of the subjective distribution, such as mean and variance, which are otherwise challenging to elicit. We provide a proof-of-concept for our framework through an experiment involving the elicitation of participants’ beliefs regarding the mathematical abilities of their peers.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.