Extracting the Wisdom of a Smaller Crowd fromDependent Quantile Judgments

52 Pages Posted: 5 Oct 2021

See all articles by Yuanyuan Lei

Yuanyuan Lei

Tsinghua University - Department of Industrial Engineering

Chen Wang

Tsinghua University - Department of Industrial Engineering

Date Written: August 18, 2021

Abstract

The task of this paper is to harness the wisdom of a crowd without calibration. We propose the Gaussian process model to account for sampling and judgmental errors in quantile judgments, and assume each expert to form predictions by linearly combining various information cues, inspired by the lens model. We develop a three-step estimation algorithm to factor quantile judgments into variable profiles (independent latent cues underlying each variable of interest) and expert profiles (each expert’s weights overthese cues). We can inquire about the dependence between experts using the weightsof latent cues, because they preserve the same clustering results as the weights of actual cues up to a full-rank linear transform. After clustering experts based on their estimatedprofiles, we pick one delegate from each group. Simulation and case studies demonstratethat the selected sub-crowd can represent the entire panel well in a new elicitation task.

Keywords: Wisdom of crowds; elicitation; quantile aggregation; lens model; matrix factorization

Suggested Citation

Lei, Yuanyuan and Wang, Chen, Extracting the Wisdom of a Smaller Crowd fromDependent Quantile Judgments (August 18, 2021). Available at SSRN: https://ssrn.com/abstract=3936178 or http://dx.doi.org/10.2139/ssrn.3936178

Yuanyuan Lei (Contact Author)

Tsinghua University - Department of Industrial Engineering ( email )

Beijing
China

Chen Wang

Tsinghua University - Department of Industrial Engineering ( email )

Beijing
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
16
Abstract Views
42
PlumX Metrics