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Poster
Eliciting Categorical Data for Optimal Aggregation
Chien-Ju Ho · Rafael Frongillo · Yiling Chen

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #111 #None
in Tuesday Posters »

Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two broad categories: those assuming agents honest and consistent but with heterogeneous error rates, and those assuming agents strategic and seek to maximize their expected reward. The former often leads to tractable aggregation of elicited data, while the latter usually focuses on optimal elicitation and does not consider aggregation. In this paper, we develop a Bayesian model, wherein agents have differing quality of information, but also respond to incentives. Our model generalizes both categories and enables the joint exploration of optimal elicitation and aggregation. This model enables our exploration, both analytically and experimentally, of optimal aggregation of categorical data and optimal multiple-choice interface design.

Keywords: (Other) Probabilistic Models and Methods · (Application) Web Applications and Internet Data · Game Theory and Econometrics

Author Information

Chien-Ju Ho (Cornell University)
Rafael Frongillo (University of Colorado Boulder)
Yiling Chen (Harvard University)

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