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A Cognitive Model for Aggregating People's Rankings

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  • Michael D Lee
  • Mark Steyvers
  • Brent Miller

Abstract

We develop a cognitive modeling approach, motivated by classic theories of knowledge representation and judgment from psychology, for combining people's rankings of items. The model makes simple assumptions about how individual differences in knowledge lead to observed ranking data in behavioral tasks. We implement the cognitive model as a Bayesian graphical model, and use computational sampling to infer an aggregate ranking and measures of the individual expertise. Applications of the model to 23 data sets, dealing with general knowledge and prediction tasks, show that the model performs well in producing an aggregate ranking that is often close to the ground truth and, as in the “wisdom of the crowd” effect, usually performs better than most of individuals. We also present some evidence that the model outperforms the traditional statistical Borda count method, and that the model is able to infer people's relative expertise surprisingly well without knowing the ground truth. We discuss the advantages of the cognitive modeling approach to combining ranking data, and in wisdom of the crowd research generally, as well as highlighting a number of potential directions for future model development.

Suggested Citation

  • Michael D Lee & Mark Steyvers & Brent Miller, 2014. "A Cognitive Model for Aggregating People's Rankings," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-9, May.
    • Handle: RePEc:plo:pone00:0096431
    DOI: 10.1371/journal.pone.0096431
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    References listed on IDEAS

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    1. George Karabatsos & William Batchelder, 2003. "Markov chain estimation for test theory without an answer key," Psychometrika, Springer;The Psychometric Society, vol. 68(3), pages 373-389, September.
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    Cited by:

    1. Joanna Jaroszewicz & Anna Majewska, 2021. "Group Spatial Preferences of Residential Locations—Simplified Method Based on Crowdsourced Spatial Data and MCDA," Sustainability, MDPI, vol. 13(9), pages 1-24, April.

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