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A cognitive model for aggregating people's rankings.

Michael D Lee1, Mark Steyvers1, Brent Miller1

  • 1Department of Cognitive Sciences, University of California Irvine, Irvine, California, United States of America.

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Summary

This study introduces a cognitive model to combine individual rankings, enhancing the "wisdom of the crowd" effect for accurate aggregate rankings and expertise measurement. The model outperforms traditional methods in prediction tasks.

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Area of Science:

  • Cognitive Science
  • Computational Psychology
  • Data Science

Background:

  • Combining individual judgments into a collective decision is a fundamental challenge.
  • Classic theories of knowledge representation and judgment offer insights into how individuals rank items.
  • Existing methods for aggregating rankings may not fully capture underlying cognitive processes.

Purpose of the Study:

  • To develop a cognitive modeling approach for combining people's rankings of items.
  • To infer an aggregate ranking and measures of individual expertise from behavioral data.
  • To evaluate the model's performance against traditional methods and the
  • wisdom of the crowd
  • effect.

Main Methods:

  • Developed a cognitive model based on psychological theories of knowledge representation.
  • Implemented the cognitive model as a Bayesian graphical model.
  • Utilized computational sampling for inferring aggregate rankings and individual expertise.

Main Results:

  • The model successfully produced aggregate rankings close to ground truth across 23 datasets.
  • The model's aggregate rankings generally outperformed individual judgments, demonstrating the "wisdom of the crowd" effect.
  • The model demonstrated superior performance compared to the traditional Borda count method and accurately inferred relative expertise.

Conclusions:

  • Cognitive modeling offers a powerful approach for analyzing and combining ranking data.
  • The developed model provides a robust method for aggregating judgments and assessing expertise.
  • This approach has significant implications for "wisdom of the crowd" research and applications involving collective intelligence.