A cognitive model for aggregating people's rankings.

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

SUBMITTER: Lee MD 

PROVIDER: S-EPMC4015936 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature