Aggregating Binary Judgments Ranked by Accuracy


  • Daniel Halpern Harvard University
  • Gregory Kehne Carnegie Mellon University
  • Dominik Peters Harvard University
  • Ariel D. Procaccia Harvard University
  • Nisarg Shah University of Toronto
  • Piotr Skowron University of Warsaw



Social Choice / Voting


We revisit the fundamental problem of predicting a binary ground truth based on independent binary judgments provided by experts. When the accuracy levels of the experts are known, the problem can be solved easily through maximum likelihood estimation. We consider, however, a setting in which we are given only a ranking of the experts by their accuracy. Motivated by the worst-case approach to handle the missing information, we consider three objective functions and design efficient algorithms for optimizing them. In particular, the recently popular distortion objective leads to an intuitive new rule. We show that our algorithms perform well empirically using real and synthetic data in collaborative filtering and political prediction domains.




How to Cite

Halpern, D., Kehne, G., Peters, D., Procaccia, A. D., Shah, N., & Skowron, P. (2021). Aggregating Binary Judgments Ranked by Accuracy. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5456-5463.



AAAI Technical Track on Game Theory and Economic Paradigms