Ranking Wily People Who Rank Each Other


  • Anson Kahng Carnegie Mellon University
  • Yasmine Kotturi Carnegie Mellon University
  • Chinmay Kulkarni Carnegie Mellon University
  • David Kurokawa Carnegie Mellon University
  • Ariel Procaccia Carnegie Mellon University


peer ranking, rank aggregation, impartiality


We study rank aggregation algorithms that take as input the opinions of players over their peers, represented as rankings, and output a social ordering of the players (which reflects, e.g., relative contribution to a project or fit for a job). To prevent strategic behavior, these algorithms must be impartial, i.e., players should not be able to influence their own position in the output ranking. We design several randomized algorithms that are impartial and closely emulate given (non-impartial) rank aggregation rules in a rigorous sense. Experimental results further support the efficacy and practicability of our algorithms.




How to Cite

Kahng, A., Kotturi, Y., Kulkarni, C., Kurokawa, D., & Procaccia, A. (2018). Ranking Wily People Who Rank Each Other. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11467



AAAI Technical Track: Game Theory and Economic Paradigms