AI Seminar sponsored by Apple

Anson Kahng -- Impartial Rank Aggregation

Abstract: 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.