Statistical Foundations of Virtual Democracy

Examines which voting rules are robust to prediction errors in “virtual democracy” systems that learn individual preferences and aggregate predicted votes. The research proves that the classic Borda count rule is robust to prediction errors, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. The authors introduce a statistical framework emphasizing voting rules whose output on true preferences likely coincides with output on noisy estimates, providing practical guidance for implementing virtual democracy systems.