Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track
Yannai A. Gonczarowski, Gregory Kehne, Ariel D Procaccia, Ben Schiffer, Shirley Zhang
In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome. This concept has been investigated extensively, but only through a worst-case lens. Instead, we study the expected distortion of voting rules with respect to an underlying distribution over voter utilities. Our main contribution is the design and analysis of a novel and intuitive rule, binomial voting, which provides strong distribution-independent guarantees for both expected distortion and expected welfare.