semi-random satisfaction
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Oregon > Multnomah County > Portland (0.04)
- North America > United States > New York > Rensselaer County > Troy (0.04)
- North America > United States > California > Los Angeles County > Pasadena (0.04)
The Semi-Random Satisfaction of Voting Axioms
We initiate the work towards a comprehensive picture of the worst average-case satisfaction of voting axioms in semi-random models, to provide a finer and more realistic foundation for comparing voting rules. We adopt the semi-random model and formulation in [Xia 2020], where an adversary chooses arbitrarily correlated ``ground truth'' preferences for the agents, on top of which random noises are added. We focus on characterizing the semi-random satisfaction of two well-studied voting axioms: Condorcet criterion and participation. We prove that for any fixed number of alternatives, when the number of voters $n$ is sufficiently large, the semi-random satisfaction of the Condorcet criterion under a wide range of voting rules is $1$, $1-\exp(-\Theta(n))$, $\Theta(n^{-0.5})$,
The Semi-Random Satisfaction of Voting Axioms
We initiate the work towards a comprehensive picture of the worst average-case satisfaction of voting axioms in semi-random models, to provide a finer and more realistic foundation for comparing voting rules. We adopt the semi-random model and formulation in [54], where an adversary chooses arbitrarily correlated "ground truth" preferences for the agents, on top of which random noises are added. We focus on characterizing the semi-random satisfaction of two well-studied voting axioms: Condorcet criterion and participation.
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Oregon > Multnomah County > Portland (0.04)
- North America > United States > New York > Rensselaer County > Troy (0.04)
- North America > United States > California > Los Angeles County > Pasadena (0.04)