activity
20212026
most citedSix Candidates Suffice to Win a Voter Majority

1 citations · 1 across the 13 of their papers we have counts for

collaborators

14 papers

cs.GT2026

Stable Voting Rules on the Edge of Optimal Metric Distortion

Ziyi Cai, Moses Charikar, Jabari Hastings +3

We prove the existence of a randomized voting rule with metric distortion at most , within of the lower bound of . Our rule comes from a generalization of…

cs.DS2026

Online Matching in Convex Bipartite Graphs

Yilong Feng, Zhihao Gavin Tang, Kangning Wang +1

Online resource-allocation systems, like outpatient scheduling and spectrum allocation, often assign sequentially arriving requests to an ordered pool of scarce resources, where ea…

cs.GT2026

Pessimal Elections for Approximately Dominating Sets

Moses Charikar, Prasanna Ramakrishnan, Kangning Wang

Condorcet's paradox is a foundational result in social choice theory, showing that no matter which candidate wins an election, a majority of voters may prefer some losing candidate…

cs.GT2026

An Exposition of Five Candidates Suffice for a Majority

Moses Charikar, Prasanna Ramakrishnan, Kangning Wang

We give a brief exposition of a result of Song, Nguyen, and Lin (2026) that every election (with ranked preferences) has a Condorcet winning set of at most five candidates.

cs.DS2026

Additively Competitive Secretaries

Mohammad Mahdian, Jieming Mao, Enze Sun +2

In the secretary problem, a set of secretary candidates arrive in a uniformly random order and reveal their values one by one. A company, who can only hire one candidate and hopes…

cs.GT2026

Winning in the Limit: Average-Case Committee Selection with Many Candidates

Yifan Lin, Shenyu Qin, Kangning Wang +1

We study the committee selection problem in the canonical impartial culture model with a large number of voters and an even larger candidate set. Here, each voter independently rep…

cs.GT2026

Distortion of Metric Voting with Bounded Randomness

Ziyi Cai, D. D. Gao, Prasanna Ramakrishnan +1

We study the design of voting rules in the metric distortion framework. It is known that any deterministic rule suffers distortion of at least , and that randomized rules can ac…

cs.GT2025

Strategyproof Tournament Rules for Teams with a Constant Degree of Selfishness

David Pennock, Daniel Schoepflin, Kangning Wang

We revisit the well-studied problem of designing fair and manipulation-resistant tournament rules. In this problem, we seek a mechanism that (probabilistically) identifies the winn…

cs.GT2025

A Geometric Analysis of Gains from Trade

Jason Hartline, Kangning Wang

We provide a geometric proof that the random proposer mechanism is a -approximation to the first-best gains from trade in bilateral exchange. We then refine this geometric analy…

cs.GT2025

Metric Distortion for Tournament Voting and Beyond

Moses Charikar, Prasanna Ramakrishnan, Zihan Tan +1

In the well-studied metric distortion problem in social choice, we have voters and candidates located in a shared metric space, and the objective is to design a voting rule that se…

cs.GT2025

Approximately Dominating Sets in Elections

Moses Charikar, Prasanna Ramakrishnan, Kangning Wang

Condorcet's paradox is a fundamental result in social choice theory which states that there exist elections in which, no matter which candidate wins, a majority of voters prefer a…

cs.GT2025

Approximately Efficient Bilateral Trade with Samples

Yuan Deng, Jieming Mao, Balasubramanian Sivan +2

We study the social efficiency of bilateral trade between a seller and a buyer. In the classical Bayesian setting, the celebrated Myerson-Satterthwaite impossibility theorem states…

cs.GT2024★ 1 cited

Six Candidates Suffice to Win a Voter Majority

Moses Charikar, Alexandra Lassota, Prasanna Ramakrishnan +2

A cornerstone of social choice theory is Condorcet's paradox which says that in an election where voters rank candidates it is possible that, no matter which candidate is d…

cs.DS2021

An Lower Bound for Online Matching on the Line

Kangning Wang

For online matching with the line metric, we present a lower bound of on the approximation ratio of any online (possibly randomized) algorithm. This beats the previous…