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A preference matrix $M$ has an entry for each pair of candidates in an election whose value $p_{ij}$ represents the proportion of voters that prefer candidate $i$ over candidate $j$. The matrix is rationalizable if it is consistent with a…

Discrete Mathematics · Computer Science 2024-06-03 Agnes Totschnig , Rohit Vasishta , Adrian Vetta

We design the weights in consensus algorithms with spatially correlated random topologies. These arise with: 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the…

Information Theory · Computer Science 2015-05-13 Dusan Jakovetic , Joao Xavier , Jose M. F. Moura

In $d$-Scattered Set we are given an (edge-weighted) graph and are asked to select at least $k$ vertices, so that the distance between any pair is at least $d$, thus generalizing Independent Set. We provide upper and lower bounds on the…

Computational Complexity · Computer Science 2018-11-13 Ioannis Katsikarelis , Michael Lampis , Vangelis Th. Paschos

We consider linear codes over a finite field of odd characteristic, derived from determinantal varieties, obtained from symmetric matrices of bounded ranks. A formula for the weight of a code word is derived. Using this formula, we have…

Information Theory · Computer Science 2023-12-25 Peter Beelen , Trygve Johnsen , Prasant Singh

The well-known Condorcet's Jury theorem posits that the majority rule selects the best alternative among two available options with probability one, as the population size increases to infinity. We study this result under an asymmetric…

Computer Science and Game Theory · Computer Science 2024-08-02 Ganesh Ghalme , Reshef Meir

One of the central economic paradigms in multi-agent systems is that agents should not be better off by acting dishonestly. In the context of collective decision-making, this axiom is known as strategyproofness and turns out to be rather…

Computer Science and Game Theory · Computer Science 2023-02-24 Felix Brand , Patrick Lederer , Sascha Tausch

The robustness of risk measures to changes in underlying loss distributions (distributional uncertainty) is of crucial importance in making well-informed decisions. In this paper, we quantify, for the class of distortion risk measures with…

Risk Management · Quantitative Finance 2023-03-14 Carole Bernard , Silvana M. Pesenti , Steven Vanduffel

This work develops a rate-distortion-based approach to stochastic Chase decoding of algebraic codes over binary memoryless symmetric (BMS) channels, replacing the heuristics traditionally used to determine flip probabilities with…

Information Theory · Computer Science 2026-05-20 Amit Berman , Ariel Doubchak , Uri Erez , Tal Philosof , Ilya Shapir

We characterize the class of group-strategyproof mechanisms for the single facility location game in any unconstrained strictly convex space. A mechanism is \emph{group-strategyproof}, if no group of agents can misreport so that all its…

Computer Science and Game Theory · Computer Science 2020-08-12 Pingzhong Tang , Dingli Yu , Shengyu Zhao

In this paper, we propose a new averaging model for modeling the competitive influence of $K$ candidates among $n$ voters in an election process. For such an influence propagation model, we address the question of how many seeded voters a…

Social and Information Networks · Computer Science 2020-09-22 Yu-Hsien Peng , Ping-En Lu , Cheng-Shang Chang , Duan-Shin Lee

The Possible Winner problem asks, given an election where the voters' preferences over the candidates are specified only partially, whether a designated candidate can become a winner by suitably extending all the votes. Betzler and Dorn [1]…

Computational Complexity · Computer Science 2011-11-29 Dorothea Baumeister , Joerg Rothe

Aggregating agent preferences into a collective decision is an important step in many problems (e.g., hiring, elections, peer review) and across areas of computer science (e.g., reinforcement learning, recommender systems). As Social Choice…

Multiagent Systems · Computer Science 2025-09-12 Leonardo Matone , Ben Abramowitz , Ben Armstrong , Avinash Balakrishnan , Nicholas Mattei

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem…

Optimization and Control · Mathematics 2022-11-22 Zhi Chen , Daniel Kuhn , Wolfram Wiesemann

We study the problem of learning a high-density region of an arbitrary distribution over $\mathbb{R}^d$. Given a target coverage parameter $\delta$, and sample access to an arbitrary distribution $D$, we want to output a confidence set $S…

Data Structures and Algorithms · Computer Science 2025-05-14 Chao Gao , Liren Shan , Vaidehi Srinivas , Aravindan Vijayaraghavan

Postselection is the process of discarding outcomes from statistical trials that are not the event one desires. Postselection can be useful in many applications where the cost of getting the wrong event is implicitly high. However, unless…

Quantum Physics · Physics 2015-09-01 Joshua Combes , Christopher Ferrie

The median voter theorem has long been the default model of voter behavior and candidate choice. While contemporary work on the distribution of political opinion has emphasized polarization and an increasing gap between the "left" and the…

Physics and Society · Physics 2021-03-25 Matthew I. Jones , Antonio D. Sirianni , Feng Fu

We consider a type of pull voting suitable for discrete numeric opinions which can be compared on a linear scale, for example, 1 ('disagree strongly'), 2 ('disagree'), $\ldots,$ 5 ('agree strongly'). On observing the opinion of a random…

Probability · Mathematics 2023-05-26 Colin Cooper , Tomasz Radzik , Takeharu Shiraga

Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than voters who have supported bad ones. To develop a formal…

Computer Science and Game Theory · Computer Science 2017-03-16 Nika Haghtalab , Ritesh Noothigattu , Ariel D. Procaccia

In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) (Frankle & Carbin, 2018) pointed out that there could exist a winning ticket (i.e., a properly pruned sub-network together with original weight initialization)…

Machine Learning · Computer Science 2021-07-20 Ning Liu , Geng Yuan , Zhengping Che , Xuan Shen , Xiaolong Ma , Qing Jin , Jian Ren , Jian Tang , Sijia Liu , Yanzhi Wang

We study the dominating set problem in an online setting. An algorithm is required to guarantee competitiveness against an adversary that reveals the input graph one node at a time. When a node is revealed, the algorithm learns about the…

Data Structures and Algorithms · Computer Science 2021-05-04 Hovhannes Harutyunyan , Denis Pankratov , Jesse Racicot