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We consider the setting of agents cooperatively minimizing the sum of local objectives plus a regularizer on a graph. This paper proposes a primal-dual method in consideration of three distinctive attributes of real-life multi-agent…

Optimization and Control · Mathematics 2023-12-11 Ziyi Yu , Nikolaos M. Freris

This paper is merged with arXiv:2107.08965v2. We refer the reader to the full and updated version. We study the problem of allocating a set of indivisible goods among agents with 2-value additive valuations. Our goal is to find an…

Computer Science and Game Theory · Computer Science 2021-10-13 Hannaneh Akrami , Bhaskar Ray Chaudhury , Kurt Mehlhorn , Golnoosh Shahkarami , Quentin Vermande

Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels. We propose a priority-based post-processing bias mitigation on both…

Artificial Intelligence · Computer Science 2021-02-02 Pranay Lohia

We study the Price of Anarchy of mechanisms for the well-known problem of one-sided matching, or house allocation, with respect to the social welfare objective. We consider both ordinal mechanisms, where agents submit preference lists over…

Computer Science and Game Theory · Computer Science 2016-03-01 George Christodoulou , Aris Filos-Ratsikas , Soren Kristoffer Stiil Frederiksen , Paul W. Goldberg , Jie Zhang , Jinshan Zhang

We study the allocation of divisible goods to competing agents via a market mechanism, focusing on agents with Leontief utilities. The majority of the economics and mechanism design literature has focused on \emph{linear} prices, meaning…

Computer Science and Game Theory · Computer Science 2019-12-24 Ashish Goel , Reyna Hulett , Benjamin Plaut

We address the problem of giving robust performance bounds based on the study of the asymptotic behavior of the insensitive load balancing schemes when the number of servers and the load scales jointly. These schemes have the desirable…

Performance · Computer Science 2016-05-11 Matthieu Jonckheere , Balakrishna Prabhu

We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment. We…

Machine Learning · Computer Science 2022-04-06 Jing Tan , Ramin Khalili , Holger Karl

In this work, we propose an axiomatic approach for measuring the performance/welfare of a system consisting of concurrent agents in a resource-driven system. Our approach provides a unifying view on popular system optimality principles,…

Systems and Control · Electrical Eng. & Systems 2021-04-19 Ezra Tampubolon , Holger Boche

Coordination is a desirable feature in many multi-agent systems such as robotic and socioeconomic networks. We consider a task allocation problem as a binary networked coordination game over an undirected regular graph. Each agent in the…

Systems and Control · Electrical Eng. & Systems 2023-10-02 Yifei Zhang , Marcos M. Vasconcelos

We provide the first asymptotic analysis of the Boston Mechanism under equilibrium play in random markets. We provide two results. First, while 63\% of students receive their first preference under truthful reporting-outperforming any other…

Theoretical Economics · Economics 2026-03-25 Josue Ortega

A principal delegates a project to a team $S$ from a pool of $n$ agents. The project's value if all agents in $S$ exert costly effort is $f(S)$. To incentivize the agents to participate, the principal assigns each agent $i\in S$ a share…

Computer Science and Game Theory · Computer Science 2025-04-29 Gil Aharoni , Martin Hoefer , Inbal Talgam-Cohen

We consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only information asymmetry (regarding previous outcomes) and cannot…

Computer Science and Game Theory · Computer Science 2019-05-15 Lee Cohen , Yishay Mansour

In this work, we revisit the problem of fairly allocating a number of indivisible items that are located on a line to multiple agents. A feasible allocation requires that the allocated items to each agent are connected on the line. The…

Computer Science and Game Theory · Computer Science 2022-05-24 Ankang Sun , Bo Li

One of the most important problems in the field of distributed optimization is the problem of minimizing a sum of local convex objective functions over a networked system. Most of the existing work in this area focus on developing…

Optimization and Control · Mathematics 2019-01-08 Fatemeh Mansoori , Ermin Wei

We study online fair allocation of $T$ sequentially arriving items among $n$ agents with heterogeneous preferences, with the objective of maximizing generalized-mean welfare, defined as the $p$-mean of agents' time-averaged utilities, with…

Computer Science and Game Theory · Computer Science 2026-02-12 Zongjun Yang , Rachitesh Kumar , Christian Kroer

We study the power of item-pricing as a tool for approximately optimizing social welfare in a combinatorial market. We consider markets with $m$ indivisible items and $n$ buyers. The goal is to set prices to the items so that, when agents…

Computer Science and Game Theory · Computer Science 2015-11-10 Michal Feldman , Nick Gravin , Brendan Lucier

The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen [Bernoulli 7 (2001) 223-242] uses the estimated covariance of the target distribution in the proposal distribution. This paper introduces a new robust adaptive…

Computation · Statistics 2011-05-30 Matti Vihola

To choose between two discrete goods, a consumer pays attention to only those with prices below a threshold. From these, she chooses her most preferred good. We assume consumers in a population have the same preference but may have…

Theoretical Economics · Economics 2025-11-07 Kaushil Patel

We consider the problem of creating assistants that can help agents solve new sequential decision problems, assuming the agent is not able to specify the reward function explicitly to the assistant. Instead of acting in place of the agent…

Machine Learning · Computer Science 2022-12-01 Sebastiaan De Peuter , Samuel Kaski

We consider distributed stochastic optimization problems that are solved with master/workers computation architecture. Statistical arguments allow to exploit statistical similarity and approximate this problem by a finite-sum problem, for…

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