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We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocation literature, we…

Computer Science and Game Theory · Computer Science 2024-02-09 Zimeng Fan , Nianli Peng , Muhang Tian , Brandon Fain

One of the most appealing aspects of the (coarse) correlated equilibrium concept is that natural dynamics quickly arrive at approximations of such equilibria, even in games with many players. In addition, there exist polynomial-time…

Computer Science and Game Theory · Computer Science 2015-04-24 Siddharth Barman , Katrina Ligett

We study the max-min fair allocation problem in which a set of $m$ indivisible items are to be distributed among $n$ agents such that the minimum utility among all agents is maximized. In the restricted setting, the utility of each item $j$…

Discrete Mathematics · Computer Science 2016-11-28 T-H. Hubert Chan , Zhihao Gavin Tang , Xiaowei Wu

We consider the task of assigning indivisible goods to a set of agents in a fair manner. Our notion of fairness is Nash social welfare, i.e., the goal is to maximize the geometric mean of the utilities of the agents. Each good comes in…

Data Structures and Algorithms · Computer Science 2019-05-13 Bhaskar Chaudhury , Yun Kuen Cheung , Jugal Garg , Naveen Garg , Martin Hoefer , Kurt Mehlhorn

We consider the problem of approximating maximum Nash social welfare (NSW) while allocating a set of indivisible items to $n$ agents. The NSW is a popular objective that provides a balanced tradeoff between the often conflicting…

Computer Science and Game Theory · Computer Science 2020-10-02 Jugal Garg , Edin Husic , Laszlo A. Vegh

We propose a new model for aggregating preferences over a set of indivisible items based on a quantile value. In this model, each agent is endowed with a specific quantile, and the value of a given bundle is defined by the corresponding…

Computer Science and Game Theory · Computer Science 2026-05-06 Haris Aziz , Shivika Narang , Mashbat Suzuki

In this work, we consider the design of Non-Obviously Manipulable (NOM) mechanisms, mechanisms that bounded rational agents may fail to recognize as manipulable, for two relevant classes of succinctly representable Hedonic Games: Additively…

Computer Science and Game Theory · Computer Science 2025-05-21 Diodato Ferraioli , Giovanna Varricchio

We consider the problem of repeatedly choosing policies to maximize social welfare. Welfare is a weighted sum of private utility and public revenue. Earlier outcomes inform later policies. Utility is not observed, but indirectly inferred.…

Econometrics · Economics 2024-07-30 Nicolo Cesa-Bianchi , Roberto Colomboni , Maximilian Kasy

If a two-player social welfare maximization problem does not admit a PTAS, we prove that any maximal-in-range truthful mechanism that runs in polynomial time cannot achieve an approximation factor better than 1/2. Moreover, for the k-player…

Computer Science and Game Theory · Computer Science 2009-12-18 Shaddin Dughmi , Hu Fu , Robert Kleinberg

Designing individualized allocation of treatments so as to maximize the equilibrium welfare of interacting agents has many policy-relevant applications. Focusing on sequential decision games of interacting agents, this paper develops a…

Econometrics · Economics 2026-05-15 Toru Kitagawa , Guanyi Wang

Fair allocation of indivisible goods studies allocating $m$ goods among $n$ agents in a fair manner. While fairness is a fundamental requirement in many real-world applications, it often conflicts with (economic) efficiency. This raises a…

Computer Science and Game Theory · Computer Science 2025-06-03 Xiaolin Bu , Zihao Li , Shengxin Liu , Jiaxin Song , Biaoshuai Tao

Partitioning a large group of employees into teams can prove difficult because unsatisfied employees may want to transfer to other teams. In this case, the team (coalition) formation is unstable and incentivizes deviation from the proposed…

Computer Science and Game Theory · Computer Science 2024-06-04 Martin Bullinger , Sonja Kraiczy

We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs). The idea of HAG is to identify and remove redundancies in…

Data Structures and Algorithms · Computer Science 2021-02-09 Alexandra Porter , Mary Wootters

We study the fair k-set selection problem where we aim to select $k$ sets from a given set system such that the (weighted) occurrence times that each element appears in these $k$ selected sets are balanced, i.e., the maximum (weighted)…

Data Structures and Algorithms · Computer Science 2025-05-20 Shi Li , Chenyang Xu , Ruilong Zhang

We study the problem of allocating indivisible items on a path among agents. The objective is to find a fair and efficient allocation in which each agent's bundle forms a contiguous block on the line. We say that an instance is \emph{$(a,…

Computer Science and Game Theory · Computer Science 2025-01-14 Yasushi Kawase , Bodhayan Roy , Mohammad Azharuddin Sanpui

We consider a team formation setting where agents have varying levels of expertise in a global set of required skills, and teams are ranked with respect to how well the expertise of teammates complement each other. We model this setting as…

Computer Science and Game Theory · Computer Science 2020-11-04 Bugra Caskurlu , Fatih Erdem Kizilkaya , Berkehan Ozen

We study coalition formation in the framework of fractional hedonic games (FHGs). The objective is to maximize social welfare in an online model where agents arrive one by one and must be assigned to coalitions immediately and irrevocably.…

Computer Science and Game Theory · Computer Science 2025-10-22 Martin Bullinger , René Romen , Alexander Schlenga

Nguyen et al. [1] introduced altruistic hedonic games in which agents' utilities depend not only on their own preferences but also on those of their friends in the same coalition. We propose to extend their model to coalition formation…

Computer Science and Game Theory · Computer Science 2023-01-16 Anna Maria Kerkmann , Simon Cramer , Jörg Rothe

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

We study the problem of computing maximin share guarantees, a recently introduced fairness notion. Given a set of $n$ agents and a set of goods, the maximin share of a single agent is the best that she can guarantee to herself, if she would…

Computer Science and Game Theory · Computer Science 2018-06-12 Georgios Amanatidis , Evangelos Markakis , Afshin Nikzad , Amin Saberi