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In the Seat Arrangement problem the goal is to allocate agents to vertices in a graph such that the resulting arrangement is optimal or fair in some way. Examples include an arrangement that maximises utility or one where no agent envies…

Data Structures and Algorithms · Computer Science 2026-01-27 José Rodríguez

We study the problem of maximizing Nash social welfare, which is the geometric mean of agents' utilities, in two well-known models. The first model involves one-sided preferences, where a set of indivisible items is allocated among a group…

Computer Science and Game Theory · Computer Science 2025-05-19 Salil Gokhale , Harshul Sagar , Rohit Vaish , Vignesh Viswanathan , Jatin Yadav

We study incentive-compatible mechanisms that maximize the Nash Social Welfare. Since traditional incentive-compatible mechanisms cannot maximize the Nash Social Welfare even approximately, we propose changing the traditional model.…

Computer Science and Game Theory · Computer Science 2024-02-23 Shahar Dobzinski , Sigal Oren , Jan Vondrak

Imitation is widely observed in populations of decision-making agents. Using our recent convergence results for asynchronous imitation dynamics on networks, we consider how such networks can be efficiently driven to a desired equilibrium…

Computer Science and Game Theory · Computer Science 2017-04-17 James Riehl , Pouria Ramazi , Ming Cao

The strategic selection of resources by selfish agents is a classic research direction, with Resource Selection Games and Congestion Games as prominent examples. In these games, agents select available resources and their utility then…

Computer Science and Game Theory · Computer Science 2024-06-14 Jonathan Gadea Harder , Simon Krogmann , Pascal Lenzner , Alexander Skopalik

We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents…

Information Theory · Computer Science 2025-06-06 Symeon Vaidanis , Photios A. Stavrou , Marios Kountouris

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While…

Machine Learning · Computer Science 2018-12-27 Chen Tessler , Daniel J. Mankowitz , Shie Mannor

We study a general model on reusable resource allocation under model uncertainty. A heterogeneous population of customers arrive at the decision maker's (DM's) platform sequentially. Upon observing a customer's type, the DM selects an…

Optimization and Control · Mathematics 2022-12-07 Xilin Zhang , Wang Chi Cheung

We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the…

Computer Science and Game Theory · Computer Science 2024-09-24 Benjamin Heymann , Flavian Vasile , David Rohde

The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified…

Machine Learning · Computer Science 2024-05-21 Hamidreza Montaseri , Amin Gohari

Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is…

Multiagent Systems · Computer Science 2023-01-18 Theodor Cimpeanu , Francisco C Santos , The Anh Han

In the current intensively changing technological environment, wireless network operators try to manage the increase of global traffic, optimizing the use of the available resources. This involves associating each user to one of its…

Networking and Internet Architecture · Computer Science 2013-04-23 Vladimir Fux , Patrick Maillé , Jean-Marie Bonnin , Nassim Kaci

Safe Reinforcement Learning (RL) often faces significant issues such as constraint violations and instability, necessitating the use of constrained policy optimization, which seeks optimal policies while ensuring adherence to specific…

Machine Learning · Computer Science 2025-08-07 Ning Yang , Pengyu Wang , Guoqing Liu , Haifeng Zhang , Pin Lv , Jun Wang

We price a contingent claim liability using the utility indifference argument. We consider an agent with exponential utility, who invests in a stock and a money market account with the goal of maximizing the utility of his investment at the…

Pricing of Securities · Quantitative Finance 2011-12-14 Maxim Bichuch

Identifying who should be treated is a central question in economics. There are two competing approaches to targeting - paternalistic and autonomous. In the paternalistic approach, policymakers optimally target the policy given observable…

Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios…

Artificial Intelligence · Computer Science 2024-01-02 Omer Ben-Porat , Yishay Mansour , Michal Moshkovitz , Boaz Taitler

This paper studies the problem of maximizing the expected utility of terminal wealth for a financial agent with an unbounded random endowment, and with a utility function which supports both positive and negative wealth. We prove the…

Portfolio Management · Quantitative Finance 2008-12-10 Mark Owen , Gordan Zitkovic

An agent choosing between various actions tends to take the one with the lowest cost. But this choice is arguably too rigid (not adaptive) to be useful in complex situations, e.g., where exploration-exploitation trade-off is relevant in…

Data Analysis, Statistics and Probability · Physics 2018-12-04 Armen E. Allahverdyan , Aram Galstyan , Ali E. Abbas , Zbigniew R. Struzik

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective…

Computer Science and Game Theory · Computer Science 2024-08-02 Richard Willis , Yali Du , Joel Z Leibo , Michael Luck

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of…

Systems and Control · Electrical Eng. & Systems 2021-03-08 Mauro Salazar , Dario Paccagnan , Andrea Agazzi , W. P. M. H. , Heemels
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