Social Welfare Maximization

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20 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
Oct 13, 2026Ruomeng Ding, Tianwei Gao, Lianrui Geng +2Social Welfare Maximization Preference Alignment
University of North Carolina at Chapel Hill
Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret
Oct 6, 2026Avishek GhoshMulti-Armed Bandits Social Welfare Maximization
Department of Computer Science and Engineering Indian Institute of Technology, Bombay
Maximizing $p$ -Mean Social Welfare in the High-Multiplicity Setting: Few Agent Types and Few Item Types
Oct 3, 2026Trung Thanh Nguyen, Khaled ElbassioniSocial Welfare Maximization Fair Resource Allocation
DataOptLab, National Economics University, Hanoi 11616, Vietnam · Khalifa University of Science and Technology, Abu Dhabi 127788, UAE
EF1-Constrained Nash Social Welfare with Identical Additive Valuations: Complexity, Guarantees, and Experiments
Sep 3, 2026Zih-Sian Yang, Yi-Hao Chen, Yu-Te Kuan +3Fair Resource Allocation Online Resource Allocation
National Taiwan Ocean University, Keelung City 202301, Taiwan · National Yang Ming Chiao Tung University, Hsinchu City 300, Taiwan
Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment
Aug 25, 2026Zachary Wojtowicz, Michelle Si, Finale Doshi-Velez +1Social Welfare Maximization AI Alignment
MIT Cambridge, MA 02139 · Harvard University Cambridge, MA 02138
Population-Robust Feature Selection via Generalized Welfare Optimization
Aug 3, 2026Ruiqi Lyu, Alistair Turcan, Bryan WilderDistribution Shift Robustness Social Welfare Maximization
Carnegie Mellon University
Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control?
Jun 22, 2026Sophie Hall, Kai Zhang, Ilia Shilov +2Multi-Agent Control Social Welfare Maximization
Simultaneously Efficient Allocation of Indivisible Items Across Multiple Dimensions
Jun 19, 2026Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin SanpuiCombinatorial Optimization Approximation Algorithms
Chuo University, Tokyo, Japan · Indian Institute of Technology Kharagpur, Kharagpur, India
Equilibrium with Internal Transfers
Jun 18, 2026Mingyang Liu, Gabriele Farina, Asuman OzdaglarNash Equilibrium Game Theory
LIDS, EECS, Massachusetts Institute of Technology
Social welfare optimisation under institutional reward and punishment
May 29, 2026Van An Nguyen, Vuong Khang Huynh, Huu Loi Bui +8Social Dilemmas Mechanism Design
Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam · 2Vietnam National University - Ho Chi Minh City (VNU-HCM), Vietnam · School of Computing, Engineering and Digital Technologies, Teesside University,2026 United Kingdom +1
Multi-Dimensional Matching in Market Design
May 19, 2026Irene AldridgeMechanism Design Social Welfare Maximization
Irene Aldridge
The Price of Proportional Representation in Temporal Voting
May 11, 2026Nicholas TehSocial Choice Theory Social Welfare Maximization
University of Oxford
Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
May 7, 2026John Wang, Mengfan XuMulti-Armed Bandits Multi-Agent System Optimization
University of Massachusetts Amherst
Optimizing Social Utility in Sequential Experiments
May 7, 2026Ander Artola Velasco, Stratis Tsirtsis, Manuel Gomez-RodriguezMechanism Design Sequential Decision Making
Max Planck Institute for Software Systems, Kaiserslautern, Germany · Hasso Plattner Institute, Potsdam, Germany
First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Apr 15, 2026Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz +1Social Welfare Maximization Algorithmic Fairness
Department of Computer Science, Saarland University, Germany · Zurich University of Applied Sciences, Switzerland
Linear Social Choice with Few Queries: A Moment-Based Approach
Mar 19, 2026Luise Ge, Daniel Halpern, Gregory Kehne +1Social Choice Theory Preference Elicitation
Washington University in St. Louis. · Google Research.
Revealing Positive and Negative Role Models to Help People Make Good Decisions
Mar 3, 2026Avrim Blum, Keziah Naggita, Matthew R. Walter +1Social Welfare Maximization
Toyota Technological Institute at Chicago
Online Generalized-Mean Welfare Maximization: Achieving Near-Optimal Regret from Samples
Feb 11, 2026Zongjun Yang, Rachitesh Kumar, Christian KroerFair Resource Allocation Online Resource Allocation
Columbia University · Carnegie Mellon University
Non-Obvious Manipulability in Additively Separable and Fractional Hedonic Games
May 19, 2025Diodato Ferraioli, Maria Fomenko, Giovanna VarricchioCooperative Game Theory Mechanism Design
University of Salerno, Italy · University of Calabria, Italy
No Screening is More Efficient with Multiple Objects
Aug 19, 2024Shunya Noda, Genta OkadaMechanism Design Social Welfare Maximization
Graduate School of Economics, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

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Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment

Ruomeng Ding, Tianwei Gao, Lianrui Geng, Mohit Bansal+1

University of North Carolina at Chapel Hill

Social Welfare Maximization Preference Alignment Pluralistic Alignment Preference Elicitation

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Pluralistic alignment needs to accommodate diverse preferences under limited human feedback. Using AI simulators to label preference comparisons reduces annotation costs, but variation in simulation accuracy across individuals can leave some people's preferences poorly represented in training. We study how to allocate human feedback across people to construct training data for pluralistic alignment, and how this allocation affects the trained policy. To make this allocation explicit and controllable, we define a social welfare objective over individual supervision utilities that measure label quality. Each query's marginal welfare gain depends on its expected supervision improvement, the recipient's current utility, and application-designated priority. Welfare-Aware Preference Acquisition (WAPA) uses auxiliary reference models to estimate query gains and individual utilities, then selects queries by estimated marginal welfare gain while updating recipient utilities. We prove exact optimality of the acquisition algorithm for the estimated welfare objective under a fixed query budget and model predictions. Experiments on six Roleplay collections and four OpinionQA regions demonstrate improved supervision and controllable allocation tradeoffs. Policy training on four Roleplay collections improves preference prediction for both seen and unseen individuals. When prioritizing a designated group and poorly represented individuals, acquiring human labels for 20% of comparisons recovers 75-86% of the average predictive utility gap between policies trained entirely on AI-generated labels and entirely on ground-truth labels.