The paper investigates welfare‑maximizing allocation of heterogeneous objects when agents use costly effort for screening instead of monetary transfers. It shows that as the number of object types increases, no‑screening mechanisms become more efficient, reducing the need for screening. The authors prove that in a symmetric continuous market with i.i.d. log‑concave values, the multidimensional allocation problem collapses to a single‑dimensional one based on agents’ best‑option values, and they demonstrate that this no‑screening optimality persists even as variety expands, supported by large‑variety limits and numerical experiments. The findings are applied to design an invitation‑based vaccine appointment system.
By Shunya Noda, Genta Okada
arXiv:2602. 17894v2 Announce Type: replace-cross Abstract: Data collection is a critical component of modern statistical and machine learning pipelines, particularly when data must be gathered from multiple heterogeneous sources to study a target population of interest.
By Michael O. Harding, Vikas Singh, Kirthevasan Kandasamy
arXiv:2606. 08360v1 Announce Type: cross Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases.
By Lingkai Kong, Hezi Jiang, Andrew Ma, Keyu Wang, Akseli Kangaslahti, Milind Tambe
arXiv:2606. 18679v1 Announce Type: cross Abstract: We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be assigned to facilities with limited capacities.
By Christopher En, Yuri Faenza, Andrea Lodi, Gonzalo Mu\~noz
The paper introduces a formal framework for Simulation-Augmented Generation (SAGE), a method that simulates individual viewpoints to answer contentious queries more representatively. By applying the metric proportional justified representation+ (mPJR+) axiom from proportional clustering, the authors prove that only a small number of simulations (n ≪ n_H) and dynamic routing to an even smaller subset (k ≪ n) are sufficient to approximate proportional representation for a large population. Empirical results on political and personal advice domains show that their routing algorithm outperforms k‑means and random selection baselines in achieving higher mPJR+ satisfaction rates.
By Sonja Kraiczy, Smitha Milli, Ratip Emin Berker, Avinandan Bose, Brandon Amos, Jamelle Watson-Daniels, Maximilian Nickel, Edith Elkind, Ariel D. Procaccia
arXiv:2606. 30932v1 Announce Type: new Abstract: Two-sided marketplaces connect distinct user groups whose interests often conflict -- improving outcomes on one side could degrade the other side's experience.
By Yufei Wu, Zhen Yan