Quota Mechanisms: Finite-Sample Optimality and Robustness

[2309.07363] Quota Mechanisms: Finite-Sample Optimality and Robustness Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Theoretical Economics arXiv:2309.07363 (econ) [Submitted on 14 Sep 2023 ( v1 ), last revised 8 Apr 2026 (this version, v5)] Title: Quota Mechanisms: Finite-Sample Optimality and Robustness Authors: Ian Ball , Deniz Kattwinkel View a PDF of the paper titled Quota Mechanisms: Finite-Sample Optimality and Robustness, by Ian Ball and Deniz Kattwinkel View PDF HTML (experimental) Abstract: A quota mechanism, such as a mandatory grading curve, links together multiple decisions. We analyze the performance of quota mechanisms when the number of linked decisions is finite and the designer has imperfect knowledge of the type distribution. Using a new optimal transport approach, we derive an ex-post decision error guarantee for quota mechanisms. This guarantee cannot be improved by any mechanisms without transfers. We quantify the sensitivity of quota mechanisms to errors in the designer's estimate of the type distribution. Finally, we show that quotas are robust to a range of agents' beliefs about each other. Subjects: Theoretical Economics (econ.TH) Cite as: arXiv:2309.07363 [econ.TH] (or arXiv:2309.07363v5 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2309.07363 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ian Ball [ view email ] [v1] Thu, 14 Sep 2023 00:37:31 UTC (54 KB) [v2] Sat, 20 Apr 2024 02:01:32 UTC (50 KB) [v3] Tue, 15 Oct 2024 19:19:05 UTC (45 KB) [v4] Tue, 13 May 2025 22:24:51 UTC (59 KB) [v5] Wed, 8 Apr 2026 18:25:36 UTC (62 KB) Full-text links: Access Paper: View a PDF of the paper titled Quota Mechanisms: Finite-Sample Optimality and Robustness, by Ian Ball and Deniz Kattwinkel View PDF HTML (experimental) TeX Source view license Current browse context: econ.TH < prev | next > new | recent | 2023-09 Change to browse by: econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Concentration Without Representation#990003
• Concentration Without Representation#989265
• The Crowdsourced Replication Initiative participant survey#392224
• Attribution Bias in Large Language Models#1104426
• Pure Tail Constraints for Online Problems#1129929