<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ian Schofield Ball | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/ian-schofield-ball/</link><description>Ian Schofield Ball</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/ian-schofield-ball/index.xml" rel="self" type="application/rss+xml"/><item><title>Quota Mechanisms: Finite-Sample Optimality and Robustness</title><link>https://macropaperwarehouse.com/papers/quota-mechanisms-finite-sample-optimality-and-robustness/</link><guid>https://macropaperwarehouse.com/papers/quota-mechanisms-finite-sample-optimality-and-robustness/</guid><description>&lt;p&gt;Ball and Kattwinkel study quota mechanisms — linking mechanisms that impose aggregate constraints on agents&amp;rsquo; reports across multiple decision problems — and provide the first theoretical analysis under realistic finite-sample conditions with uncertainty about the type distribution. The canonical examples are mandatory grading curves, prescription drug monitoring programs, storable votes procedures, and lifetime assistance caps (TANF). Prior literature (Jackson and Sonnenschein 2007; Matsushima et al. 2010) established only asymptotic results under the assumption that the designer knows the exact population distribution, leaving the practical rationale for quotas incomplete.&lt;/p&gt;</description></item></channel></rss>