Random assignment with multi-unit demands

We consider the multi-unit random assignment problem in which agents express preferences over objects and objects are allocated to agents randomly based on the preferences. The most well-established preference relation to compare random allocations of objects is stochastic dominance (SD) which also leads to corresponding notions of envy-freeness, efficiency, and weak strategyproofness. We show that there exists no rule that is anonymous, neutral, efficient and weak strategyproof. For single-unit random assignment, we show that there exists no rule that is anonymous, neutral, efficient and weak group-strategyproof. We then study a generalization of the PS (probabilistic serial) rule called multi-unit-eating PS and prove that multi-unit-eating PS satisfies envy-freeness, weak strategyproofness, and unanimity.Comment: 17 page

A Pseudo-Market Approach to Allocation with Priorities

We propose a pseudo-market mechanism for no-monetary-transfer allocation of indivisible objects based on priorities such as those in school choice. Agents are given token money, face priority-specific prices, and buy utility-maximizing random assignments. The mechanism is asymptotically incentive compatible, and the resulting assignments are fair and constrained Pareto efficient. Hylland and Zeckhauser's (1979) position-allocation problem is a special case of our framework, and our results on incentives and fairness are also new in their classical setting

A Pseudo-Market Approach to Allocation with Priorities

We propose a pseudo-market mechanism for no-monetary-transfer allocation of indivisible objects based on priorities such as those in school choice. Agents are given token money, face priority-specific prices, and buy utility-maximizing random assignments. The mechanism is asymptotically incentive compatible, and the resulting assignments are fair and constrained Pareto efficient. Hylland and Zeckhauser's (1979) position-allocation problem is a special case of our framework, and our results on incentives and fairness are also new in their classical setting

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess