Multi-type Resource Allocation with Partial Preferences

June 13, 2019 Β· Declared Dead Β· πŸ› AAAI Conference on Artificial Intelligence

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Authors Haibin Wang, Sujoy Sikdar, Xiaoxi Guo, Lirong Xia, Yongzhi Cao, Hanpin Wang arXiv ID 1906.06836 Category cs.AI: Artificial Intelligence Citations 9 Venue AAAI Conference on Artificial Intelligence Last Checked 4 months ago
Abstract
We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well known PS and RP mechanisms to the multi-type resource allocation problem (MTRA) with partial preferences. In our setting, there are multiple types of divisible items, and a group of agents who have partial order preferences over bundles consisting of one item of each type. We show that for the unrestricted domain of partial order preferences, no mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies ex-post-efficiency, sd-strategy-proofness, and upper invariance, recovering the properties of PS and RP.
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