no code implementations • 8 May 2023 • Xiaoxi Guo, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, Hanpin Wang
The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness.
no code implementations • 18 Sep 2021 • Xiaoxi Guo, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, Hanpin Wang
In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner.
no code implementations • 29 Jan 2021 • Sujoy Sikdar, Xiaoxi Guo, Haibin Wang, Lirong Xia, Yongzhi Cao
We study the relationship between properties of the local mechanisms, each responsible for assigning all of the resources of a designated type, and the properties of a sequential mechanism which is composed of these local mechanisms, one for each type, applied sequentially, under lexicographic preferences, a well studied model of preferences over multiple types of resources in artificial intelligence and economics.
no code implementations • 25 Apr 2020 • Xiaoxi Guo, Sujoy Sikdar, Haibin Wang, Lirong Xia, Yongzhi Cao, Hanpin Wang
For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences, and sd-weak-strategyproof under lexicographic preferences.
no code implementations • 13 Jun 2019 • Haibin Wang, Sujoy Sikdar, Xiaoxi Guo, Lirong Xia, Yongzhi Cao, Hanpin Wang
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.