TY - GEN
T1 - Fair and Truthful Mechanism with Limited Subsidy
AU - Goko, Hiromichi
AU - Igarashi, Ayumi
AU - Kawase, Yasushi
AU - Makino, Kazuhisa
AU - Sumita, Hanna
AU - Tamura, Akihisa
AU - Yokoi, Yu
AU - Yokoo, Makoto
N1 - Funding Information:
We would like to thank anonymous reviewers for valuable comments. This work was partially supported by the joint project of Kyoto University and Toyota Motor Corporation, titled “Advanced Mathematical Science for Mobility Society”, JST PRESTO Grant Numbers JPMJPR2122, JPMJPR212B, and JPMJPR20C1, and JSPS KAKENHI Grant Numbers JP16K00023, JP17K12646, JP18K18004, JP19K22841, JP20H00609, JP20H05967, JP20K19739, JP20H00609, JP21H04979, JP21K17708, and JP21H03397.
Publisher Copyright:
© 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
PY - 2022
Y1 - 2022
N2 - The notion of envy-freeness is a natural and intuitive fairness requirement in resource allocation. With indivisible goods, such fair allocations are unfortunately not guaranteed to exist. Classical works have avoided this issue by introducing an additional divisible resource, i.e., money, to subsidize envious agents. In this paper, we aim to design a truthful allocation mechanism of indivisible goods to achieve both fairness and efficiency criteria with a limited amount of subsidy. Following the work of Halpern and Shah, our central question is as follows: to what extent do we need to rely on the power of money to accomplish these objectives? We show that, when agents have matroidal valuations, there is a truthful allocation mechanism that achieves envy-freeness and utilitarian optimality by subsidizing each agent with at most 1, the maximum marginal contribution of each item for each agent. The design of the mechanism rests crucially on the underlying matroidal M-convexity of the Lorenz dominating allocations. For superadditive valuations, we show that there is a truthful mechanism that achieves envy-freeness and utilitarian optimality, with each agent receiving a subsidy of at most m; furthermore, we show that the amount m is necessary even when agents have additive valuations.
AB - The notion of envy-freeness is a natural and intuitive fairness requirement in resource allocation. With indivisible goods, such fair allocations are unfortunately not guaranteed to exist. Classical works have avoided this issue by introducing an additional divisible resource, i.e., money, to subsidize envious agents. In this paper, we aim to design a truthful allocation mechanism of indivisible goods to achieve both fairness and efficiency criteria with a limited amount of subsidy. Following the work of Halpern and Shah, our central question is as follows: to what extent do we need to rely on the power of money to accomplish these objectives? We show that, when agents have matroidal valuations, there is a truthful allocation mechanism that achieves envy-freeness and utilitarian optimality by subsidizing each agent with at most 1, the maximum marginal contribution of each item for each agent. The design of the mechanism rests crucially on the underlying matroidal M-convexity of the Lorenz dominating allocations. For superadditive valuations, we show that there is a truthful mechanism that achieves envy-freeness and utilitarian optimality, with each agent receiving a subsidy of at most m; furthermore, we show that the amount m is necessary even when agents have additive valuations.
KW - Envy-freeness
KW - Fair Division
KW - Mechanism Design with Money
UR - http://www.scopus.com/inward/record.url?scp=85134289290&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85134289290
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 534
EP - 542
BT - International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
Y2 - 9 May 2022 through 13 May 2022
ER -