Multi resource allocation with partial preferences
We provide efficient, fair, and non-manipulable mechanisms for the multi-type resource allocation problems (MTRAs) and multiple assignment problems where agents have partial preferences over bundles consisting of multiple divisible items. We uncover a natural reduction from multiple assignment probl...
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Veröffentlicht in: | Artificial intelligence 2023-01, Vol.314, p.103824, Article 103824 |
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description | We provide efficient, fair, and non-manipulable mechanisms for the multi-type resource allocation problems (MTRAs) and multiple assignment problems where agents have partial preferences over bundles consisting of multiple divisible items. We uncover a natural reduction from multiple assignment problems to MTRAs, which preserves the properties of MTRA mechanisms. We extend the well-known random priority (RP) and probabilistic serial (PS) mechanisms to MTRAs with partial preferences as multi-type PS (MPS) and multi-type RP (MRP) and propose a new mechanism, multi-type general dictatorship (MGD), which combines the ideas of MPS and MRP. We show that for the unrestricted domain of partial order preferences, unfortunately, no mechanism satisfies both sd-efficiency and sd-envy-freeness, even as they each satisfy different weaker notions of the desirable properties of efficiency, fairness, and non-manipulability we consider. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MRP satisfies ex-post-efficiency, sd-strategyproofness, and upper invariance, while MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, recovering the properties of RP and PS; the MGD satisfies sd-efficiency, equal treatment of equals, and decomposability under the unrestricted domain of partial preferences. We introduce a natural domain of bundle net preferences, which generalizes previously studied domain restrictions of partial preferences for multiple assignment problems and is incomparable to the domain of acyclic CP-nets. We show that MRP and MPS satisfy all properties of the RP and PS under bundle net preferences as well. |
doi_str_mv | 10.1016/j.artint.2022.103824 |
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We uncover a natural reduction from multiple assignment problems to MTRAs, which preserves the properties of MTRA mechanisms. We extend the well-known random priority (RP) and probabilistic serial (PS) mechanisms to MTRAs with partial preferences as multi-type PS (MPS) and multi-type RP (MRP) and propose a new mechanism, multi-type general dictatorship (MGD), which combines the ideas of MPS and MRP. We show that for the unrestricted domain of partial order preferences, unfortunately, no mechanism satisfies both sd-efficiency and sd-envy-freeness, even as they each satisfy different weaker notions of the desirable properties of efficiency, fairness, and non-manipulability we consider. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MRP satisfies ex-post-efficiency, sd-strategyproofness, and upper invariance, while MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, recovering the properties of RP and PS; the MGD satisfies sd-efficiency, equal treatment of equals, and decomposability under the unrestricted domain of partial preferences. We introduce a natural domain of bundle net preferences, which generalizes previously studied domain restrictions of partial preferences for multiple assignment problems and is incomparable to the domain of acyclic CP-nets. We show that MRP and MPS satisfy all properties of the RP and PS under bundle net preferences as well.</description><identifier>ISSN: 0004-3702</identifier><identifier>EISSN: 1872-7921</identifier><identifier>DOI: 10.1016/j.artint.2022.103824</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Computational social choice ; Domains ; Efficiency ; Invariance ; Material requirements planning ; Multi resource allocation ; Partial preference ; Probabilistic serial ; Random priority ; Resource allocation</subject><ispartof>Artificial intelligence, 2023-01, Vol.314, p.103824, Article 103824</ispartof><rights>2022 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Jan 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c329t-fc76c129dcb92816357a32b66c0b6e76483d70ea92039e5e4e9d853e8b726be13</cites><orcidid>0000-0002-9800-6691 ; 0000-0001-9517-7332 ; 0000-0003-4742-812X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.artint.2022.103824$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Wang, Haibin</creatorcontrib><creatorcontrib>Sikdar, Sujoy</creatorcontrib><creatorcontrib>Guo, Xiaoxi</creatorcontrib><creatorcontrib>Xia, Lirong</creatorcontrib><creatorcontrib>Cao, Yongzhi</creatorcontrib><creatorcontrib>Wang, Hanpin</creatorcontrib><title>Multi resource allocation with partial preferences</title><title>Artificial intelligence</title><description>We provide efficient, fair, and non-manipulable mechanisms for the multi-type resource allocation problems (MTRAs) and multiple assignment problems where agents have partial preferences over bundles consisting of multiple divisible items. We uncover a natural reduction from multiple assignment problems to MTRAs, which preserves the properties of MTRA mechanisms. We extend the well-known random priority (RP) and probabilistic serial (PS) mechanisms to MTRAs with partial preferences as multi-type PS (MPS) and multi-type RP (MRP) and propose a new mechanism, multi-type general dictatorship (MGD), which combines the ideas of MPS and MRP. We show that for the unrestricted domain of partial order preferences, unfortunately, no mechanism satisfies both sd-efficiency and sd-envy-freeness, even as they each satisfy different weaker notions of the desirable properties of efficiency, fairness, and non-manipulability we consider. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MRP satisfies ex-post-efficiency, sd-strategyproofness, and upper invariance, while MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, recovering the properties of RP and PS; the MGD satisfies sd-efficiency, equal treatment of equals, and decomposability under the unrestricted domain of partial preferences. We introduce a natural domain of bundle net preferences, which generalizes previously studied domain restrictions of partial preferences for multiple assignment problems and is incomparable to the domain of acyclic CP-nets. We show that MRP and MPS satisfy all properties of the RP and PS under bundle net preferences as well.</description><subject>Computational social choice</subject><subject>Domains</subject><subject>Efficiency</subject><subject>Invariance</subject><subject>Material requirements planning</subject><subject>Multi resource allocation</subject><subject>Partial preference</subject><subject>Probabilistic serial</subject><subject>Random priority</subject><subject>Resource allocation</subject><issn>0004-3702</issn><issn>1872-7921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LxTAQDKJgffoPPBQ895ls2qS5CPLwC5540XNI0y2m1LYmeYr_3pR6di_LLrOzM0PIJaNbRpm47rfGRzfGLVCAtOI1lEckY7WEQipgxySjlJYFlxROyVkIfRq5Uiwj8HwYoss9hungLeZmGCZropvG_NvF93xemM2Qzx479DhaDOfkpDNDwIu_viFv93evu8di__LwtLvdF5aDikVnpbAMVGsbBTUTvJKGQyOEpY1AKcqat5KiUZCkYIUlqrauONaNBNEg4xtytfLOfvo8YIi6TxrH9FKDTCUkE2VClSvK-imEpFLP3n0Y_6MZ1Us6utdrOnpJR6_ppLOb9QyTgy-HXgfrFnut82ijbif3P8Evn7RupQ</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Wang, Haibin</creator><creator>Sikdar, Sujoy</creator><creator>Guo, Xiaoxi</creator><creator>Xia, Lirong</creator><creator>Cao, Yongzhi</creator><creator>Wang, Hanpin</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9800-6691</orcidid><orcidid>https://orcid.org/0000-0001-9517-7332</orcidid><orcidid>https://orcid.org/0000-0003-4742-812X</orcidid></search><sort><creationdate>202301</creationdate><title>Multi resource allocation with partial preferences</title><author>Wang, Haibin ; Sikdar, Sujoy ; Guo, Xiaoxi ; Xia, Lirong ; Cao, Yongzhi ; Wang, Hanpin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c329t-fc76c129dcb92816357a32b66c0b6e76483d70ea92039e5e4e9d853e8b726be13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computational social choice</topic><topic>Domains</topic><topic>Efficiency</topic><topic>Invariance</topic><topic>Material requirements planning</topic><topic>Multi resource allocation</topic><topic>Partial preference</topic><topic>Probabilistic serial</topic><topic>Random priority</topic><topic>Resource allocation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Haibin</creatorcontrib><creatorcontrib>Sikdar, Sujoy</creatorcontrib><creatorcontrib>Guo, Xiaoxi</creatorcontrib><creatorcontrib>Xia, Lirong</creatorcontrib><creatorcontrib>Cao, Yongzhi</creatorcontrib><creatorcontrib>Wang, Hanpin</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Haibin</au><au>Sikdar, Sujoy</au><au>Guo, Xiaoxi</au><au>Xia, Lirong</au><au>Cao, Yongzhi</au><au>Wang, Hanpin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi resource allocation with partial preferences</atitle><jtitle>Artificial intelligence</jtitle><date>2023-01</date><risdate>2023</risdate><volume>314</volume><spage>103824</spage><pages>103824-</pages><artnum>103824</artnum><issn>0004-3702</issn><eissn>1872-7921</eissn><abstract>We provide efficient, fair, and non-manipulable mechanisms for the multi-type resource allocation problems (MTRAs) and multiple assignment problems where agents have partial preferences over bundles consisting of multiple divisible items. 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Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MRP satisfies ex-post-efficiency, sd-strategyproofness, and upper invariance, while MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, recovering the properties of RP and PS; the MGD satisfies sd-efficiency, equal treatment of equals, and decomposability under the unrestricted domain of partial preferences. We introduce a natural domain of bundle net preferences, which generalizes previously studied domain restrictions of partial preferences for multiple assignment problems and is incomparable to the domain of acyclic CP-nets. 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subjects | Computational social choice Domains Efficiency Invariance Material requirements planning Multi resource allocation Partial preference Probabilistic serial Random priority Resource allocation |
title | Multi resource allocation with partial preferences |
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