Migration as Submodular Optimization
Migration presents sweeping societal challenges that have recently attracted significant attention from the scientific community. One of the prominent approaches that have been suggested employs optimization and machine learning to match migrants to localities in a way that maximizes the expected nu...
Uložené v:
| Vydané v: | arXiv.org |
|---|---|
| Hlavní autori: | , |
| Médium: | Paper |
| Jazyk: | English |
| Vydavateľské údaje: |
Ithaca
Cornell University Library, arXiv.org
15.11.2018
|
| Predmet: | |
| ISSN: | 2331-8422 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Migration presents sweeping societal challenges that have recently attracted significant attention from the scientific community. One of the prominent approaches that have been suggested employs optimization and machine learning to match migrants to localities in a way that maximizes the expected number of migrants who find employment. However, it relies on a strong additivity assumption that, we argue, does not hold in practice, due to competition effects; we propose to enhance the data-driven approach by explicitly optimizing for these effects. Specifically, we cast our problem as the maximization of an approximately submodular function subject to matroid constraints, and prove that the worst-case guarantees given by the classic greedy algorithm extend to this setting. We then present three different models for competition effects, and show that they all give rise to submodular objectives. Finally, we demonstrate via simulations that our approach leads to significant gains across the board. |
|---|---|
| AbstractList | Migration presents sweeping societal challenges that have recently attracted significant attention from the scientific community. One of the prominent approaches that have been suggested employs optimization and machine learning to match migrants to localities in a way that maximizes the expected number of migrants who find employment. However, it relies on a strong additivity assumption that, we argue, does not hold in practice, due to competition effects; we propose to enhance the data-driven approach by explicitly optimizing for these effects. Specifically, we cast our problem as the maximization of an approximately submodular function subject to matroid constraints, and prove that the worst-case guarantees given by the classic greedy algorithm extend to this setting. We then present three different models for competition effects, and show that they all give rise to submodular objectives. Finally, we demonstrate via simulations that our approach leads to significant gains across the board. |
| Author | Procaccia, Ariel D Gölz, Paul |
| Author_xml | – sequence: 1 givenname: Paul surname: Gölz fullname: Gölz, Paul – sequence: 2 givenname: Ariel surname: Procaccia middlename: D fullname: Procaccia, Ariel D |
| BookMark | eNotjktLAzEURoMoWGt_gLsB3c5487iZZCnFF7R00e5LkkkkpZ3UZEbEX299rM7iwPm-K3Lep94TckOhEQoR7k3-jB8NVaAbYLLlZ2TCOKe1EoxdklkpO4AfwRD5hNwt41s2Q0x9ZUq1Hu0hdePe5Gp1HOIhfv2qa3IRzL742T-nZPP0uJm_1IvV8-v8YVEbZFiz1tughRA0aKM4OOG0pp1wDGRr0ArTgVSBW7DOOQgWtfcQjKWoT2cEn5Lbv-wxp_fRl2G7S2PuT4tbRoFRKUAi_wYu_kKL |
| ContentType | Paper |
| Copyright | 2018. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2018. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS |
| DOI | 10.48550/arxiv.1809.02673 |
| DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
| DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 2331-8422 |
| Genre | Working Paper/Pre-Print |
| GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS |
| ID | FETCH-LOGICAL-a525-27ebf94441f9a830c4c991d4c2067a5b4ad068f3b0bccc0fb59ee0fab15925543 |
| IEDL.DBID | M7S |
| IngestDate | Mon Jun 30 09:32:56 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a525-27ebf94441f9a830c4c991d4c2067a5b4ad068f3b0bccc0fb59ee0fab15925543 |
| Notes | SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50 |
| OpenAccessLink | https://www.proquest.com/docview/2102164065?pq-origsite=%requestingapplication% |
| PQID | 2102164065 |
| PQPubID | 2050157 |
| ParticipantIDs | proquest_journals_2102164065 |
| PublicationCentury | 2000 |
| PublicationDate | 20181115 |
| PublicationDateYYYYMMDD | 2018-11-15 |
| PublicationDate_xml | – month: 11 year: 2018 text: 20181115 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | Ithaca |
| PublicationPlace_xml | – name: Ithaca |
| PublicationTitle | arXiv.org |
| PublicationYear | 2018 |
| Publisher | Cornell University Library, arXiv.org |
| Publisher_xml | – name: Cornell University Library, arXiv.org |
| SSID | ssj0002672553 |
| Score | 1.6719202 |
| SecondaryResourceType | preprint |
| Snippet | Migration presents sweeping societal challenges that have recently attracted significant attention from the scientific community. One of the prominent... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| SubjectTerms | Competition Computer simulation Greedy algorithms Machine learning Migration Optimization |
| Title | Migration as Submodular Optimization |
| URI | https://www.proquest.com/docview/2102164065 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_opuDJb_yYo4ddu7VN2rQnQdlQcLPokHkar2kiO-zDdg7_fF-yTg-CFy-BEAhJXvi9X17eB0BL-kwE6McuZyio0YmLpHZd7kuhfQ8TZe0dLw9iMIhHoyStDG5l5Va5wUQL1PlcGht5xzxNiNqTxrxevLumapT5Xa1KaGxD3WRJCKzr3vO3jSWIBDFmtv7MtKm7Olh8TlZtk7SqbYbZLwi2eqW3_98VHUA9xYUqDmFLzY5g1_pzyvIYWv3J21q2DpYOocN0nhuHU-eRIGJaxV6ewLDXHd7euVVBBBfDwISOqUwnnAiMTjBmnuSS2F3OpUnBjmHGMfeiWLPMy6SUns7CRClPY0aUhc6Bs1OozeYzdQaO8BA1U7mvleJRqOmJHEV0KDSzDLjQ59DY7HlcXepy_LPhi7-HL2GPeEVsQvb8sAG1ZfGhrmBHrpaTsmhC_aY7SJ-aVlbUS-_76esXwz6dzw |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V07T8MwED6VFgQTb_EokKGMaZ3YiZMBMQBVqz6oRIW6RY5jow590JQCP4r_yDltYEBiY2DJ4siK7i7ffT7fA6AiHcpd4QQ2o4LjQ4e2QLdrM0dy7RARqize8djm3W4wGIS9AnzktTAmrTLHxAyok4k0MfKaOZogtUePeT19ts3UKHO7mo_QWJpFS72_4pEtvWreon4vXbd-179p2KupArbwXFN_pWIdMmQBOhQBJZJJpEgJk6aPufBiJhLiB5rGJJZSEh17oVJEixj9PtJvRnHbNSgxA_5ZpuDDV0jH9Tm-QZd3p1mnsJqYvQ0XVdMjq2qW6Q_Ez9xYffufCWAHSj0xVbNdKKjxHmxk2aoy3YdKZ_i0tFxLpBZi32iSmHRa6x4BcLSqLD2A_l981yEUx5OxOgKLEyE0VYmjlWK-p0PCfR91gDtLl3F9DOVcxNHql02jb_me_L58AZuNfqcdtZvd1ilsIYMKTHGi45WhOJ-9qDNYl4v5MJ2dZ-ZhQfTH2vgEpQX2ug |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Migration+as+Submodular+Optimization&rft.jtitle=arXiv.org&rft.au=G%C3%B6lz%2C+Paul&rft.au=Procaccia%2C+Ariel+D&rft.date=2018-11-15&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.1809.02673 |