A guide to formulating fairness in an optimization model
Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for...
Ausführliche Beschreibung
Autor*in: |
Xinying Chen, Violet [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
---|
Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 326(2023), 1 vom: 07. Apr., Seite 581-619 |
---|---|
Übergeordnetes Werk: |
volume:326 ; year:2023 ; number:1 ; day:07 ; month:04 ; pages:581-619 |
Links: |
---|
DOI / URN: |
10.1007/s10479-023-05264-y |
---|
Katalog-ID: |
OLC2144292183 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2144292183 | ||
003 | DE-627 | ||
005 | 20240118094228.0 | ||
007 | tu | ||
008 | 240118s2023 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10479-023-05264-y |2 doi | |
035 | |a (DE-627)OLC2144292183 | ||
035 | |a (DE-He213)s10479-023-05264-y-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q VZ |
084 | |a 3,2 |2 ssgn | ||
100 | 1 | |a Xinying Chen, Violet |e verfasserin |0 (orcid)0000-0002-5669-6102 |4 aut | |
245 | 1 | 0 | |a A guide to formulating fairness in an optimization model |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
520 | |a Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. | ||
650 | 4 | |a Fairness | |
650 | 4 | |a Distributive justice | |
700 | 1 | |a Hooker, J. N. |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Annals of operations research |d Springer US, 1984 |g 326(2023), 1 vom: 07. Apr., Seite 581-619 |w (DE-627)12964370X |w (DE-600)252629-3 |w (DE-576)018141862 |x 0254-5330 |
773 | 1 | 8 | |g volume:326 |g year:2023 |g number:1 |g day:07 |g month:04 |g pages:581-619 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10479-023-05264-y |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-WIW | ||
912 | |a SSG-OLC-MAT | ||
951 | |a AR | ||
952 | |d 326 |j 2023 |e 1 |b 07 |c 04 |h 581-619 |
author_variant |
c v x cv cvx j n h jn jnh |
---|---|
matchkey_str |
article:02545330:2023----::gieoomltnfinsiaotm |
hierarchy_sort_str |
2023 |
publishDate |
2023 |
allfields |
10.1007/s10479-023-05264-y doi (DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Xinying Chen, Violet verfasserin (orcid)0000-0002-5669-6102 aut A guide to formulating fairness in an optimization model 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. Fairness Distributive justice Hooker, J. N. aut Enthalten in Annals of operations research Springer US, 1984 326(2023), 1 vom: 07. Apr., Seite 581-619 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 https://doi.org/10.1007/s10479-023-05264-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 326 2023 1 07 04 581-619 |
spelling |
10.1007/s10479-023-05264-y doi (DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Xinying Chen, Violet verfasserin (orcid)0000-0002-5669-6102 aut A guide to formulating fairness in an optimization model 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. Fairness Distributive justice Hooker, J. N. aut Enthalten in Annals of operations research Springer US, 1984 326(2023), 1 vom: 07. Apr., Seite 581-619 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 https://doi.org/10.1007/s10479-023-05264-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 326 2023 1 07 04 581-619 |
allfields_unstemmed |
10.1007/s10479-023-05264-y doi (DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Xinying Chen, Violet verfasserin (orcid)0000-0002-5669-6102 aut A guide to formulating fairness in an optimization model 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. Fairness Distributive justice Hooker, J. N. aut Enthalten in Annals of operations research Springer US, 1984 326(2023), 1 vom: 07. Apr., Seite 581-619 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 https://doi.org/10.1007/s10479-023-05264-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 326 2023 1 07 04 581-619 |
allfieldsGer |
10.1007/s10479-023-05264-y doi (DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Xinying Chen, Violet verfasserin (orcid)0000-0002-5669-6102 aut A guide to formulating fairness in an optimization model 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. Fairness Distributive justice Hooker, J. N. aut Enthalten in Annals of operations research Springer US, 1984 326(2023), 1 vom: 07. Apr., Seite 581-619 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 https://doi.org/10.1007/s10479-023-05264-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 326 2023 1 07 04 581-619 |
allfieldsSound |
10.1007/s10479-023-05264-y doi (DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Xinying Chen, Violet verfasserin (orcid)0000-0002-5669-6102 aut A guide to formulating fairness in an optimization model 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. Fairness Distributive justice Hooker, J. N. aut Enthalten in Annals of operations research Springer US, 1984 326(2023), 1 vom: 07. Apr., Seite 581-619 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 https://doi.org/10.1007/s10479-023-05264-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 326 2023 1 07 04 581-619 |
language |
English |
source |
Enthalten in Annals of operations research 326(2023), 1 vom: 07. Apr., Seite 581-619 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 |
sourceStr |
Enthalten in Annals of operations research 326(2023), 1 vom: 07. Apr., Seite 581-619 volume:326 year:2023 number:1 day:07 month:04 pages:581-619 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Fairness Distributive justice |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Annals of operations research |
authorswithroles_txt_mv |
Xinying Chen, Violet @@aut@@ Hooker, J. N. @@aut@@ |
publishDateDaySort_date |
2023-04-07T00:00:00Z |
hierarchy_top_id |
12964370X |
dewey-sort |
14 |
id |
OLC2144292183 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144292183</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118094228.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10479-023-05264-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144292183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10479-023-05264-y-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xinying Chen, Violet</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5669-6102</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A guide to formulating fairness in an optimization model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fairness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributive justice</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hooker, J. N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of operations research</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">326(2023), 1 vom: 07. Apr., Seite 581-619</subfield><subfield code="w">(DE-627)12964370X</subfield><subfield code="w">(DE-600)252629-3</subfield><subfield code="w">(DE-576)018141862</subfield><subfield code="x">0254-5330</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:326</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:07</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:581-619</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10479-023-05264-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">326</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">07</subfield><subfield code="c">04</subfield><subfield code="h">581-619</subfield></datafield></record></collection>
|
author |
Xinying Chen, Violet |
spellingShingle |
Xinying Chen, Violet ddc 004 ssgn 3,2 misc Fairness misc Distributive justice A guide to formulating fairness in an optimization model |
authorStr |
Xinying Chen, Violet |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)12964370X |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0254-5330 |
topic_title |
004 VZ 3,2 ssgn A guide to formulating fairness in an optimization model Fairness Distributive justice |
topic |
ddc 004 ssgn 3,2 misc Fairness misc Distributive justice |
topic_unstemmed |
ddc 004 ssgn 3,2 misc Fairness misc Distributive justice |
topic_browse |
ddc 004 ssgn 3,2 misc Fairness misc Distributive justice |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Annals of operations research |
hierarchy_parent_id |
12964370X |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Annals of operations research |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 |
title |
A guide to formulating fairness in an optimization model |
ctrlnum |
(DE-627)OLC2144292183 (DE-He213)s10479-023-05264-y-p |
title_full |
A guide to formulating fairness in an optimization model |
author_sort |
Xinying Chen, Violet |
journal |
Annals of operations research |
journalStr |
Annals of operations research |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
container_start_page |
581 |
author_browse |
Xinying Chen, Violet Hooker, J. N. |
container_volume |
326 |
class |
004 VZ 3,2 ssgn |
format_se |
Aufsätze |
author-letter |
Xinying Chen, Violet |
doi_str_mv |
10.1007/s10479-023-05264-y |
normlink |
(ORCID)0000-0002-5669-6102 |
normlink_prefix_str_mv |
(orcid)0000-0002-5669-6102 |
dewey-full |
004 |
title_sort |
a guide to formulating fairness in an optimization model |
title_auth |
A guide to formulating fairness in an optimization model |
abstract |
Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT |
container_issue |
1 |
title_short |
A guide to formulating fairness in an optimization model |
url |
https://doi.org/10.1007/s10479-023-05264-y |
remote_bool |
false |
author2 |
Hooker, J. N. |
author2Str |
Hooker, J. N. |
ppnlink |
12964370X |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10479-023-05264-y |
up_date |
2024-07-03T21:18:07.317Z |
_version_ |
1803594222605959168 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144292183</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118094228.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10479-023-05264-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144292183</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10479-023-05264-y-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xinying Chen, Violet</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5669-6102</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A guide to formulating fairness in an optimization model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning. We present what appears to be the best practical approach to formulating each criterion in a linear, nonlinear, or mixed integer programming model. We also survey axiomatic and bargaining derivations of fairness criteria from the social choice literature while taking into account interpersonal comparability of utilities. Finally, we cite relevant philosophical and ethical literature where appropriate.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fairness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributive justice</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hooker, J. N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of operations research</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">326(2023), 1 vom: 07. Apr., Seite 581-619</subfield><subfield code="w">(DE-627)12964370X</subfield><subfield code="w">(DE-600)252629-3</subfield><subfield code="w">(DE-576)018141862</subfield><subfield code="x">0254-5330</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:326</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:07</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:581-619</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10479-023-05264-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">326</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">07</subfield><subfield code="c">04</subfield><subfield code="h">581-619</subfield></datafield></record></collection>
|
score |
7.400571 |