Large-scale peer-to-peer loan consensus based on minimum cost consensus

Gespeichert in:

Autor*in:	Zhang, Huanhuan [verfasserIn] Kou, Gang [verfasserIn] Peng, Yi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Group decision group negotiation large-scale minimum cost consensus peering to peering lending soft consensus

Übergeordnetes Werk:	Enthalten in: Journal of the Operational Research Society - Operational Research Society, London : Taylor and Francis, 1978, 73(2022), 10, Seite 2326-2337
Übergeordnetes Werk:	volume:73 ; year:2022 ; number:10 ; pages:2326-2337

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/01605682.2021.1981782

Katalog-ID:	1833437497

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982			\|2 26 \|1 00 \|x DE-206 \|b Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.

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allfields	10.1080/01605682.2021.1981782 doi (DE-627)1833437497 (DE-599)KXP1833437497 DE-627 ger DE-627 rda eng Zhang, Huanhuan verfasserin (DE-588)1068823410 (DE-627)820788481 (DE-576)428184782 aut Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206 Kou, Gang verfasserin (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut Peng, Yi verfasserin (DE-588)1013878957 (DE-627)66485723X (DE-576)347900860 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 10, Seite 2326-2337 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:10 pages:2326-2337 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1981782 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 10 2326-2337 26 01 0206 4268472150 x1z 07-02-23 26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.
spelling	10.1080/01605682.2021.1981782 doi (DE-627)1833437497 (DE-599)KXP1833437497 DE-627 ger DE-627 rda eng Zhang, Huanhuan verfasserin (DE-588)1068823410 (DE-627)820788481 (DE-576)428184782 aut Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206 Kou, Gang verfasserin (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut Peng, Yi verfasserin (DE-588)1013878957 (DE-627)66485723X (DE-576)347900860 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 10, Seite 2326-2337 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:10 pages:2326-2337 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1981782 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 10 2326-2337 26 01 0206 4268472150 x1z 07-02-23 26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.
allfields_unstemmed	10.1080/01605682.2021.1981782 doi (DE-627)1833437497 (DE-599)KXP1833437497 DE-627 ger DE-627 rda eng Zhang, Huanhuan verfasserin (DE-588)1068823410 (DE-627)820788481 (DE-576)428184782 aut Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206 Kou, Gang verfasserin (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut Peng, Yi verfasserin (DE-588)1013878957 (DE-627)66485723X (DE-576)347900860 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 10, Seite 2326-2337 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:10 pages:2326-2337 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1981782 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 10 2326-2337 26 01 0206 4268472150 x1z 07-02-23 26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.
allfieldsGer	10.1080/01605682.2021.1981782 doi (DE-627)1833437497 (DE-599)KXP1833437497 DE-627 ger DE-627 rda eng Zhang, Huanhuan verfasserin (DE-588)1068823410 (DE-627)820788481 (DE-576)428184782 aut Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206 Kou, Gang verfasserin (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut Peng, Yi verfasserin (DE-588)1013878957 (DE-627)66485723X (DE-576)347900860 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 10, Seite 2326-2337 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:10 pages:2326-2337 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1981782 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 10 2326-2337 26 01 0206 4268472150 x1z 07-02-23 26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.
allfieldsSound	10.1080/01605682.2021.1981782 doi (DE-627)1833437497 (DE-599)KXP1833437497 DE-627 ger DE-627 rda eng Zhang, Huanhuan verfasserin (DE-588)1068823410 (DE-627)820788481 (DE-576)428184782 aut Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206 Kou, Gang verfasserin (DE-588)140231773 (DE-627)616526946 (DE-576)315424672 aut Peng, Yi verfasserin (DE-588)1013878957 (DE-627)66485723X (DE-576)347900860 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 10, Seite 2326-2337 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:10 pages:2326-2337 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1981782 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 10 2326-2337 26 01 0206 4268472150 x1z 07-02-23 26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.
language	English
source	Enthalten in Journal of the Operational Research Society 73(2022), 10, Seite 2326-2337 volume:73 year:2022 number:10 pages:2326-2337
sourceStr	Enthalten in Journal of the Operational Research Society 73(2022), 10, Seite 2326-2337 volume:73 year:2022 number:10 pages:2326-2337
format_phy_str_mv	Article
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institution	findex.gbv.de
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topic_facet	Group decision group negotiation large-scale minimum cost consensus peering to peering lending soft consensus
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	false
container_title	Journal of the Operational Research Society
authorswithroles_txt_mv	Zhang, Huanhuan @@aut@@ Kou, Gang @@aut@@ Peng, Yi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320465098
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language_de	englisch
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While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.</subfield></datafield></record></collection>
author	Zhang, Huanhuan
spellingShingle	Zhang, Huanhuan misc Group decision misc group negotiation misc large-scale misc minimum cost consensus misc peering to peering lending misc soft consensus Large-scale peer-to-peer loan consensus based on minimum cost consensus
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topic_title	26 00 DE-206 Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng Group decision (dpeaa)DE-206 group negotiation (dpeaa)DE-206 large-scale (dpeaa)DE-206 minimum cost consensus (dpeaa)DE-206 peering to peering lending (dpeaa)DE-206 soft consensus (dpeaa)DE-206
topic	misc Group decision misc group negotiation misc large-scale misc minimum cost consensus misc peering to peering lending misc soft consensus
topic_unstemmed	misc Group decision misc group negotiation misc large-scale misc minimum cost consensus misc peering to peering lending misc soft consensus
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title_full	Large-scale peer-to-peer loan consensus based on minimum cost consensus Huanhuan Zhang, Gang Kou and Yi Peng
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title_short	Large-scale peer-to-peer loan consensus based on minimum cost consensus
url	https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1981782 https://doi.org/10.1080/01605682.2021.1981782 https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1981782
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code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4346</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">73</subfield><subfield code="j">2022</subfield><subfield code="e">10</subfield><subfield code="h">2326-2337</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4268472150</subfield><subfield code="y">x1z</subfield><subfield code="z">07-02-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Large-scale decision-making, which involves a large number of participants, is common in practical applications. While cost is a crucial factor in large- scale decision-making, research on minimum cost consensus in group decision-making are mainly focused on a small number of experts. The goal of this paper is to expand the application of minimum cost consensus models to large-scale decision-making problems, and provide an approach for modeling and optimizing the cost in large-scale decision-making. This paper randomly collected a sample of 103 loans from one of the leading U.S. peer-to-peer loan platforms-Lending Club, and constructed several loan consensuses models. The minimum cost of loan consensus and changes of borrowers’ group opinions under such scenarios were studied. In addition, we discussed the effects of different loan consensus levels on the adjustments of interest rates and the consensus costs. The results showed that the proposed models can help Lending Club improve the efficiency of loan consensus reaching, and increase the rate of return of lenders. The proposed models provide a new way to coordinate the funds supply and demand of large-scale peer-to-peer lending, and help lenders and borrowers to reach a certain level of consensus on interest rates.</subfield></datafield></record></collection>
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Large-scale peer-to-peer loan consensus based on minimum cost consensus

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