Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process
In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving...
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| Autor*in: |
Hahn, Eugene D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Erschienen: |
350 Main Street , Malden , MA 02148 , USA .: Decision Sciences ; 2003 |
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Online-Ressource |
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| Reproduktion: |
2003 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Decision sciences - Oxford : Wiley-Blackwell, 1988, 34(2003), 3, Seite 0 |
| Übergeordnetes Werk: |
volume:34 ; year:2003 ; number:3 ; pages:0 |
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| DOI / URN: |
10.1111/j.1540-5414.2003.02274.x |
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| 520 | |a In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. | ||
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10.1111/j.1540-5414.2003.02274.x doi (DE-627)NLEJ242373453 DE-627 ger DE-627 rakwb Hahn, Eugene D. verfasserin aut Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process 350 Main Street , Malden , MA 02148 , USA . Decision Sciences 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| Analytic Hierarchy Process In Decision sciences Oxford : Wiley-Blackwell, 1988 34(2003), 3, Seite 0 Online-Ressource (DE-627)NLEJ243926456 (DE-600)2066218-X 1540-5915 nnns volume:34 year:2003 number:3 pages:0 http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 34 2003 3 0 |
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10.1111/j.1540-5414.2003.02274.x doi (DE-627)NLEJ242373453 DE-627 ger DE-627 rakwb Hahn, Eugene D. verfasserin aut Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process 350 Main Street , Malden , MA 02148 , USA . Decision Sciences 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| Analytic Hierarchy Process In Decision sciences Oxford : Wiley-Blackwell, 1988 34(2003), 3, Seite 0 Online-Ressource (DE-627)NLEJ243926456 (DE-600)2066218-X 1540-5915 nnns volume:34 year:2003 number:3 pages:0 http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 34 2003 3 0 |
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10.1111/j.1540-5414.2003.02274.x doi (DE-627)NLEJ242373453 DE-627 ger DE-627 rakwb Hahn, Eugene D. verfasserin aut Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process 350 Main Street , Malden , MA 02148 , USA . Decision Sciences 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| Analytic Hierarchy Process In Decision sciences Oxford : Wiley-Blackwell, 1988 34(2003), 3, Seite 0 Online-Ressource (DE-627)NLEJ243926456 (DE-600)2066218-X 1540-5915 nnns volume:34 year:2003 number:3 pages:0 http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 34 2003 3 0 |
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10.1111/j.1540-5414.2003.02274.x doi (DE-627)NLEJ242373453 DE-627 ger DE-627 rakwb Hahn, Eugene D. verfasserin aut Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process 350 Main Street , Malden , MA 02148 , USA . Decision Sciences 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| Analytic Hierarchy Process In Decision sciences Oxford : Wiley-Blackwell, 1988 34(2003), 3, Seite 0 Online-Ressource (DE-627)NLEJ243926456 (DE-600)2066218-X 1540-5915 nnns volume:34 year:2003 number:3 pages:0 http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 34 2003 3 0 |
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10.1111/j.1540-5414.2003.02274.x doi (DE-627)NLEJ242373453 DE-627 ger DE-627 rakwb Hahn, Eugene D. verfasserin aut Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process 350 Main Street , Malden , MA 02148 , USA . Decision Sciences 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| Analytic Hierarchy Process In Decision sciences Oxford : Wiley-Blackwell, 1988 34(2003), 3, Seite 0 Online-Ressource (DE-627)NLEJ243926456 (DE-600)2066218-X 1540-5915 nnns volume:34 year:2003 number:3 pages:0 http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 34 2003 3 0 |
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Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process |
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In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. |
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In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. |
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In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ242373453</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707154117.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s2003 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/j.1540-5414.2003.02274.x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ242373453</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="100" ind1="1" ind2=" "><subfield code="a">Hahn, Eugene D.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Decision Making with Uncertain Judgments: A Stochastic Formulation of the Analytic Hierarchy Process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">350 Main Street , Malden , MA 02148 , USA .</subfield><subfield code="b">Decision Sciences</subfield><subfield code="c">2003</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods. The stochastic methods are found to give results that are congruent with those of the eigenvector method in matrices of different sizes and different levels of inconsistency. Moreover, inferential statements can be made about the priorities when the stochastic approach is adopted, and these statements may be of considerable value to a decision maker. The methods described are fully compatible with judgments from the standard version of AHP and can be used to construct a stochastic formulation of it.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2003</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2003||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Analytic Hierarchy Process</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Decision sciences</subfield><subfield code="d">Oxford : Wiley-Blackwell, 1988</subfield><subfield code="g">34(2003), 3, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243926456</subfield><subfield code="w">(DE-600)2066218-X</subfield><subfield code="x">1540-5915</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:34</subfield><subfield code="g">year:2003</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/j.1540-5414.2003.02274.x</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">34</subfield><subfield code="j">2003</subfield><subfield code="e">3</subfield><subfield code="h">0</subfield></datafield></record></collection>
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