Bayesian strategy assessment in multi‐attribute decision making
Identifieur interne : 001579 ( Istex/Corpus ); précédent : 001578; suivant : 001580Bayesian strategy assessment in multi‐attribute decision making
Auteurs : Arndt Bröder ; Stefanie SchifferSource :
- Journal of Behavioral Decision Making [ 0894-3257 ] ; 2003-07.
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Abstract
Behavioral Decision Research on multi‐attribute decision making is plagued with the problem of drawing inferences from behavioral data on cognitive strategies. This bridging problem has been tackled by a range of methodical approaches, namely Structural Modeling (SM), Process Tracing (PT), and comparative model fitting. Whereas SM and PT have been criticized for a number of reasons, the comparative fitting approach has some theoretical advantages as long as the formal relation between theories and data is specified. A Bayesian method is developed that is able to assess, whether an empirical data vector was most likely generated by a ‘Take The Best’ heuristic (Gigerenzer et al., 1991), by an equal weight rule, or a compensatory strategy. Equations are derived for the two‐ and three‐alternative cases, respectively, and a simulation study supports its validity. The classification also showed convergent validity with Process Tracing measures in an experiment. Potential extensions of the general approach to other applications in behavioral decision research are discussed. Copyright © 2003 John Wiley & Sons, Ltd.
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DOI: 10.1002/bdm.442
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Bayesian strategy assessment in multi‐attribute decision making</title><author><name sortKey="Broder, Arndt" sort="Broder, Arndt" uniqKey="Broder A" first="Arndt" last="Bröder">Arndt Bröder</name><affiliation><mods:affiliation>University of Bonn, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>Psychologisches Institut der Universität Bonn, Römerstrasse 164, D‐53117 Bonn, Germany.</mods:affiliation></affiliation></author><author><name sortKey="Schiffer, Stefanie" sort="Schiffer, Stefanie" uniqKey="Schiffer S" first="Stefanie" last="Schiffer">Stefanie Schiffer</name><affiliation><mods:affiliation>University of Bonn, Germany</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:26EDBA405F928F31FFB27495B23F5695C42AD429</idno><date when="2003" year="2003">2003</date><idno type="doi">10.1002/bdm.442</idno><idno type="url">https://api.istex.fr/document/26EDBA405F928F31FFB27495B23F5695C42AD429/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001579</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001579</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Bayesian strategy assessment in multi‐attribute decision making</title><author><name sortKey="Broder, Arndt" sort="Broder, Arndt" uniqKey="Broder A" first="Arndt" last="Bröder">Arndt Bröder</name><affiliation><mods:affiliation>University of Bonn, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>Psychologisches Institut der Universität Bonn, Römerstrasse 164, D‐53117 Bonn, Germany.</mods:affiliation></affiliation></author><author><name sortKey="Schiffer, Stefanie" sort="Schiffer, Stefanie" uniqKey="Schiffer S" first="Stefanie" last="Schiffer">Stefanie Schiffer</name><affiliation><mods:affiliation>University of Bonn, Germany</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Behavioral Decision Making</title><title level="j" type="abbrev">J. Behav. Decis. Making</title><idno type="ISSN">0894-3257</idno><idno type="eISSN">1099-0771</idno><imprint><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2003-07">2003-07</date><biblScope unit="volume">16</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="193">193</biblScope><biblScope unit="page" to="213">213</biblScope></imprint><idno type="ISSN">0894-3257</idno></series><idno type="istex">26EDBA405F928F31FFB27495B23F5695C42AD429</idno><idno type="DOI">10.1002/bdm.442</idno><idno type="ArticleID">BDM442</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0894-3257</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bayesian methods</term><term>Process Tracing</term><term>Structural Modeling</term><term>cognitive strategies</term><term>multi‐attribute</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Behavioral Decision Research on multi‐attribute decision making is plagued with the problem of drawing inferences from behavioral data on cognitive strategies. This bridging problem has been tackled by a range of methodical approaches, namely Structural Modeling (SM), Process Tracing (PT), and comparative model fitting. Whereas SM and PT have been criticized for a number of reasons, the comparative fitting approach has some theoretical advantages as long as the formal relation between theories and data is specified. A Bayesian method is developed that is able to assess, whether an empirical data vector was most likely generated by a ‘Take The Best’ heuristic (Gigerenzer et al., 1991), by an equal weight rule, or a compensatory strategy. Equations are derived for the two‐ and three‐alternative cases, respectively, and a simulation study supports its validity. The classification also showed convergent validity with Process Tracing measures in an experiment. Potential extensions of the general approach to other applications in behavioral decision research are discussed. Copyright © 2003 John Wiley & Sons, Ltd.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Arndt Bröder</name><affiliations><json:string>University of Bonn, Germany</json:string><json:string>Psychologisches Institut der Universität Bonn, Römerstrasse 164, D‐53117 Bonn, Germany.</json:string></affiliations></json:item><json:item><name>Stefanie Schiffer</name><affiliations><json:string>University of Bonn, Germany</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>multi‐attribute</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>cognitive strategies</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Process Tracing</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Bayesian methods</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Structural Modeling</value></json:item></subject><articleId><json:string>BDM442</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Behavioral Decision Research on multi‐attribute decision making is plagued with the problem of drawing inferences from behavioral data on cognitive strategies. This bridging problem has been tackled by a range of methodical approaches, namely Structural Modeling (SM), Process Tracing (PT), and comparative model fitting. Whereas SM and PT have been criticized for a number of reasons, the comparative fitting approach has some theoretical advantages as long as the formal relation between theories and data is specified. A Bayesian method is developed that is able to assess, whether an empirical data vector was most likely generated by a ‘Take The Best’ heuristic (Gigerenzer et al., 1991), by an equal weight rule, or a compensatory strategy. Equations are derived for the two‐ and three‐alternative cases, respectively, and a simulation study supports its validity. The classification also showed convergent validity with Process Tracing measures in an experiment. Potential extensions of the general approach to other applications in behavioral decision research are discussed. 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Zucchini</name></json:item></author><host><volume>44</volume><pages><last>61</last><first>41</first></pages><author></author><title>Journal of Mathematical Psychology</title></host><title>An introduction to model selection</title></json:item></refBibs><genre><json:string>article</json:string></genre><host><volume>16</volume><publisherId><json:string>BDM</json:string></publisherId><pages><total>21</total><last>213</last><first>193</first></pages><issn><json:string>0894-3257</json:string></issn><issue>3</issue><subject><json:item><value>Research Article</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1099-0771</json:string></eissn><title>Journal of Behavioral Decision Making</title><doi><json:string>10.1002/(ISSN)1099-0771</json:string></doi></host><categories><wos><json:string>social science</json:string><json:string>psychology, applied</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>psychology & cognitive sciences</json:string><json:string>social psychology</json:string></scienceMetrix></categories><publicationDate>2003</publicationDate><copyrightDate>2003</copyrightDate><doi><json:string>10.1002/bdm.442</json:string></doi><id>26EDBA405F928F31FFB27495B23F5695C42AD429</id><score>0.022511933</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/26EDBA405F928F31FFB27495B23F5695C42AD429/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/26EDBA405F928F31FFB27495B23F5695C42AD429/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/26EDBA405F928F31FFB27495B23F5695C42AD429/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Bayesian strategy assessment in multi‐attribute decision making</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><availability><p>Copyright © 2003 John Wiley & Sons, Ltd.</p></availability><date>2003</date></publicationStmt><notesStmt><note>Deutsche Forschungsgemeinschaft - No. Br 2130/1‐1;</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Bayesian strategy assessment in multi‐attribute decision making</title><author xml:id="author-1"><persName><forename type="first">Arndt</forename><surname>Bröder</surname></persName><affiliation>University of Bonn, Germany</affiliation><affiliation>Psychologisches Institut der Universität Bonn, Römerstrasse 164, D‐53117 Bonn, Germany.</affiliation></author><author xml:id="author-2"><persName><forename type="first">Stefanie</forename><surname>Schiffer</surname></persName><affiliation>University of Bonn, Germany</affiliation></author></analytic><monogr><title level="j">Journal of Behavioral Decision Making</title><title level="j" type="abbrev">J. Behav. Decis. Making</title><idno type="pISSN">0894-3257</idno><idno type="eISSN">1099-0771</idno><idno type="DOI">10.1002/(ISSN)1099-0771</idno><imprint><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2003-07"></date><biblScope unit="volume">16</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="193">193</biblScope><biblScope unit="page" to="213">213</biblScope></imprint></monogr><idno type="istex">26EDBA405F928F31FFB27495B23F5695C42AD429</idno><idno type="DOI">10.1002/bdm.442</idno><idno type="ArticleID">BDM442</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2003</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="fr"><p>Behavioral Decision Research on multi‐attribute decision making is plagued with the problem of drawing inferences from behavioral data on cognitive strategies. 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This bridging problem has been tackled by a range of methodical approaches, namely Structural Modeling (SM), Process Tracing (PT), and comparative model fitting. Whereas SM and PT have been criticized for a number of reasons, the comparative fitting approach has some theoretical advantages as long as the formal relation between theories and data is specified. A Bayesian method is developed that is able to assess, whether an empirical data vector was most likely generated by a ‘Take The Best’ heuristic (Gigerenzer et al., 1991), by an equal weight rule, or a compensatory strategy. Equations are derived for the two‐ and three‐alternative cases, respectively, and a simulation study supports its validity. The classification also showed convergent validity with Process Tracing measures in an experiment. Potential extensions of the general approach to other applications in behavioral decision research are discussed. Copyright © 2003 John Wiley & Sons, Ltd.</abstract><note type="funding">Deutsche Forschungsgemeinschaft - No. Br 2130/1‐1; </note><subject lang="en"><genre>keywords</genre><topic>multi‐attribute</topic><topic>cognitive strategies</topic><topic>Process Tracing</topic><topic>Bayesian methods</topic><topic>Structural Modeling</topic></subject><relatedItem type="host"><titleInfo><title>Journal of Behavioral Decision Making</title></titleInfo><titleInfo type="abbreviated"><title>J. Behav. Decis. 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