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  <pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/281/A-Bayesian-network-based-framework-for-multi-criteria-decision-making">
    <rdfs:label><![CDATA[A Bayesian network based framework for multi-criteria decision making]]></rdfs:label>
    <pub:title><![CDATA[A Bayesian network based framework for multi-criteria decision making]]></pub:title>
    <pub:publishedOn rdf:datatype="&xsd;dateTime">2004-08-06T00:00:00-05:00</pub:publishedOn>
    <pub:abstract><![CDATA[Multi-Criteria Decision Making (MCDM) involves the selection of
the best actions from a set of alternatives, each of which is
evaluated against multiple, and often conflicting, criteria. Most
of the existing MCDM methods only focus on decisions under
certainty. The criteria were evaluated separately as if they were
independent of each other. Complex, often uncertain interactions
between criteria, and between criteria and other factors are not
modeled in a coherent and systematic manner. To address these
issues, we propose in this paper a decision framework based on
Bayesian networks (BN) and influence diagram (ID) to structure and
manage MCDM problems with explicit modeling of uncertain
interactions among entities of interest. In this framework, a
decision problem is represented by an ID where each decision node
represents the set of alternatives for a decision, a utility node
represents the set of objectives (decision maker’s preferences),
decision criteria and internal or external factors that may affect
the criteria are represented by chance nodes. Interdependencies
among these nodes are qualitatively modeled by the links in the
diagram and quantitatively by conditional probability tables (CPT)
associated with each of the chance nodes and the utility node. The
joint probability distribution, which is compactly captured by the
network structure and CPT, encodes the domain expert’s knowledge
of interdependency between variables. The decision problem is then
treated as an optimization problem: recommend the decision
alternative which optimizes the expected utility, given
observations of some external factors and preferences made by the
decision maker. Various algorithms developed for BN and ID can be
employed to automatically solve this problem. The steps that need
to be taken to model a MCDM problem as an ID is presented,
illustrated with a running example. Other related issues are also
discussed. Our preliminary work indicates that this framework is
of great potential as a modeling tool to support MCDM decision
making in an uncertain environment.
]]></pub:abstract>
    <pub:address><![CDATA[Whistler, British Columbia CA]]></pub:address>
    <pub:counter>2928</pub:counter>
    <pub:googleKey>58ILnQRMCjMJ</pub:googleKey>
    <pub:googleCitations>6</pub:googleCitations>
    <pub:tag><![CDATA[uncertainty]]></pub:tag>
    <pub:tag><![CDATA[bayesian reasoning]]></pub:tag>
    <pub:tag><![CDATA[bayesian reasoning]]></pub:tag>
    <pub:tag><![CDATA[decision support]]></pub:tag>
    <pub:booktitle><![CDATA[Proceedings of the 17th International Conference on Multiple Criteria Decision Analysis]]></pub:booktitle>
    <pub:author>
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         <rdf:first><person:PhDStudent rdf:about="http://ebiquity.umbc.edu/person/html/Wiboonsak/Watthayu/"><person:name><![CDATA[Wiboonsak  Watthayu]]></person:name><rdfs:label><![CDATA[Wiboonsak  Watthayu]]></rdfs:label></person:PhDStudent></rdf:first>
         <rdf:rest>
           <rdf:List>
             <rdf:first><person:PrincipalFaculty rdf:about="http://ebiquity.umbc.edu/person/html/Yun/Peng/"><person:name><![CDATA[Yun  Peng]]></person:name><rdfs:label><![CDATA[Yun  Peng]]></rdfs:label></person:PrincipalFaculty></rdf:first>
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    </pub:author>
    <pub:firstAuthor><person:PhDStudent rdf:about="http://ebiquity.umbc.edu/person/html/Wiboonsak/Watthayu/"><person:name><![CDATA[Wiboonsak  Watthayu]]></person:name><rdfs:label><![CDATA[Wiboonsak  Watthayu]]></rdfs:label></person:PhDStudent></pub:firstAuthor>
    <pub:softCopy><pub:SoftCopy>
      <pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
      <pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/235.pdf]]></pub:softCopyURI>
      <pub:softCopySize>278896</pub:softCopySize>
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  </pub:InProceedings>

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