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 <titleInfo>
  <title>Risk and decision analysis in projects  :</title>
  <subTitle>3.0 edition</subTitle>
 </titleInfo>
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  <namePart>Schuyler, John</namePart>
  <role>
   <roleTerm type="text">Primary Author</roleTerm>
  </role>
 </name>
 <typeOfResource manuscript="no" collection="yes">mixed material</typeOfResource>
 <genre authority="marcgt">bibliography</genre>
 <originInfo>
  <place>
   <placeTerm type="text">Aurora, Colorado</placeTerm>
   <publisher>Planning Press</publisher>
   <dateIssued>2016</dateIssued>
  </place>
 </originInfo>
 <language>
  <languageTerm type="code">en</languageTerm>
  <languageTerm type="text">English</languageTerm>
 </language>
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  <form authority="gmd">Printed Material</form>
  <extent>xxii, 412 p. : index. ; 28 cm.</extent>
 </physicalDescription>
 <note>Decision analysis (DA) provides assistance in making logical, consistent&#13;
 decisions under uncertainty. This book instructs readers in applying DA&#13;
 to a wide range of project decisions.  This is a wholly rewritten and &#13;
expanded successor to the best-selling prior editions.  The entire asset&#13;
 life cycle is covered, from an initial feasibility analysis, to the &#13;
project plan, to the post-project review, and on to a look-back analysis&#13;
 of the capital investment decision.  The book primarily uses a business&#13;
 investment perspective, and assumes maximizing value for the project &#13;
owner is the objective. However, DA applies to all manner of decisions &#13;
for individuals, government, and non-profit organizations.  DA is a &#13;
problem-solving process. There are three key features: 1) probabilities &#13;
and probability distributions express best judgments about risks and &#13;
uncertainties. 2) The organization has a decision policy expressed as a &#13;
single metric (the objective function). 3) Probabilities and outcome &#13;
values combine in the probability-weighting expected value calculation. &#13;
 This book aims to make decision making clear, simple, and logical. A &#13;
clear decision policy can be elusive, and we offer suggestions for &#13;
making trade-offs among conflicting objectives. Converting the three &#13;
pillars of project management (cost, schedule and performance) into &#13;
project value equivalents makes the trade-offs clear.  This book is &#13;
intended for serious PM students and practitioners. This is an essential&#13;
 concepts and how-to book. The scope is quantitative analysis, from &#13;
project inception to post-project review. Project cost and schedule &#13;
modeling, in modest detail, is essential to feasibility analysis. A &#13;
general background in PM and corporate planning will be helpful.  The &#13;
methods are quantitative and straightforward. We presume the reader is &#13;
comfortable with basic algebra. Experience with spreadsheets will be &#13;
helpful, as Microsoft® Excel® is the basis for several downloadable &#13;
calculation examples.  The book has six pages of Suggested Reading &#13;
annotated references (plus footnote additions), over 500 Glossary &#13;
definitions, and over 2200 Index entries. Online supplements include &#13;
several whitepapers and other documents, example calculation &#13;
spreadsheets, color images of several important figures, four videos &#13;
(including a critical chain simulation), and the Utility Elicitation &#13;
Program (as a web app).  Key topics include: &lt;b&gt;Decision trees&lt;/b&gt; and &lt;b&gt;Monte Carlo simulation&lt;/b&gt; for calculating outcome distributions and expected values • Probability concepts, including Bayes’ rule for &lt;b&gt;value of information analysis&lt;/b&gt; • Popular probability &lt;b&gt;distribution types&lt;/b&gt; and when they apply • &lt;b&gt;Eliciting expert judgments&lt;/b&gt;, with attention to potential cognitive and motivational biases • Recognizing the &lt;b&gt;three pillars&lt;/b&gt;&#13;
 project in terms of project value  • A 10-step decision analysis &#13;
process applicable to typical decisions • Project modeling concepts and &#13;
techniques, with special attention to &lt;b&gt;risk drivers&lt;/b&gt; and other &lt;b&gt;correlations&lt;/b&gt;  • Deterministic and stochastic &lt;b&gt;sensitivity analysis&lt;/b&gt;  • &lt;b&gt;Decision policy&lt;/b&gt;&#13;
 that separates objectives, time value, and risk attitude  • @RISK® with&#13;
 Microsoft® Project for project schedule simulations under uncertainty •&#13;
 Logical, consistent &lt;b&gt;risk policy&lt;/b&gt; expressed as a utility function • &lt;i&gt;Merge bias&lt;/i&gt; when task chains converge at a merge point  • &lt;i&gt;Tail estimate bias&lt;/i&gt; when estimating highly uncertain quantities  • &lt;i&gt;Optimizer’s curse&lt;/i&gt;, a portfolio forecasting bias • &lt;i&gt;Winner’s curse&lt;/i&gt;, a bias characteristic of auctions • Overconfidence and other common &lt;b&gt;cognitive biases&lt;/b&gt; • Using the best of &lt;b&gt;critical chain&lt;/b&gt; and Monte Carlo simulation • &lt;i&gt;Stochastic variance&lt;/i&gt; between a deterministic and a stochastic model • &lt;b&gt;Modeling&lt;/b&gt;&#13;
 risk and uncertainty using probabilities, probability distributions, &#13;
explicit formula relationships, correlation coefficients, risk drivers, &#13;
conditional branching, and rework cycles.&lt;br&gt;</note>
 <note type="statement of responsibility"></note>
 <subject authority="">
  <topic>Project management</topic>
 </subject>
 <subject authority="">
  <topic>Risk analysis</topic>
 </subject>
 <subject authority="">
  <topic>Decision analysis</topic>
 </subject>
 <classification>AH/EEQ/AEC</classification>
 <identifier type="isbn">9780966440164</identifier>
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  <physicalLocation>Perpustakaan - Sekolah Tinggi Manajemen PPM Pusat Informasi Manajemen</physicalLocation>
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