| 000 | 21969cam a2205209Ii 4500 | ||
|---|---|---|---|
| 001 | 2009 | ||
| 008 | 121210s2014 ohua b 001 0 eng | ||
| 020 | _a0538797576 (hardback) | ||
| 020 | _a9780538797573 (hardback) | ||
| 040 | _erda | ||
| 049 | _aTR-IsMEF | ||
| 050 | 0 | 0 |
_aHD30.23 _b.C54 2014 |
| 100 | 1 |
_aClemen, Robert T. _q(Robert Taylor), _d1952-, _eauthor. |
|
| 245 | 1 | 0 |
_aMaking hard decisions with DecisionTools / _cRobert T. Clemen, Fuqua School of Business Duke University, Terence Reilly, Babson College ; with contributions by Samuel E. Bodily and Jeffrey Guyse ; and cases by Samuel E. Bodily, Dana Clyman, Sherwood C. Frey, Jr., and Phillip E. Pfeier |
| 250 | _aThird edition. | ||
| 264 |
_aMason, OH : _bSouth-Western, Cengage Learning, _c2014. |
||
| 264 | _a©2014. | ||
| 300 |
_axxvi, 816 pages : _billustrations ; _c24 cm. |
||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aunmediated _2rdamedia |
||
| 338 |
_avolume _2rdacarrier |
||
| 500 | _a"Now with Darden cases."--Cover. | ||
| 504 | _aIncludes bibliographical references and indexes. | ||
| 596 | _a1 | ||
| 630 | 0 | 0 | _aDecisionTools. |
| 650 | 0 | _aDecision making. | |
| 650 | 0 |
_aDecision making _xComputer programs. |
|
| 700 | 1 |
_aReilly, Terence, _eauthor. |
|
| 700 | 1 |
_aBodily, Samuel E., _econtributor. |
|
| 700 | 1 |
_aGuyse, Jeffery, _econtributor. |
|
| 900 | _aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules | ||
| 910 | _aPandora. | ||
| 942 |
_2lcc _cBKS |
||
| 970 | 0 | 1 |
_aPreface, _pxxi. |
| 970 | 1 | 2 |
_tIntroduction to decision analysis, _p1. |
| 970 | 1 | 1 |
_tWhy are decisions hard?, _p3. |
| 970 | 1 | 1 |
_tWhy study decision analysis?, _p5. |
| 970 | 1 | 1 |
_tSubjective judgements and decision making, _p7. |
| 970 | 1 | 1 |
_tThe decision analysis process, _p8. |
| 970 | 1 | 1 |
_tReguisite decision models, _p11. |
| 970 | 1 | 1 |
_tWhere is decision analysis used?, _p11. |
| 970 | 1 | 1 |
_tWhere does the software fit in?, _p12. |
| 970 | 1 | 1 |
_tWhere are we going from here?, _p14. |
| 970 | 0 | 1 |
_aSummary, _p14. |
| 970 | 0 | 1 |
_aQuestions and problems, _p15. |
| 970 | 1 | 1 |
_tCase studies commercial space travel, _p16. |
| 970 | 1 | 1 |
_tDupont and chlorofluorocarbons, _p17. |
| 970 | 1 | 1 |
_tChoosing a vice-presidential candidate, _p17. |
| 970 | 0 | 1 |
_aReferences, _p18. |
| 970 | 0 | 1 |
_aEpilogue, _p19. |
| 970 | 1 | 2 |
_tModeling decisions, _p21. |
| 970 | 1 | 2 |
_tElements of decision problems, _p23. |
| 970 | 1 | 1 |
_tValues and objectives, _p23. |
| 970 | 1 | 1 |
_tMaking money: a special objective, _p24. |
| 970 | 1 | 1 |
_tValues and the current decision context, _p25. |
| 970 | 1 | 1 |
_tDecisions to make, _p27. |
| 970 | 1 | 1 |
_tSequential decisions, _p28. |
| 970 | 1 | 1 |
_tUncertain events, _p29. |
| 970 | 1 | 1 |
_tConsequences, _p31. |
| 970 | 1 | 1 |
_tThe time value of money: a special kind of trade-off, _p33. |
| 970 | 0 | 1 |
_aSummary, _p40. |
| 970 | 0 | 1 |
_aQuestions and problems, _p40. |
| 970 | 1 | 1 |
_tCase studies The value of patience, _p42. |
| 970 | 1 | 1 |
_tEarly bird, inc, _p43. |
| 970 | 0 | 1 |
_aReferences, _p44. |
| 970 | 0 | 1 |
_aEpilogue, _p45. |
| 970 | 1 | 2 |
_tStructuring decisions, _p46. |
| 970 | 1 | 1 |
_tStructuring values, _p47. |
| 970 | 1 | 1 |
_tFundamental and means objectives, _p49. |
| 970 | 1 | 1 |
_tGetting the decision context right, _p53. |
| 970 | 1 | 1 |
_tStructuring decisions: influence diagrams, _p56. |
| 970 | 1 | 1 |
_tInfluence diagrams and the fundamental objectives hierarchy, _p58. |
| 970 | 1 | 1 |
_tUsing arcs to represent relationships, _p60. |
| 970 | 1 | 1 |
_tSome basic influence diagrams, _p60. |
| 970 | 1 | 1 |
_tThe basic risky decision, _p61. |
| 970 | 1 | 1 |
_tImperfect information, _p62. |
| 970 | 1 | 1 |
_tSequential decisions, _p65. |
| 970 | 1 | 1 |
_tIntermediate calculations, _p67. |
| 970 | 1 | 1 |
_tConstructing an influence diagram, _p69. |
| 970 | 1 | 1 |
_tSome common mistakes, _p71. |
| 970 | 1 | 1 |
_tMultiple representations and requisite models, _p72. |
| 970 | 1 | 1 |
_tStructuring decisions: decision trees, _p73. |
| 970 | 1 | 1 |
_tDecision trees and the objectives hierarchy, _p75. |
| 970 | 1 | 1 |
_tBasic decision trees, _p76. |
| 970 | 1 | 1 |
_tThe basic risky decision, _p76. |
| 970 | 1 | 1 |
_tImperfect information, _p77. |
| 970 | 1 | 1 |
_tSequential decisions, _p78. |
| 970 | 1 | 1 |
_tDecision trees and influence diagrams compared, _p79. |
| 970 | 1 | 1 |
_tDecision details: defining elements of the decision, _p80. |
| 970 | 1 | 1 |
_tMore decision details: cash flows and probabilities, _p82. |
| 970 | 1 | 1 |
_tDefining measurement scales for fundamental objectives, _p83. |
| 970 | 1 | 1 |
_tUsing precision tree for structuring decisions, _p89. |
| 970 | 1 | 1 |
_tConstructing a decision tree for the research-and-development decision, _p89. |
| 970 | 1 | 1 |
_tConstructing an influence diagram for the basic risky decision, _p97. |
| 970 | 0 | 1 |
_aSummary, _p104. |
| 970 | 0 | 1 |
_aExercises, _p105. |
| 970 | 0 | 1 |
_aQuestions and problems, _p107. |
| 970 | 1 | 1 |
_tCase study precribed fire, _p114. |
| 970 | 1 | 1 |
_tThe SS kuniang, _p114. |
| 970 | 1 | 1 |
_tThe hillblom estate, part I, _p115. |
| 970 | 0 | 1 |
_aReferences, _p116. |
| 970 | 0 | 1 |
_aEpilogue, _p117. |
| 970 | 1 | 2 |
_tMaking choices, _p118. |
| 970 | 1 | 1 |
_tDecision trees and expected monetary value, _p122. |
| 970 | 1 | 1 |
_tSolving influence diagrams: overview, _p127. |
| 970 | 1 | 1 | _tSolving influence diagrans: the details online only at www.cengagebrain.com. |
| 970 | 1 | 1 |
_tRisk profiles, _p129. |
| 970 | 1 | 1 |
_tCumulative risk profiles, _p133. |
| 970 | 1 | 1 |
_tDominance: an alternative to EMV, _p135. |
| 970 | 1 | 1 |
_tMaking decisions with multiple objectives, _p139. |
| 970 | 1 | 1 |
_tAnalysis: on objective at a time, _p140. |
| 970 | 1 | 1 |
_tSubjective rattings for constructed attribute scales, _p142. |
| 970 | 1 | 1 |
_tAssessing trade-off weights, _p143. |
| 970 | 1 | 1 |
_tAnalysis: expected values and risk profiles for two objectives, _p145. |
| 970 | 1 | 1 |
_tDecision analysis using precision tree, _p147. |
| 970 | 1 | 1 |
_tDecision tree, _p148. |
| 970 | 1 | 1 |
_tInfluence diagrams, _p154. |
| 970 | 1 | 1 |
_tMultiple-attribute models, _p158. |
| 970 | 0 | 1 |
_aSummary, _p162. |
| 970 | 0 | 1 |
_aExercises, _p162. |
| 970 | 0 | 1 |
_aQuestions and problems, _p164. |
| 970 | 1 | 1 |
_tCase studies Southern electronics, Part I, _p170. |
| 970 | 1 | 1 |
_tSouthern electronics, Part II, _p170. |
| 970 | 1 | 1 |
_tStrenlar, _p171. |
| 970 | 1 | 1 |
_tJob offers, _p172. |
| 970 | 1 | 1 |
_tSS kuniang, Part II, _p173. |
| 970 | 1 | 1 |
_tMarketing specialists, Ltd., _p174. |
| 970 | 0 | 1 |
_aReferences, _p176. |
| 970 | 0 | 1 |
_aEpilogue, _p176. |
| 970 | 1 | 2 |
_tSensitivity analysis, _p177. |
| 970 | 1 | 1 |
_tSensitivity analysis: a modeling approach, _p180. |
| 970 | 1 | 1 |
_tProblem identification and structure, _p180. |
| 970 | 1 | 1 |
_tOne-way sensitivity analysis: sensitivity graphs, _p188. |
| 970 | 1 | 1 |
_tOne-way sensitivity analysis: tornado diagrams, _p191. |
| 970 | 1 | 1 |
_tDominance considerations, _p194. |
| 970 | 1 | 1 |
_tTwo-way sensitivity analysis, _p196. |
| 970 | 1 | 1 |
_tSensitivity to probabilities, _p200. |
| 970 | 1 | 1 |
_tSensitivity to probabilities-house-hunting, _p203. |
| 970 | 1 | 1 |
_tSensitivity analysis in action, _p210. |
| 970 | 1 | 1 |
_tSensitivity analysis: a built-in irony, _p212. |
| 970 | 1 | 1 |
_tSensitivity analysis using excel® and precisiontree, _p212. |
| 970 | 0 | 1 |
_aSummary, _p223. |
| 970 | 0 | 1 |
_aExercises, _p223. |
| 970 | 0 | 1 |
_aQuestions and problems, _p224. |
| 970 | 1 | 1 |
_tCase studies dumond international, Part I, _p228. |
| 970 | 1 | 1 |
_tStrenlar, Part II, _p229. |
| 970 | 1 | 1 |
_tJob offers, Part II, _p230. |
| 970 | 1 | 1 |
_tThe hillblom estate, Part II, _p230. |
| 970 | 1 | 1 |
_tManpads, _p230. |
| 970 | 0 | 1 |
_aReferences, _p232. |
| 970 | 1 | 2 |
_tOrganizational use of decision analysis, _p233. |
| 970 | 1 | 1 |
_tThe decision-making process, _p234. |
| 970 | 1 | 1 |
_tA six-step decision process: the lacing diagram, _p234. |
| 970 | 1 | 1 |
_tOrganizational issues in enhancing creativity and enabling choices, _p239. |
| 970 | 1 | 1 |
_tDeveloping alternatives: understanding the creative process, _p241. |
| 970 | 1 | 1 |
_tValue-focused thinking for creating alternatives, _p243. |
| 970 | 1 | 1 |
_tFundamental objectives, _p243. |
| 970 | 1 | 1 |
_tMeans objectives, _p244. |
| 970 | 1 | 1 |
_tStrategy tables, _p246. |
| 970 | 1 | 1 | _tBlocks to creativity and additional creativity techniques online only at www.cengagebrain.com. |
| 970 | 1 | 1 |
_tManaging and monitoring the six-step decision process, _p254. |
| 970 | 1 | 1 |
_tOther examples, _p255. |
| 970 | 0 | 1 |
_aSummary, _p256. |
| 970 | 0 | 1 |
_aQuestions and problems, _p257. |
| 970 | 1 | 1 |
_tCase study eastman kodak, _p258. |
| 970 | 0 | 1 |
_aReferences, _p259. |
| 970 | 0 | 1 |
_aEpilogue, _p250. |
| 970 | 1 | 2 |
_tCases, _p261. |
| 970 | 1 | 1 |
_tAthens glass works, _p261. |
| 970 | 1 | 1 |
_tIntegrated sitting systems, Inc, _p263. |
| 970 | 1 | 1 |
_tInternational guidance and controls, _p266. |
| 970 | 1 | 1 |
_tGeorge's t-shirts, _p267. |
| 970 | 1 | 2 |
_tModeling uncertainty, _p269. |
| 970 | 1 | 2 |
_tProbability basics, _p271. |
| 970 | 1 | 1 |
_tA little probability theory, _p271. |
| 970 | 1 | 1 |
_tVenn diagrams, _p272. |
| 970 | 1 | 1 |
_tMore probability formulas, _p273. |
| 970 | 1 | 1 |
_tPrecision tree® and bayers' theorem, _p279. |
| 970 | 1 | 1 |
_tUncertain quantities, _p279. |
| 970 | 1 | 1 |
_tDiscrete probability distributions, _p280. |
| 970 | 1 | 1 |
_tExpected value, _p282. |
| 970 | 1 | 1 |
_tVariance anc standard deviation, _p285. |
| 970 | 1 | 1 |
_tContinuous probability distributions, _p288. |
| 970 | 1 | 1 |
_tStochastic dominance revisited, _p290. |
| 970 | 1 | 1 |
_tProbability density functions, _p290. |
| 970 | 1 | 1 |
_tExpected value, variance, and standard deviation: the continuos case, _p291. |
| 970 | 1 | 1 | _tCorrelation and covariance for measuring dependence online only at www.cengagebrain.com. |
| 970 | 1 | 1 |
_tOil wildcatting, _p293. |
| 970 | 1 | 1 |
_tJohn Hinckley's trial, _p299. |
| 970 | 0 | 1 |
_aSummary, _p301. |
| 970 | 0 | 1 |
_aExercises, _p301. |
| 970 | 0 | 1 |
_aQuestions and problems, _p395. |
| 970 | 1 | 1 |
_tCase studies Decision analysis monthly, _p308. |
| 970 | 1 | 1 |
_tScreening for colorectal cancer, _p309. |
| 970 | 1 | 1 |
_tAIDS, _p310. |
| 970 | 1 | 1 |
_tDiscrimination and the death penalty, _p312. |
| 970 | 0 | 1 |
_aReferences, _p313. |
| 970 | 0 | 1 |
_aEpilogue, _p313. |
| 970 | 1 | 2 |
_tSubjective probability, _p315. |
| 970 | 1 | 1 |
_tUncertainty and public policy, _p315. |
| 970 | 1 | 1 |
_tProbability: a subjective interpretation, _p317. |
| 970 | 1 | 1 |
_tAssessing discrete probabilities, _p319. |
| 970 | 1 | 1 |
_tAssessing continuous probabilities, _p323. |
| 970 | 1 | 1 |
_tHeuristic and biases in probability assessment, _p330. |
| 970 | 1 | 1 |
_tMemory biases, _p332. |
| 970 | 1 | 1 |
_tStatistical biases, _p334. |
| 970 | 1 | 1 |
_tConfidence biases, _p336. |
| 970 | 1 | 1 |
_tAdjustment heuristics and biases, _p336. |
| 970 | 1 | 1 |
_tMotivational bias, _p338. |
| 970 | 1 | 1 |
_tHeuristics and biases: implications, _p338. |
| 970 | 1 | 1 |
_tDecomposition and probabillity assessment, _p339. |
| 970 | 1 | 1 |
_tExperts and probability assessment: pulling it all together, _p344. |
| 970 | 1 | 1 |
_tConstructing distributions using @RISK, _p350. |
| 970 | 1 | 1 | _tCoherence and the dutch book online only at www.cengagebrain.com. |
| 970 | 0 | 1 |
_aSummary, _p354. |
| 970 | 0 | 1 |
_aExercises, _p355. |
| 970 | 0 | 1 |
_aQuestions and problems, _p356. |
| 970 | 1 | 1 |
_tCase studies assessing cancer risk-froum mouse to man, _p361. |
| 970 | 1 | 1 |
_tBreast implants, _p362. |
| 970 | 1 | 1 |
_tThe space shuttle challenger, _p363. |
| 970 | 0 | 1 |
_aReferences, _p365. |
| 970 | 0 | 1 |
_aEpilogue, _p366. |
| 970 | 1 | 2 |
_tTheoretical probability models, _p367. |
| 970 | 1 | 1 |
_tThe binomial distribution, _p369. |
| 970 | 1 | 1 |
_tThe posson distribution, _p377. |
| 970 | 1 | 1 |
_tThe exponential distribution, _p382. |
| 970 | 1 | 1 |
_tThe normal distribution, _p385. |
| 970 | 1 | 1 |
_tThe triangular distribution, _p390. |
| 970 | 1 | 1 |
_tThe beta distribution, _p392. |
| 970 | 0 | 1 |
_aSummary, _p399. |
| 970 | 0 | 1 |
_aExercises, _p400. |
| 970 | 0 | 1 |
_aQuestions and problems, _p401. |
| 970 | 1 | 1 |
_tCase studies overbooking, _p411. |
| 970 | 1 | 1 |
_tEarthqueake prediction, _p412. |
| 970 | 1 | 1 |
_tMunicipal solid waste, _p414. |
| 970 | 0 | 1 |
_aReferences, _p416. |
| 970 | 0 | 1 |
_aEpilogue, _p417. |
| 970 | 1 | 2 |
_tUsing data, _p418. |
| 970 | 1 | 1 |
_tUsing data to construct probability distributions, _p418. |
| 970 | 1 | 1 |
_tEmprical CDFs, _p422. |
| 970 | 1 | 1 |
_tUsing data to fit theoretical probability models, _p428. |
| 970 | 1 | 1 |
_tUsing @RISK to fit distributions to data, _p431. |
| 970 | 1 | 1 |
_tUsing data to model relationships, _p443. |
| 970 | 1 | 1 |
_tThe regression approach, _p447. |
| 970 | 1 | 1 |
_tAssumption 1, _p447. |
| 970 | 1 | 1 |
_tAssumption 2, _p450. |
| 970 | 1 | 1 |
_tEstimation: the basics, _p452. |
| 970 | 1 | 1 |
_tEstimation: more than one conditioning variable, _p459. |
| 970 | 1 | 1 |
_tRegression analysis and modeling: some Do's and don't's, _p465. |
| 970 | 1 | 1 |
_tRegression analysis: some bells and whistles, _p467. |
| 970 | 1 | 1 |
_tRegression modeling: decision analysis versus statistical inference, _p470. |
| 970 | 1 | 1 |
_tAn admonition: use with care, _p471. |
| 970 | 1 | 1 | _tNatural conjugate distributions online only at www.cengagebrain.com. |
| 970 | 0 | 1 |
_aSummary, _p471. |
| 970 | 0 | 1 |
_aExercises, _p471. |
| 970 | 0 | 1 |
_aQuestions and problems, _p472. |
| 970 | 1 | 1 |
_tCase studies taco shells, _p479. |
| 970 | 0 | 1 |
_aReferences, _p480. |
| 970 | 0 | 1 |
_aEpilogue: solar trash compactors, _p480. |
| 970 | 1 | 2 |
_tSimulation, _p481. |
| 970 | 1 | 1 |
_tMechanics of simulation, _p483. |
| 970 | 1 | 1 |
_tSampling from porbability distributions, _p486. |
| 970 | 1 | 1 |
_tSimulation models, _p488. |
| 970 | 1 | 1 |
_tSimulating the model, _p492. |
| 970 | 1 | 1 |
_tExamples of simulation models, _p501. |
| 970 | 1 | 1 |
_tProbability models, _p501. |
| 970 | 1 | 1 |
_tA capital budgeting model, _p504. |
| 970 | 1 | 1 |
_tStock price model, _p506. |
| 970 | 1 | 1 |
_tSimulating spreadsheet models using @RISK, _p511. |
| 970 | 1 | 1 |
_tCorrelations among random variables, _p516. |
| 970 | 1 | 1 |
_tSequential simulations, _p520. |
| 970 | 1 | 1 |
_tSimulation, decision trees, and influence diagrams, _p522. |
| 970 | 0 | 1 |
_aSummary, _p523. |
| 970 | 0 | 1 |
_aExercises, _p523. |
| 970 | 0 | 1 |
_aQuestions and problems, _p524. |
| 970 | 1 | 1 |
_tCase studies choosing a manufacturing process, _p526. |
| 970 | 1 | 1 |
_tLa hacienda musa, _p527. |
| 970 | 1 | 1 |
_tOverbooking, Part III, _p529. |
| 970 | 0 | 1 |
_aReferences, _p529. |
| 970 | 0 | 1 |
_aEpilogue, _p530. |
| 970 | 1 | 2 |
_aValue of information, _p531. |
| 970 | 1 | 1 |
_tValue of information: some basic ideas, _p532. |
| 970 | 1 | 1 |
_tProbability and perfect information, _p532. |
| 970 | 1 | 1 |
_tThe expected value of information, _p535. |
| 970 | 1 | 1 |
_tExpected value of perfect information, _p536. |
| 970 | 1 | 1 |
_tExpected value of imperfect information, _p538. |
| 970 | 1 | 1 |
_tValue of information in complex problems, _p544. |
| 970 | 1 | 1 |
_tValue of information, sensitivity analysis, and structuring, _p545. |
| 970 | 1 | 1 |
_tValue of information and nonmonetary obkectives, _p547. |
| 970 | 1 | 1 |
_tValue of information and experts, _p548. |
| 970 | 1 | 1 |
_tCalculating EVPI and EVII with precisiontree, _p548. |
| 970 | 1 | 1 |
_tEVPI, _p548. |
| 970 | 1 | 1 |
_tInfluence diagrams, _p549. |
| 970 | 1 | 1 |
_tDecision trees, _p550. |
| 970 | 1 | 1 |
_tEVII, _p552. |
| 970 | 0 | 1 |
_aSummary, _p553. |
| 970 | 0 | 1 |
_aExercises, _p554. |
| 970 | 0 | 1 |
_aQuestions and problems, _p555. |
| 970 | 1 | 1 |
_tCase studies Texaco-Pennzoil revisited, _p558. |
| 970 | 1 | 1 |
_tMedical tests, _p558. |
| 970 | 1 | 1 |
_tDumond international Part II, _p559. |
| 970 | 0 | 1 |
_aReferences, _p559. |
| 970 | 1 | 2 |
_tReal options, _p561. |
| 970 | 1 | 1 |
_tOption basics, _p563. |
| 970 | 1 | 1 |
_tFinancial options: a brief tutorial, _p564. |
| 970 | 1 | 1 |
_tReal options, _p568. |
| 970 | 1 | 1 |
_tAn approach to valuing real options, _p570. |
| 970 | 1 | 1 |
_tDiscrete uncertainties and choices: decision trees, _p570. |
| 970 | 1 | 1 |
_tContinuos uncertainties and discrete choices: spreadsheet simulation, _p573. |
| 970 | 1 | 1 |
_tOptionality and proteiz, _p574. |
| 970 | 1 | 1 |
_tA trigger value for deciding, _p577. |
| 970 | 1 | 1 |
_tValuing the abandon option, _p578. |
| 970 | 1 | 1 |
_tValuing the scale-up option, _p581. |
| 970 | 1 | 1 |
_tReview of the approach for continuous uncertainties, _p588. |
| 970 | 1 | 1 |
_tComprasion with real option valuation from financial theory, _p588. |
| 970 | 1 | 1 |
_tWhat discount rate?, _p589. |
| 970 | 1 | 1 |
_tFinding optimal decision values using RISK optimizer, _p590. |
| 970 | 0 | 1 |
_aSummary, _p595. |
| 970 | 0 | 1 |
_aExercises, _p595. |
| 970 | 0 | 1 |
_aQuestions and problems, _p597. |
| 970 | 0 | 1 |
_aReferences, _p602. |
| 970 | 1 | 2 |
_tCases, _p603. |
| 970 | 1 | 1 |
_tLAC Leman festival de la musique (A), _p603. |
| 970 | 1 | 1 |
_tLAC Leman festival de la musique (B), _p605. |
| 970 | 1 | 1 |
_tSprigg Lane (A), _p606. |
| 970 | 1 | 1 |
_tAppshop, Inc., _p614. |
| 970 | 1 | 1 |
_tCalambra olive oil (A), _p615. |
| 970 | 1 | 1 |
_tCalambra olive oil (B), _p626. |
| 970 | 1 | 1 |
_tSCOR-eStore.com, _p629. |
| 970 | 1 | 2 |
_tModeling preferences, _p635. |
| 970 | 1 | 2 |
_tRisk attitudes, _p637. |
| 970 | 1 | 1 |
_tRisk, _p639. |
| 970 | 1 | 1 |
_tRisk attitudes, _p641. |
| 970 | 1 | 1 |
_tInvesting in the stock market, revisited, _p643. |
| 970 | 1 | 1 |
_tExpected utility, certainty equivalents, and risk premiums, _p645. |
| 970 | 1 | 1 |
_tKeeping terms straight, _p649. |
| 970 | 1 | 1 |
_tUtility function assessment, _p649. |
| 970 | 1 | 1 |
_tAssessment using certainty equivalents, _p650. |
| 970 | 1 | 1 |
_tAssessment using probabilities, _p652. |
| 970 | 1 | 1 |
_tAssessment using tradeoffs, _p653. |
| 970 | 1 | 1 |
_tGambles, lotteries, and investments, _p654. |
| 970 | 1 | 1 |
_tRisk tolerance and the exponential utility function, _p654. |
| 970 | 1 | 1 |
_tPitfalls in utility assessment: biases in the CE, PE and TO methods, _p657. |
| 970 | 1 | 1 |
_tThe endowment effect, _p658. |
| 970 | 1 | 1 |
_tPreference reversals, _p658. |
| 970 | 1 | 1 |
_tImplications for assessing utilities, _p659. |
| 970 | 1 | 1 |
_tModeling preferences using precisiontree, _p660. |
| 970 | 1 | 1 |
_tDecreasing and constant risk aversion, _p664. |
| 970 | 1 | 1 |
_tDecreasing risk aversion, _p665. |
| 970 | 1 | 1 |
_tAn enterpreneurial example, _p665. |
| 970 | 1 | 1 |
_tConstant risk aversion, _p667. |
| 970 | 1 | 1 |
_tSome caveats, _p669. |
| 970 | 0 | 1 |
_aSummary, _p670. |
| 970 | 0 | 1 |
_aExercises, _p670. |
| 970 | 0 | 1 |
_aQuestions and problems, _p672. |
| 970 | 1 | 1 |
_tCase studies interplants, Inc, _p680. |
| 970 | 1 | 1 |
_tStrenlaer, Part III, _p681. |
| 970 | 0 | 1 |
_aReferences, _p681. |
| 970 | 0 | 1 |
_aEpilogue, _p682. |
| 970 | 1 | 2 |
_tUtility axioms, paradoxes, and implications, _p683. |
| 970 | 1 | 1 |
_tAxioms for expected utility, _p684. |
| 970 | 1 | 1 |
_tParadoxes, _p691. |
| 970 | 1 | 1 |
_tHedonic framming, _p696. |
| 970 | 1 | 1 |
_tFailure to ignore sunk costs, _p697. |
| 970 | 1 | 1 |
_tStatus quo bias, _p698. |
| 970 | 1 | 1 |
_tImplications, _p698. |
| 970 | 1 | 1 |
_tImplications for utility assessment, _p698. |
| 970 | 1 | 1 |
_tManagerial and policy implications, _p700. |
| 970 | 1 | 1 |
_tA final perspective, _p702. |
| 970 | 0 | 1 |
_aSummary, _p703. |
| 970 | 0 | 1 |
_aExercises, _p703. |
| 970 | 0 | 1 |
_aQuestions and problems, _p704. |
| 970 | 1 | 1 |
_tCase studies The life insurance game, _p708. |
| 970 | 1 | 1 |
_tNuclear power paranoia, _p709. |
| 970 | 1 | 1 |
_tThe manager's perspective, _p709. |
| 970 | 0 | 1 |
_aReferences, _p709. |
| 970 | 0 | 1 |
_aEpilogue, _p712. |
| 970 | 1 | 2 |
_tConflicting objectives I: fundamental objectives and the additive utility function, _p713. |
| 970 | 1 | 1 |
_tObjectives and attributes, _p716. |
| 970 | 1 | 1 |
_tTrading off conflicting objectives: the basics, _p718. |
| 970 | 1 | 1 |
_tChoosing an automobile: an example, _p718. |
| 970 | 1 | 1 |
_tThe additive utility function, _p720. |
| 970 | 1 | 1 |
_tChoosing an automobile: proportional scores, _p721. |
| 970 | 1 | 1 |
_tAssessing weights: pricing out the objectives, _p722. |
| 970 | 1 | 1 |
_tIndifference curves, _p724. |
| 970 | 1 | 1 |
_tAssessing individual utility functions, _p725. |
| 970 | 1 | 1 |
_tProportional scores, _p726. |
| 970 | 1 | 1 |
_tRations, _p728. |
| 970 | 1 | 1 |
_tStandard utility-function assessment, _p729. |
| 970 | 1 | 1 |
_tAssessing weights, _p730. |
| 970 | 1 | 1 |
_tPricing out, _p730. |
| 970 | 1 | 1 |
_tSwing weighting, _p731. |
| 970 | 1 | 1 |
_tLottery weights, _p734. |
| 970 | 1 | 1 |
_tBiases and inconsistencies in weight assessment, _p736. |
| 970 | 1 | 1 |
_tKeeping concepts straight: certainty versus uncertainty, _p737. |
| 970 | 1 | 1 |
_tAn examople: library choices, _p738. |
| 970 | 1 | 1 |
_tUsing software for multiple-objective decisions, _p745. |
| 970 | 0 | 1 |
_aSummary, _p745. |
| 970 | 0 | 1 |
_aExercises, _p746. |
| 970 | 0 | 1 |
_aQuestions and problems, _p747. |
| 970 | 1 | 1 |
_tCase studies The satanic versus, _p755. |
| 970 | 1 | 1 |
_tDilemmas in medicine, _p755. |
| 970 | 1 | 1 |
_tA matter of ethics, _p757. |
| 970 | 1 | 1 |
_tFDA and the testing of experimental drugs, _p757. |
| 970 | 0 | 1 |
_aReferences, _p758. |
| 970 | 0 | 1 |
_aEpilogue, _p759. |
| 970 | 1 | 2 |
_tConflicting objectives II: multiattribute utility models with interactions, _p760. |
| 970 | 1 | 1 |
_tMultiattribute utility functions: direct assessment, _p761. |
| 970 | 1 | 1 |
_tIndependence conditions, _p763. |
| 970 | 1 | 1 |
_tPreferential independence, _p763. |
| 970 | 1 | 1 |
_tDetermining whether independence exists, _p765. |
| 970 | 1 | 1 |
_tUsing independence, _p767. |
| 970 | 1 | 1 |
_tAdditive independence, _p768. |
| 970 | 1 | 1 |
_tSubstitudes and complements, _p770. |
| 970 | 1 | 1 |
_tAssessing a two-attribute utility function, _p771. |
| 970 | 1 | 1 | _tThree or more attributes online only at www.cengagebrain.com. |
| 970 | 1 | 1 |
_tWhen independence fails, _p776. |
| 970 | 1 | 1 |
_tMultiattribute utility in action: BC Hydro, _p777. |
| 970 | 0 | 1 |
_aSummary, _p782. |
| 970 | 0 | 1 |
_aExercises, _p782. |
| 970 | 0 | 1 |
_aQuestions and problems, _p783. |
| 970 | 1 | 1 |
_tCase study a mining investment decision, _p786. |
| 970 | 0 | 1 |
_aReferences, _p788. |
| 970 | 0 | 1 |
_aEpilogue, _p788. |
| 970 | 1 | 2 |
_tCases, _p789. |
| 970 | 1 | 1 |
_tJohn Carter: hedging, _p789. |
| 970 | 1 | 1 |
_tSleepmore mattress manufacturing: plant consolidation, _p790. |
| 970 | 1 | 1 |
_tSusan Jones (A), _p795. |
| 970 | 1 | 1 |
_tSusan Janes (B), _p797. |
| 970 | 1 | 2 |
_tConclusion and further reading, _p799. |
| 970 | 1 | 1 |
_tA decision-analysis reading list, _p800. |
| 970 | 1 | 1 |
_tDecision analysis, _p801. |
| 970 | 1 | 1 |
_tBehavioral decision making, _p802. |
| 970 | 0 | 1 |
_aAuthor index, _p805. |
| 970 | 0 | 1 |
_aSubject index, _p807. |
| 999 |
_c11223 _d11223 |
||
| 003 | KOHA | ||