# week11da - Spring, 2010 DecisionAnalysis MS405 Conflicting...

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Kemal Kılıç, Sabancı Üniversitesi Kemal Kılıç, Sabancı Üniversitesi Spring Spring , 2010 , 2010 Decision Analysis   MS 405    Conflicting Objectives II Kemal Kılıç Faculty of Engineering and Natural Sciences Week 11 1

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Kemal Kılıç, Sabancı Üniversitesi Kemal Kılıç, Sabancı Üniversitesi Spring Spring , 2010 , 2010 Review - Multiple Objectives Note that the utility theory we discussed was single attribute utility theory. We often find ourselves with multiple objectives. Now we will start discussing multiattribute utility case. First we will create and additive preference model and present the Additive Utility Function , where we will calculate a utility score for each objective and then add the scores, weighting them appropriately according to the various objectives (Chapter 15). Next , we will relax the assumption that the total utility is weighted sum of individual utilities and provide some extensions to the theory (Chapter 16). Week 11 2
Kemal Kılıç, Sabancı Üniversitesi Kemal Kılıç, Sabancı Üniversitesi Spring Spring , 2010 , 2010 Review - Assessing Utility Functions Proportional Scores Every utility value is proportional to how far it is along the range Ratio Scores For each individual outcome, rate how much more or less it is than some standard Lottery Assessment Previous way of deriving utilities Captures risk attitude Week 11 3

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Kemal Kılıç, Sabancı Üniversitesi Kemal Kılıç, Sabancı Üniversitesi Spring Spring , 2010 , 2010 Review - Assessing Weights Pricing Out Determine the marginal rate of substitution (how much of attribute A would you pay (give up) to get 1 unit of attribute B ) OK if utilities are linear and interval scaled Swing Weighting Procedure used before with values Example pgs 615 - 617 with cars (Color is included as an additional attribute) Lottery Weighting Same idea as evaluating individual utilities Week 11 4
Kemal Kılıç, Sabancı Üniversitesi Spring Spring , 2010 , 2010 Review - Certainty vs. Uncertainty The assumption in previous example is that we knew the payoffs (values) for the different alternatives Thus, once we evaluated utilities and weights, we could get an exact answer to the decision problem Value functions are only appropriate for decisions under certainty The value functions could be arbitrarily assigned; what mattered was the ordering between alternatives The proportional scaling we did with the value functions is OK under uncertainty when the decision maker is making decisions based only on expected value (i.e., risk neutral). Lottery-based utility derivation is used for uncertainty The weight assessment methods can be used in either case Note that we assumed that multiattribute utility is the sum of individual utilities. Week 11

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## This note was uploaded on 12/19/2011 for the course ECON Econ203 taught by Professor Majdabpadawnan during the Spring '11 term at University of Maribor.

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week11da - Spring, 2010 DecisionAnalysis MS405 Conflicting...

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