Logical Decisions - Classweb

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A: I could see where some people might think these were equally desirable, especially given what I've said before about these two measures, but I really think a truck should have balanced performance. I don't think I'd like to give up so much fuel economy to get really good power. So, I'd pick the balanced alternative B. Q: Good, that's the type of interaction I'm trying to get at here. Suppose I make alternative B less desirable. If I define B' as having 110 hp and getting 18 mpg, would you still prefer it to alternative A? A: No, now I think I'd prefer alternative A. I think I'd have trouble deciding if alternative B had 125 hp and got 19 mpg. LDW can combine this second tradeoff between power and fuel economy with the first one and the tradeoffs for the other members to compute the scaling constants for a multiplicative MUF formula. The algebra is somewhat complicated and won't be described here. The computations are all done automatically by LDW. You can make some qualitative checks on the computations, however. Since alternative B had levels that were less than the mid-preference levels, this means that the decision maker felt that the low level on MPG for alternative A had a relatively large effect on the overall utility. This corresponds to the third case in the table above (destructive interaction), where big K is greater than 0 and the small k sum to less than 1. i The response to a probabilistic question can also define the degree of interaction between two measures. Here LDW asks the decision maker to choose between two uncertain outcomes. This is the most difficult type of preference question used in LDW. You might pose the question as follows: Q: We already talked about the tradeoff between power and fuel economy. Now I want to look at those measures again to see if they have any interaction. Suppose a truck has uncertainties for some reason -- maybe it has an option package the dealer doesn't have the details for. Anyway, if you can imagine it, suppose the truck has a 50 percent 9-60 Section 9 -- In Depth
chance having 160 hp (the most preferred level) and 30 mpg (also the most preferred level) and a 50 percent chance of having 80 hp and 16 mpg (the least preferred levels). In other words, it's an all or nothing gamble, either the most preferred level on both measures or the least preferred levels, both with an equal chance of occurring. Call this alternative A. Now suppose a second truck is the same as the first but has a 50 percent chance of having 160 hp and 16 mpg and a 50 percent chance of having 80 hp and 30 mpg. It's still uncertain. You're guaranteed to get either good horsepower or good gas mileage, but not both. Call this alternative B. I know these are complicated so I've drawn them in Figure 9-16. Which of A or B would you prefer? Figure 9-16. Example of assessment question for interactions Section 9 -- In Depth 9-61
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Logical Decisions Decision Support
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Table of Contents Table of Contents
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Assessing Weights with Weight Ratio
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Assessing Interactions Between Meas
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Introduction Real decisions aren't
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S E C T I O N Requirements and Inst
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"Logical Decisions". This program g
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Quick Start Introduction This secti
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Defining goals and measures. In LDW
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Figure 3-1. The SUF for "Years of E
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weights of the measures. All of the
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S E C T I O N Basic Tutorial 4
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Figure 4-1. Tutorial overview. 4-2
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Now lets make sure the alternatives
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Assume you have decided that you wi
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Next we will enter the measures for
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Defining Preferences The alternativ
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1. Select the Assess::Common Units
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almost equally unacceptable, while
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1. Select the Assess::Common Units
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When you do this, the tradeoff grap
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1. Select "Performance" and "Price"
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8. Click on the "Equal" button to t
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Figure 4-11. Display generated by R
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Figure 4-13. Overall ranking for tr
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Figure 4-15. Graph showing sensitiv
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You can see the completed introduct
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Advanced Tutorial This tutorial sec
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Probabilities There is a problem wi
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This information indicates that the
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A uniform distribution is defined b
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On the left is a list of the possib
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Figure 5-2. Example of Results::Unc
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screen a large database for the alt
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9. LDW will ask if you want to appe
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commit to buying their truck before
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see that the rankings for all the a
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S E C T I O N Using LDW 1: Structur
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The Edit::Insert option. The Insert
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! Summary -- view a dialog box that
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structure like an organization char
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If you check the Show Assessment St
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saves it for later pasting. When yo
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You can create a new Matrix view by
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The quick entry view shows the alte
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Structuring Goals The goals in an L
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these two fields to describe each m
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the hierarchy. In the Matrix view,
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! Point Estimate -- use a single nu
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Figure 6-12. Example of a measure l
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Figure 6-14. Example of a measure l
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pass, LDW replaces each probabilist
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Figure 6-17. Dialog box for definin
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Figure 6-18. Measure Category Dialo
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S E C T I O N Using LDW 2: Assessin
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measure utility functions for the g
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information as possible when you ch
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measures with categories require mu
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The "Reset" button deletes any asse
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You can change the shape of the cur
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Figure 7-4. Utility Assessment Scre
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very different from the average of
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Figure 7-6. Assessment matrix for A
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You can, however, leave the assessm
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properties dialog box can be select
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the same level. LDW provides a grap
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options in the Hierarchy menu to ad
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The check box labeled "Allow Repres
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Next you will see a tradeoff assess
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The Tradeoff::Use Alternatives to S
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Figure 7-13. Example of Direct Entr
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You can think of the importance num
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Figure 7-15. Assessment Screen for
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When you have selected two members
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("importance strength") that best d
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clicking on the "Initialize" button
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Set a Small k. The Enter Small k op
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select the tradeoff you would like
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Figure 7-18. Effects of consistency
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Using LDW 3: Reviewing Results Intr
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The curve shows how the utility fun
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The tradeoff graph has one measure
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When you select the option, you are
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the relative weights of the "Price"
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epresents a computer alternative wi
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Figure 8-11. Example of Review::Ass
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e true even if the measure has a ve
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an analysis are included in exactly
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Graph Weights The Graph Weights dis
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Figure 8-16. Results::Rank Alternat
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Figure 8-18. Dialog box for Results
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You can use the measure equivalents
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simulation for each alternative usi
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Sensitivity Graphs Sensitivity grap
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You can view a sensitivity table by
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Figure 8-28. Example of Results::Sc
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Figure 8-30. Example of Results::Sc
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In the dialog box, you are asked to
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Figure 8-34. Dialog box for Results
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Graph an Alternative The graph an a
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Figure 8-38. Example of Results::Gr
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Finally, the two radio buttons on t
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Figure 8-43. Example of Results::Co
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Printing and Saving Windows You can
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The dialog box will show you the fi
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Figure 8-45. Dialog box for Edit::C
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Changing the range for utility. You
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Figure 8-50. Dialog box for Prefere
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the current color for that type of
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copies the selected objects to the
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analysis with the skeleton analysis
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Figure 8-55. Dialog box for File::I
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the other measures. The measure nam
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The idea of the Import Structure op
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Next, LDW asks you if it should app
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Other Options utility function for
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In addition, the Window menu contai
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In Depth Introduction This section
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A third example is a portfolio deci
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Each alternative has a raw score (c
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into more specific goals continues
Page 255 and 256: Measures in LDW You define measures
Page 257 and 258: computed measure levels to common u
Page 259 and 260: Converting the Measures to Common U
Page 261 and 262: Figure 9-2. Example of linear (stra
Page 263 and 264: For an example of the mid-level spl
Page 265 and 266: Since U(L0) = U(80) = 0 and U(L1) =
Page 267 and 268: Figure 9-5. Summary of SUF assessme
Page 269 and 270: describe two alternatives: A, which
Page 271 and 272: with equal chances of 40 and 70 per
Page 273 and 274: In the original formulation of the
Page 275 and 276: 1 Equal Importance Two activities c
Page 277 and 278: You begin the process by selecting
Page 279 and 280: Figure 9-7. Effects of goals with a
Page 281 and 282: 0.5. Then the weight assigned to "P
Page 283 and 284: ! You can use the "Smarter Method"
Page 285 and 286: allocates this weight before comput
Page 287 and 288: nth root of the product of the rati
Page 289 and 290: Figure 9-8. Summary of estimating t
Page 291 and 292: on the decision maker's response, L
Page 293 and 294: Figure 9-10. MUF assessment figure
Page 295 and 296: Now think of adjusting P so that th
Page 297 and 298: Another approach is to use the rang
Page 299 and 300: Figure 9-12. Quantitative range vs.
Page 301 and 302: If a measure’s range changes, LDW
Page 303 and 304: Figure 9-14 is an example of the ov
Page 305: Similarly, a single member can have
Page 309 and 310: A probability of less than 0.5 for
Page 311 and 312: Interpreting the Ranking Results LD
Page 313: S E C T I O N Examples 10
Page 316 and 317: the idea that other manufactures an
Page 318 and 319: The completed goals hierarchy is sh
Page 320 and 321: Buying a House The ranking results
Page 322 and 323: Figure 10-5. Goals hierarchy for bu
Page 324 and 325: Overall goal Quality goal Costs goa
Page 326 and 327: The preference assessments were don
Page 328 and 329: Figure 10-9. Goals hierarchy for re
Page 330 and 331: ! Noise, ! Agricultural Impacts, an
Page 333: S E C T I O N Commands Summary 11
Page 336 and 337: Assess Menu Edit Menu The Assess me
Page 338 and 339: File Menu Edit::Insert Lets you add
Page 340 and 341: utility function” if the active g
Page 342 and 343: Matrix Menu ! Hierarchy -- options
Page 344 and 345: Results::Dynamic Sensitivity See th
Page 346 and 347: Review::Compute Utilities Compute t
Page 348 and 349: Tradeoff Menu View Menu LDW display
Page 351: S E C T I O N Glossary 12
Page 354 and 355: Common Units Certainty Equivalent C
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LDW File Level Lottery Measure assi
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MUF MUF Formula See also: Alternati
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Preference Set Probabilistic Level
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Tradeoff Trial program decides whic
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Bibliography B
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The classic reference on multiple m
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Appendix This appendix describes th
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= .125 Thus the weight for cost is
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Index adjusted AHP ................
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computing ................8 - 3 int
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