Logical Decisions - Classweb

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LDW File Level Lottery Measure assigned one of a limited number of possible text labels for each measure that uses labels. See also: Level, Measure. LDW files are used to save the information from a LDW session. LDW files contain the following information: ! Measure definitions, ! Alternative data, including the levels of the measures and probability distributions, ! Goal data, and ! Preference Set data including SUFs, MUF type and tradeoffs for each set. See also: Alternative, Goal, Level, Measure, MUF, Preference Set, SUF, Tradeoff. An alternative's level on a measure is the number on the measure's scale (having the proper units) that indicates how the alternative performs on that measure. Levels can also be probabilistic, so that the level is defined by a probability distribution instead of a single number. Levels can be text labels, where each alternative is assigned one of a limited number of text descriptors. Levels can also be defined as the weighted sum of a group of measure categories. Levels should not have a value or preference content. Levels are just data. The preference information is added when the levels are converted to utility. See also: Alternative, Label, Measure, Measure Category, Probabilistic Levels, Utility. A lottery is a standardized gamble used when assessing utilities. A lottery generally has two possible outcomes, each with a well defined probability. Lotteries are often compared to a level that occurs with certainty. See also: Assess Utility option, Certainty Equivalent, Level, Utility. Evaluation measures are the variables that are used to rank the alternatives. The decision analysis literature uses many aliases for measures, including "attributes", "criteria", and "scales". 12-4 Section 12 -- Glossary
Measure Category Member Monte Carlo Simulation A measure consists of a name, a three letter abbreviation, units and most and least preferred levels. LDW puts no restrictions on the most and least preferred levels. The most preferred level can be greater or less than the least preferred level. There is also no requirement that the ranges on different measures be comparable. The ranges are made comparable when levels on the measures are converted to utility using the SUF for each measure. See also: Alternative, Level, Range, SUF, Utility. A sub-measure associated with a measure. You can define measures so that their levels are the weighted sum of several measure category levels. Measure category levels are not converted to common units before they are summed, but otherwise they are exactly like measure levels. See also: Common Units, Level, Measure, Measure Level. A member of a goal is either a measure or another goal that is included under the first goal in a goals hierarchy. Note: At least one of the goals in an analysis must only have measures as members. See also: Goal, Goals Hierarchy, Measure, MUF, Preference Set, Utility. A method for estimating the uncertainty of a number that is a complex function of one or more probability distributions. It can be very difficult to compute analytically a probability distribution that is the result of combining other distributions. This is the case for estimating a probability distribution over utility from the probability distributions over measure levels, particularly when complex SUFs and MUFs are involved. Monte Carlo simulation avoids this problem by using random numbers to provide an estimate of the distribution. Monte Carlo simulation uses a random number generator to produce random samples from the probabilistic levels. each set of samples is used to compute the utility of one possible outcome of the measure level uncertainties. Each computed utility is called a trial. Many trials are done and the results for each trial are saved. The sorted trials can be used as an estimate of the cumulative probability distribution of the desired utility. Section 12 -- Glossary 12-5
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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
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Measures in LDW You define measures
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computed measure levels to common u
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Converting the Measures to Common U
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Figure 9-2. Example of linear (stra
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For an example of the mid-level spl
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Since U(L0) = U(80) = 0 and U(L1) =
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Figure 9-5. Summary of SUF assessme
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describe two alternatives: A, which
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with equal chances of 40 and 70 per
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In the original formulation of the
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1 Equal Importance Two activities c
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You begin the process by selecting
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Figure 9-7. Effects of goals with a
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0.5. Then the weight assigned to "P
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! You can use the "Smarter Method"
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allocates this weight before comput
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nth root of the product of the rati
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Figure 9-8. Summary of estimating t
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on the decision maker's response, L
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Figure 9-10. MUF assessment figure
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Now think of adjusting P so that th
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Another approach is to use the rang
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Figure 9-12. Quantitative range vs.
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If a measure’s range changes, LDW
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Figure 9-14 is an example of the ov
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Page 307 and 308: chance having 160 hp (the most pref
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
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Page 318 and 319: The completed goals hierarchy is sh
Page 320 and 321: Buying a House The ranking results
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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
Page 358 and 359: MUF MUF Formula See also: Alternati
Page 360 and 361: Preference Set Probabilistic Level
Page 362 and 363: Tradeoff Trial program decides whic
Page 365: Bibliography B
Page 368 and 369: The classic reference on multiple m
Page 371 and 372: Appendix This appendix describes th
Page 373: = .125 Thus the weight for cost is
Page 377 and 378: Index adjusted AHP ................
Page 379: computing ................8 - 3 int
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