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1 Civil Systems Planning Benefit/Cost Analysis Scott Matthews Courses: 12-706 and 73-359 Lecture 17 - 11/1/2004.

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Presentation on theme: "1 Civil Systems Planning Benefit/Cost Analysis Scott Matthews Courses: 12-706 and 73-359 Lecture 17 - 11/1/2004."— Presentation transcript:

1 1 Civil Systems Planning Benefit/Cost Analysis Scott Matthews Courses: 12-706 and 73-359 Lecture 17 - 11/1/2004

2 12-706 and 73-3592 Admin Issues  PS 3 due next Wednesday  Sorry again for confusion, thanks for patience  Make sure you use REVISED version  Specifics on Risksim results to submit  Early Course Feedback  Lecture

3 12-706 and 73-3593 Early Evaluation Comments - “Positive”  Goals clear  Grading criteria clear  Well Organized  Good examples in lecture  Responsive/answers questions  HW Hard but fun  Excel add-ins  Case studies help (and more coming!)  Open-ended stuff is novel and interesting (thanks)  Analogies/stories/jokes good (new rule: 1 per class)  Enthusiastic (that’s a new one)

4 12-706 and 73-3594 Early Comments - “Negative” Not meant to be defensive  The clarity sucks (the clarity of this comment sucks??)  No concern for students, hard to approach (?)  Assignment weights too much/too little (see syllabus)  Assignments should be out of 100 not 50 (?)  Grading too strict (don’t worry yet - see syllabus)  More feedback on point deductions (ok)  All these are odd since I have never received them before!  Minor adjustments on slides at last minute (ok)  Want to see links sooner (thus the problem - see above)  Book sucks (I know - but it’s the best one given content)  Talk too fast and too quietly (tell me!)  Need more hints (ok - but should ask for them)  More project details (coming)

5 12-706 and 73-3595 Multi-objective Methods  Multiobjective programming  Mult. criteria decision making (MCDM)  Is both an analytical philosophy and a set of specific analytical techniques  Deals explicitly with multi-criteria DM  Provides mechanism incorporating values  Promotes inclusive DM processes  Encourages interdisciplinary approaches

6 12-706 and 73-3596 Decision Making  Real decision making problems are MC in nature  Most decisions require tradeoffs  E.g. college-selection problem  BCA does not handle MC decisions well  It needs dollar values for everything  Assumes all B/C quantifiable  BCA still important : economic efficiency

7 12-706 and 73-3597 MCDM Terminology  Non-dominance (aka Pareto Optimal)  Alternative is non-dominated if there is no other feasible alternative that would improve one criterion without making at least one other criterion worse  Non-dominated set: set of all alternatives of non-dominance

8 12-706 and 73-3598 Choosing a Car  CarFuel Eff (mpg) Comfort  Index  Mercedes2510  Chevrolet283  Toyota356  Volvo309  Which dominated, non-dominated?

9 12-706 and 73-3599 Conflicting Criteria  Two criteria ‘conflict’ if the alternative which is best in one criteria is not the best in the other  Do fuel eff and comfort conflict? Usual.  Tradeoff: the amount of one criterion which must be given up to attain an increase of one unit in another criteria

10 12-706 and 73-35910 Tradeoff of Car Problem Fuel Eff Comfort 10 5 0 2030 M V T C 1) What is tradeoff between Mercedes and Volvo? 2) What can we see graphically about dominated alternatives?

11 12-706 and 73-35911 Tradeoff of Car Problem Fuel Eff Comfort 10 5 0 2030 M(25,10) V(30,9) T C 5 The slope of the line between M and V is -1/5, i.e., you must trade one unit less of comfort for 5 units more of fuel efficiency.

12 12-706 and 73-35912 On Objectives  Specifying and using objectives is fundamentally important  Is the most important thing you do  Get it right, on the way to win-win  Get them wrong, in big trouble!  Objective (aka criterion): a statement of desirable performance which includes a direction or orientation (e.g. min air emissions)

13 12-706 and 73-35913 More Defs  Measures (or attributes)  Indicate degree to which objective is achieved or advanced  Of course its ideal when these are in the same order of magnitude. If not, should adjust them to do so.  Goal: level of achievement of an objective to strive for  Note objectives often have sub-objectives, etc.

14 12-706 and 73-35914 Example Objective Minimize air emissions Objective: Min. SO2Min. NOxSub-objectives: Measures: tons SO2/yrtons NOx/yr Potential Goal: reduce SO2 emissions by 50%! This implies the need for an objective hierarchy or value tree

15 12-706 and 73-35915 Structuring Objectives Choose a college ReputationCost Atmosphere AcademicSocial TuitionLivingTrans.  Making this tree is useful for  Communication (for DM process)  Creation of alternatives  Evaluation of alternatives

16 12-706 and 73-35916 Key Issues  Specification - objectives need to be specified to allow measures to be specified  ‘Max air quality’ not good enough!  Find a balance between enough spec. to allow measure and ‘too much’ spec.  Means v. Ends - Hierarchy should only include ‘ends objectives’

17 12-706 and 73-35917 Desirable Properties of Obj’s  Completeness (reflects overall objs)  Operational (supports choice)  Decomposable (preference for one is not a function of another)  Non-redundant (avoid double count)  Minimize size

18 12-706 and 73-35918 Applications  Simple Multi-Attribute Rating Tech. (SMART)  Analytical Hierarchy Process (AHP)  Elicits preferences of pairwise comparisons at common levels  E.g. how much more important is x vs. y?  Then user gives ‘data’ on measures

19 12-706 and 73-35919 How to solve MCDM problems  All methods (AHP, SMART,..) return some sort of weighting factor set  Use these weighting factors in conjunction with data values (mpg, price,..) to make value functions  In multilevel/hierarchical trees, deal with each set of weights at each level of tree


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