Presentation on theme: "Value Focused Thinking 101 The Mechanics Mike Bailey asst. by Matt Aylward, Greg Parnell (by extension) Keeney, Ralph L. 1992. Value-Focused Thinking,"— Presentation transcript:
Value Focused Thinking 101 The Mechanics Mike Bailey asst. by Matt Aylward, Greg Parnell (by extension) Keeney, Ralph L Value-Focused Thinking, A Path to Creative Decisionmaking. Harvard University Press, Cambridge, Mass.
Value-Focused Thinking A.K.A. Multi-Objective Decision Analysis (MODA) Descendant of Saul Gaas’s Analytical Hierarchical Processes (AHP), and prior methods Amalgamate and Balance incomparable Desired Properties I want a car that’s…fast, fun, cheap, cool, roomy, easy to take care of, red, with a bike rack, long-lasting… –How do you score cars to get the right balance? –What if the right balance is being determined by a group (possibly experts)? Discussion on application in a group of experts…Keeney
MECHANICS Skip all of the VFT Theology Focus on how to EXECUTE –Construction of the Value Tree –Calculation of Value Weights –Evaluation of an Alternative Motivated by a simple example: Selecting a College
BUILDING THE VALUE TREE Structure important issues into a two-layer tree… –Values (Functions) –Characteristics (Tasks) Pick the Best College V1. PriceV2. SelectivityV3. Livability
THE VALUE TREE Pick the Best College V1. Price V2. Selectivity V3. Livability M1.1 Annual Tuition M1.2 Housing Cost M1.3 Living Expenses M2.1 Petersons Guide Rank M2.2 Avg SAT Score M3.1 BBall Team Rank M3.2 Bars M3.3 Proximity to Home M3.4 Cool Factor
THE VALUE TREE Pick the Best College V1. Price V2. Selectivity V3. Livability M1.1 Annual Tuition M1.2 Housing Cost M1.3 Living Expenses M2.1 Petersons Guild Rank M2.2 Avg SAT Score M3.1 BBall Team Rank M3.2 Bars M3.3 Proximity to Home M3.4 Cool Factor FUNCTIONS TASKS
BUILDING THE VALUE FUNCTION Each measure’s importance is weighted for its contribution Each alternative is measured against each measure VFT Practice: Weights done before measurements. Try to keep the participants away from the alternatives for as long as possible.
WEIGHTS Have each participant individually rank order the measures For our example 1.Tuition 2.Housing 3.Peterson 4.Proximity 5.SAT avg 6.BBall Rank 7.Cool 8.Living Expenses 9.Bars
PAIRWISE PREFERENCE MATRIX
BUILDING UP WEIGHTS P = sum of all preference matrix elements –Something close to n 2 /2 S i = number of times option i preferred –i th row sum of matrix P Rank the measures by S i Build clumps (3-6 clumps)
COLLEGE EXAMPLE Tuition19 Housing14 Peterson12 Prox to Home12 SAT Score11 BBall Team9 Cool8 Living Expenses8 Bars2
VARIABILITY Judgment call Made by Analyst, not Participants High/Medium/Low How much variability in the measure is present in the options being considered? –Tuition: $8,500 to $38,000 [HIGH] –Proximity: 1hr to 6hr [LOW] –SAT’s: 1050 to 1200 [LOW]
WEIGHT MATRIX Importance HighMedLow Variance High Med Low >> Pre-assign numerical weights to each cell. Bin the metrics according to Importance and Variability. >> Enforce Monotonicity.
MEASUREMENT Now we consider each alternative We will build a utility curve for each metric –Translates a measurable (X) onto a [0,1] utility value (Y) over the range of the alternatives We will measure each alternative’s utility value
Key Concept: Families of Curves Linear –Each unit of X returns one unit of Y Convex –Initially, increments of X return multiple units of Y Concave –Initially, increments of X return less than one unit of Y S-Curve –Combines convex and concave Y X Y X Y X Y X MPH Concealment Payload H2O Prod.
UTILITY CURVE: TUITION This can get fancy, but a line through a few points is AOK.
ROANOKE COLLEGE BINWEIGHTSCORETOTAL Tuition19 H/H Housing14 L/M Peterson12 M/M Prox to Home12 L/M SAT Score11 L/M BBall Team9 H/L Cool8 M/L Living Expenses8 M/L Bars2 ?/VL00.90 SCORE 309.8
FINAL LAP Score each alternative on each measure Take the weighted sum That’s the alternative’s score FINE’ !
SOME ISSUES TO WATCH OUT FOR Interdependent measures –Peterson guide and SAT Avg –Guidance: Work to indentify aggregated but objective measures that are independent Value’s influence –More component measures leads to unintentionally over/under emphasis of a specific value –Guidance: Establish a number (3) of measures per Value Arbitrary weights –Weight matrix can be unbalanced or overbalanced –Do sensitivity analysis in the open Filtering –Delete (not just weight = 0) infeasible alternatives –E.g. a tuition you just can’t/won’t pay