1 DSS-19980210.pptSteven O. Kimbrough Foundations for DSS: Rationality, Utility Theory & Decision...
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Transcript of 1 DSS-19980210.pptSteven O. Kimbrough Foundations for DSS: Rationality, Utility Theory & Decision...
1DSS-19980210.ppt Steven O. Kimbrough
Foundations for DSS:Rationality, Utility Theory
&Decision Analysis
• Topics in DSS– End user computing
– DSS concepts
– EIS, GDSS, groupware
– Rationality
– Frameworks for decision making
– Decision trees
– Multiattribute decision modeling
– Spreadsheet implementations
• Reading assignments (2 sessions)– Zwass, DSS chapter
– Kimbrough, MIS Notes, Part II, DSS; chapter 5, “A Brief Introduction to Decision Analysis” (skip sections 5.3-4); chapter 6, “Case: DSS Evaluation with MAUT”
– Kimbrough et al., AMV DSS paper
– Dawes, “Robust Beauty of Improper Linear Models”
2DSS-19980210.ppt Steven O. Kimbrough
End User Computing
• Concept
• History– Motivations
• Packages
• Management issues– How much?
– Who?
– + or -?
– etc.
• Examples?
3DSS-19980210.ppt Steven O. Kimbrough
S(imple) or F(ancy)?
S
S
F
F
50%, -$1
50%, -$1
90%, -$3
10%, -$1
10%, -$1
90%, -$3
50%, -$3
50%, -$3
4DSS-19980210.ppt Steven O. Kimbrough
DSS Concepts
• Data, models,…, and documents– Interactively
• History– Motivations
• Packages and tools– Roll your own
– DSS generators
– Spreadsheets+
• Management issues– How much?
– Who?
– + or -?
– etc.
• Examples?
5DSS-19980210.ppt Steven O. Kimbrough
DSS Concepts (con’t.)
• Data-oriented DSS– Questions? Examples?
• Model-oriented DSS– Examples?
• DSS application theory– what if: exploration, training, insight
– objectivity: models, data in “public”
– argumentation and persuasion
• Development of DSS
• How do executives use DSS?
• EIS?
• GDSS
• Groupware
6DSS-19980210.ppt Steven O. Kimbrough
Examples of Model-BasedDSS
• See, e.g., Interfaces– From one recent issue:
“IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation--Semiconductor Sector” IMPReSS has raised on-time deliveries from 75 to 95 percent without increasing inventories, enabled the sector to expand market share, and helped it to move from an annual loss of $75 million ot an annual profit of over $40 million.
“Integrated Planning for Poultry Production at Sadia” Sadia has saved over $50 billion over three years using mathematical models to obtain better conversaion of feed to live bird weight, improved utilitization of birds, improved fulfillment of production plans, reduced lead times, and wide ranging studies of price and demand scenarios.
“KeyCorp Service Excellence Management System” KeyCorp’s SEMS models have helpd it reduce customer processing time by 53 percent, improve customer wai time, and reduce personnel expenses.
And more!
7DSS-19980210.ppt Steven O. Kimbrough
Reminders on Rationality
• "To do something rationally is to do it for good
and cognet reasons. And this is not the same
as just having a motive for doing it. All of us
almost always act for motives, but valid
reasons are...what motivate the rational agent,
and most of us do not act rationally all of the
time."
• "From the rational point of view, our mere
wants have little significance. They can and
should be outweighed by our interests and our
needs."
• "Rationality is not just a matter of having some
reasons for what one does, but of aligning
one's beliefs, actions, and evaluations
effectively with the best or strongest available
reasons."
8DSS-19980210.ppt Steven O. Kimbrough
Rationality
• "Rationality does not make demands beyond
the limits of what is genuinely possible for us---
it does not require accomplishments beyond
the limits of the possible. For rationality, no
more is demanded of us than doing our realistic
best to work efficiently and effectively towards
the realization of our cognitive, practical, and
evaluative goals."
• "To be sure, rationality is not just a passible
matter of making good use of the materials one
has on hand---in cognitive matters, say, the
evidence in view. It is also a matter of actively
seeking to enhance these materials: in the
cognitive case, by developing new evidential
resources that enable one to amplify and to test
one's conclusions. The endeavour to make the
most of one's opportunities is an aspect of
intelligence that is crucial to rationality."
9DSS-19980210.ppt Steven O. Kimbrough
Rationality
• "Rationality makes demands upon us. It
speaks in didactic tones: this or that is what
you should do."
• "Accordingly, rationality in all its forms calls for
the comparative assessment of feasible
alternatives, and so demands five faculties:
"1. Imagination...
"2. Information-processing...
"3. Evaluation...
"4. Selection---Informed Choice...
"5. Agency: the capacity to implement
choices."
• "Rational choice in a given situation generally requires a consideration of the wider context."
All this from Rescher, Rationality. (Aunte Martha)
10DSS-19980210.ppt Steven O. Kimbrough
Frameworks for DecisionMaking
• General elements for decision making
• Actions--a
» Up to us
• Outcomes--o
» Given to us
» Not considering game theory here.
» How might we do this?
• Probabilities--P(o|a)
• Desirabilities--D(o|a)
11DSS-19980210.ppt Steven O. Kimbrough
Example: Which Wine to Bring?
• Actions: bring red, bring white, bring rosé
• Outcomes, primary:
• Beef served
• Chicken served
• Fish
• Vegetarian
• Outcomes, net:
• Beef served with your red wine
• Beef served with your white wine
... etc.
12DSS-19980210.ppt Steven O. Kimbrough
Example: Which Wine to Bring?(con't.)
• Model with tables: (a) probabillities, (b)
desirabilities, (c) net results
Red
White
Rosé
Actions:a(i)
Outcomes: o(j)
Beef Chicken Fish Vegetarian
P(o(1)|a(1)) .........................:::::::
P(o(j)|a(i))
13DSS-19980210.ppt Steven O. Kimbrough
Example: Which Wine to Bring?(con't.)
• Model with tables: (b) desirabilities
Red
White
Rosé
Actions:a(i)
Outcomes: o(j)
Beef Chicken Fish Vegetarian
D(o(1)|a(1)) .........................:::::::
D(o(j)|a(i))
14DSS-19980210.ppt Steven O. Kimbrough
Example: Which Wine to Bring?(con't.)
• Model with tables: (c) net results
Red
White
Rosé
Actions:a(i)
Outcomes: o(j)
Beef Chicken Fish Vegetarian
P(o(1)|a(1))*D(o(1)|a(1)) +....:::::::
P(o(j)|a(i))*D(o(j)|a(i))j
actionsoutcomes
D(a(1))
D(a(2))
D(a(3))
• A reasonable rule: Pick (do) an a(1), such that
D(a(i)) D(a(j)), for i ° j
15DSS-19980210.ppt Steven O. Kimbrough
Frameworks for DecisionMaking
• Recall: general elements for decision making
• Actions--up to us
• Outcomes--given to us
• Probabilities--P(o|a)
• Desirabilities--D(o|a)
• But, how well do we know them?
• Certainty
• Risk (only up to a probability)
• Ambiguity (have only a rough idea of what
the probabilities are)
• Uncertainty (have no idea what the
probabilities are)
16DSS-19980210.ppt Steven O. Kimbrough
Frameworks for DecisionMaking
• In addition, we may or may not have a complete list
of the
• Actions
• Outcomes
• Decision making can become complex
• How many cells in this framework? Two levels of
completeness and four levels of knowledge (but
not applying to actions, assume we have them with
certainty), then the combinations are:
• a: 2, o: 4*2, p: 4*2, d: 4*2, or
• 2*3^8 = 13,122
And this is just a framework!
17DSS-19980210.ppt Steven O. Kimbrough
Decision Trees
• A very useful method, best when
• actions, outcomes, probabilities, desirabilities:
complete
• outcomes: uncertain
• probabilities: certain
• desirabilities: certain
• Otherwise, useful for doing sensitivity analysis
18DSS-19980210.ppt Steven O. Kimbrough
Decision TreesSimple Example: Parking
Meter
Plug the meter- $1.75
Don't plugthe meter
No ticket
p = 0.12
Ticket
1-p = 0.88
$0.00
- $15.00
19DSS-19980210.ppt Steven O. Kimbrough
Decision TreesSimple Example: Parking
Meter
Plug the meter- $1.75
Don't plugthe meter
No ticket
p = 0.12
Ticket
1-p = 0.88
$0.00
- $15.00
EV = 0.12*0.00 + 0.88*-$15.00 = -$13.20
EV = -$1.75
20DSS-19980210.ppt Steven O. Kimbrough
Decision Analysis: Theory-ette
• Four basic assumptions for utility theory
1. With sufficient calculation an individual faced with
two prospects, P1 and P2, will be able to decide
whether he or she prefers prospect P1 to P2, P2 to
P1, or whether he or she likes each equally well.
2. If P1 is regarded at least as well as P2, and P2 at
least as well as P3, then P1 is regarded at least as
well as P3.
3. If P1 is preferred to P2 which is preferred to P3,
then there is a mixture of P1 and P3 which is
preferred to P2, and there is a mixture of P1 and P3
over which P2 is preferred.
4. Suppose the individual prefers P1 to P2 and P3 is
another prospect. Then the individual prefers a
mixture of P1 and P3 to the same mixture of P2 and
P3.
21DSS-19980210.ppt Steven O. Kimbrough
Decision Analysis: Theory-ette(continued)
• Utility theory as the "logic of decision"---given your
beliefs and preferences it tells you other things you
should believe and prefer, if you are to be
consistent.
• Some basic concepts
• Shape of the utility curve
• Risk aversion
• Risk proneness
==> Utility theory accomodates different attitudes
towards risk.
• Example of utility or preference elicitation
22DSS-19980210.ppt Steven O. Kimbrough
Multiattribute Decisions
• When outcomes have more than one salient aspect
• Example, evaluating a firm:
• Sales
• Debt
• Quality of its products
• Growth of its industry....
• Example, what it takes to be a "world class competitor" (Businessweek criteria):
• Speed
• Quality
• Service
• Example: choosing a city to live in
• Example: choosing a job
• Example: designing a product
23DSS-19980210.ppt Steven O. Kimbrough
Multiattribute Decisions
• Just about all outcomes (for interesting problems) are multiattribute
• Note: an alternative would be to measure everything in dollars and have a single attribute utility function on dollars. Why is or why isn't this a good idea?
• Basic idea: reduce many (different) aspects to a single scale. Trading off apples and oranges?
• On the single scale---of utility---we can take expectations, if need be.
• We call the different outcome aspects attributes, hence "multiattribute utility theory" or MAUT (MUT?)
24DSS-19980210.ppt Steven O. Kimbrough
Multiattribute Decisions:Combining Attribute
Values• How do we combine attribute values?
• Simple approach, assume an additive model:
U(X) = w1*u1(x1) + .... + wn*un(xn)
for n attributes, where
w1 + ... + wn = 1 and wi >= 0, all i
Also, typically, 0 <= ui <= 1 (or 100), all i
w s are "weights"---relative importance weights
u s are unidimensional utility functions
Accepting this simple model, our task is to represent a situation using it and to fill in the blanks
AHP (analytic hierarchy process) is ONE such method. We'll look at another.
25DSS-19980210.ppt Steven O. Kimbrough
Multiattribute Decisions:Combining Attribute Values
(con't.)• Are there other ways of combining attribute
values? Yes, see, e.g., Table 8.4, p. 276, in von Winterfeldt and Edwards.
• AHP assumes the additive model.
• In the AMVDSS paper, which you are to read, I used a multiplicative model in two attributes.
• When is it OK to use an additive model?
• Roughly, when the attributes are preferentially independent (OK, and usual, to be statistically dependent)
• Warning: this is tricky, so be careful
• What happens in practice?
• Use the additive model whenever possible and reformulate attributes to insure that's OK
26DSS-19980210.ppt Steven O. Kimbrough
Multiattribute Decisions:
Five Universal StepsFrom Edwards (p. 273):
1. Define alternatives and value-relevant attributes.
2. Evaluate each alternative separately on each attribute.
3. Assign relative weights to the attributes.
4. Aggregate the weights of attributes and the single-attribute evaluations of alternatives to obtain an overall evaluation of alternatives.
5. Perform sensitivity analyses and make recommendations.
Different approaches differ on 2, 3, and 4.
Besides agreeing on 1 and 5, all approaches rely extensively onsubjective assessments.
27DSS-19980210.ppt Steven O. Kimbrough
SMART: 10 Steps
As noted, there are different versions, but here is a reasonable, workable, useful one:
1. Identify the organization whose values are to be determined.
2. Identify the purpose of the value elicitation.
3. Identify the entities (alternatives, objects) that are to be evaluated.
4. Identify the relevant dimensions of value (attributes).
5. Rank the dimensions in order of importance.
6. Make ratio estimates of the relative importance of each attribute relative to the one ranked lowest in importance.
7. Sum the importance weights; divide each by the sum.
8. Measure the relative value of each entity (alternative, object) on each dimension on a scale of 0 to 100.
9. Calculate the overall values using a weighted additive model.
10.Choose the alternative that maximizes the overall value.
28DSS-19980210.ppt Steven O. Kimbrough
SMART: Discussion of the 10 Steps
1. Identify the organization whose values are to be determined.
2. Identify the purpose of the value elicitation.
3. Identify the entities (alternatives, objects) that are to be evaluated.
Pretty obvious, but often forgotten, at the peril of the forgetters.
29DSS-19980210.ppt Steven O. Kimbrough
SMART: Discussion of the 10 Steps
4. Identify the relevant dimensions of value (attributes).
• A useful technique: value trees.
• Basic idea: have gross and detailed descriptions of value, e.g.,
• Speed
• Quality
• Service
and each of these can be broken down into attributes.
30DSS-19980210.ppt Steven O. Kimbrough
SMART: Discussion of the 10 Steps
5. Rank the dimensions in order of importance.
This is convenient and helps to make the subjective assessment a little easier. One needn't agonize over ties or close calls.
6. Make ratio estimates of the relative importance of each attribute relative to the one ranked lowest in importance.
Try this: taking into account the actual ranges assumed for the attributes, give the least important attribute 10 points. Give the next least important attribute 10 or more points.....
7. Sum the importance weights; divide each by the sum.
This normalizes the wi s to a 0--1 scale.
Note: a nice technique for doing sensitivity analysis in a spreadsheet.
31DSS-19980210.ppt Steven O. Kimbrough
SMART: Discussion of the 10 Steps
8. Measure the relative value of each entity (alternative, object) on each dimension on a scale of 0 to 100.
This is more involved that it sounds. We're after ui(xi) for each i and for each object to be evaluated.
• First, determine the upper and lower limits for each xi (this should have been done earlier)
• Determine which is best for each xi, the upper or the lower bound.
• Get a utility function for each xi, ui(xi).
• The easy thing: draw a straight line.
• Utility elicitation (with risk): use lotteries and midpoint splitting
• Value elicitation (with certainty): midpoint splitting, ask What value of x is halfway between these two extremes, measured in value to me?
• Now, actually do the score, get xij, j ranging across all options.
• Apply the utility function, ui, to each xij score
32DSS-19980210.ppt Steven O. Kimbrough
SMART: Discussion of the 10 Steps
9. Calculate the overall values using a weighted additive model.
10.Choose the alternative that maximizes the overall value.
These steps are easy!
9. Plug your numbers into the (additive) formula to get a value score for each alternative.
10. Pick an alternative with the highest score.
...but do sensitivity analysis!
How?