1 DSS-19980210.pptSteven O. Kimbrough Foundations for DSS: Rationality, Utility Theory & Decision Analysis Topics in DSS –End user computing –DSS concepts

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”


End User Computing

• Concept

• History– Motivations

• Packages

• Management issues– How much?

– Who?

– + or -?

– etc.

• Examples?


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


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?


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


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!


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."


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."


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)


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)


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.


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))



• Model with tables: (b) desirabilities

Red

White

Rosé

Actions:a(i)

Outcomes: o(j)


D(o(1)|a(1)) .........................:::::::

D(o(j)|a(i))



• Model with tables: (c) net results

Red

White

Rosé

Actions:a(i)

Outcomes: o(j)


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



• 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)



• 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!


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


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


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


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.


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


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


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?)


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.


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


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.


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.


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.



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.



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.



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



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?

Documents

1 DSS-19980210.pptSteven O. Kimbrough Foundations for DSS: Rationality, Utility Theory & Decision Analysis Topics in DSS –End user computing –DSS concepts