Multi-Criteria Decision Making
Decision Making and Risk, Sp2006: Session 6
Notebook Computer Decision

Consumer is interested in buying a notebook
computer.

Goes to electronics store.

Sees the following options.

How should she go about buying one?
Choices
Brand and Model
Price
Processor
Speed
RAM
HD
Screen
Model “A”
699
1.8 GHz
256 MB
100 GB
15.4
Model “B”
749
1.9 GHz
256 MB
100 GB
15
Model “C”
599
1.4 GHz
512 MB
60 GB
12
Model “D”
799
1.7 GHz
256 MB
80 GB
14.1
How would you proceed if you were the one making the choice?
Types of MCDM Rules

Compensatory:




Simple compensatory
Linear (integration and valuation)
Non-linear (linear integration, non-linear valuation)
Non-compensatory

Screening



Selection



Conjunctive
Disjunctive
Elimination by aspects
Lexicographic
Heuristics
Simple Compensatory Rule

Alternative with the most number of top of class
performance.
Brand and Model
Price
Processor
Speed
RAM
HD
Screen
Model “A”
699
1.8 GHz
256 MB
90 GB
15.4
1
Model “B”
749
1.9 GHz
256 MB
100 GB
15.4
3
Model “C”
599
1.4 GHz
512 MB
60 GB
12
2
Model “D”
799
1.7 GHz
256 MB
80 GB
14.1
0
Approach #1

Imagine “n” objects, with “m” attributes each.

For the ith object:

Declare attribute importance for each of the “m” attributes (wj).

Determine how much you like the level of the attribute in the ith product (aij)

Combine attribute importance with attractiveness of the attribute level featured in the product.

Add the above combinations.

For the ith object, Vi = Σj=1 to n (wj * aij), akin to expected utility.

Repeat this for all i objects.

Pick “i” such that, i = Max (V1, V2, V3…..Vn)
Illustration – Linear Compensatory
Decision Rules
Price
CPU
RAM
HD
Screen
A
3
3
1
3
4
3.1
B
2
4
1
4
4
2.8
C
4
1
4
1
1
2.5
D
1
2
1
2
2
1.5
0.3
0.25
0.2
0.15
0.2
Wt
Non-linear Compensatory Decision Rules

Valuation of additional unit is not identical across the
range of the levels.


60G to 80G is more consequential compared to 100 to
120G
However, integration across attributes of the
alternatives is still linear.
Summary of Compensatory

Simple compensatory:
No relative valuation of attribute
levels other than best/not best, equal weight for all attributes.

Linear Compensatory:
Equal valuation of attributes levels,
linear combination of valuation through attribute-specific
weights.

Non-linear Compensatory: Unequal valuation of attribute
levels, linear combination of valuation through attributespecific weights.
Non-Compensatory Decision Rules


Screening Rules

Conjunctive decision rule

Disjunctive decision rule
Choice Rules

Elimination by aspects

Lexicographic decision rule
Elimination by Aspects

Elimination By Aspects





Start with minimum cutoff on most important attribute.
Eliminate those that do not clear cutoff.
Take the next most important attribute, and repeat steps
above.
Stop when you have one brand.
Elimination rule, rather than selection rule.
Conjunctive

Eliminate alternatives that don’t meet/exceed cutoff on every
attribute.
Brand
Price
CPU
RAM
HD
Screen Size
Compaq A
3
3
1
4
4
Compaq B
2
4
1
3
3
Compaq C
4
1
4
1
1
Compaq D
1
2
1
2
2
Min Cutoff
2
2
1
2
2
×
×
Disjunctive

Accept any alternative that exceeds minimum cutoff
on at least one attribute.
Brand
Price
CPU
RAM
HD
Screen Size
Compaq A
3
3
1
3
4
Compaq B
2
4
1
3
4
Compaq C
4
1
4
1
1
Compaq D
1
2
1
4
2
Cutoff
3
2
1
2
2
Elimination By Aspects
Brand
Price
CPU
RAM
HD
Screen Size
Compaq A
3
3
1
3
4
Compaq B
2
4
1
3
4
Compaq C
4
1
4
1
1
Compaq D
1
2
1
4
2
Cutoff
<=2
N/A
<=2
×
×
×
Lexicographic

Select best option on the most important attribute.
Brand
Price
CPU
RAM
HD
Screen Size
Compaq A
3
3
1
3
4
Compaq B
2
4
1
3
4
Compaq C
4
1
4
1
1
Compaq D
1
2
1
4
2
Weight
.3
.2
.2
.1
.2
Comparing Compensatory and Noncompensatory Decision Rules

Compensatory Decision Rules




Non-Compensatory Decision Rules




Strengths of one attribute can overcome weakness of another.
Selection rules
Effortful
Strength of one attribute cannot overcome weakness of another.
Elimination rules
Easier
Often decision makers use hybrid rules
Highlights

What happens when decision options come with multiple attributes?


Reduce them to a single attribute
Deal with multiple attributes

Compensatory


Non-compensatory








Weighted attribute utility approach (compensatory)
EBA (sequential elimination)
Lexicographic (selection by reduction to single attribute…repeat if necessary)
Conjunctive (inclusion based on thresholds for every attribute)

Assists in narrowing the consideration set
Disjunctive (inclusion based on threshold for at least one attribute)

Assists in broadening the consideration set
Different strategies at different stages.
Vary in effort and data required.
Regret may be a function of the type of decision strategy.
Thresholds are the result of past experiences, negatives leave a stronger
imprint.
Heuristics


Decision rules, shortcuts.
Sometimes, they are meta-decisions.
Some Examples
 What I did the last time around?
 What does the expert think?
 The price-quality relationship is:





Bogus, so, look for the relatively less expensive option.
Valid, so, look for the relatively more expensive option.
Minimize decision effort/cost.
Minimize regret.
Maximize effectiveness.