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Non-Probabilistic Decision Rules: Lexicographic Ordering, Satisficing, Maxmax Payoff, Slides of Human-Computer Interaction Design

An overview of non-probabilistic decision rules, including lexicographic ordering, satisficing, maxmax payoff, maxmin payoff, minmax regret, laplace, and hurwitz principle. It covers the types of decision making environments, types of non-probabilistic decision rules, desirable properties of decision rules, and specific examples of each rule. The document also discusses the advantages and disadvantages of each rule.

Typology: Slides

2012/2013

Uploaded on 05/08/2013

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Non-probability

decision rules

222

Types of Decision Making Environment

 Non-Probability Decision Making

 Decision maker knows with certainty the consequences of every alternative or decision choice

 Decision Making under Risk

 Decision maker can assign the probabilities of the various outcomes

4

Values

Alternatives Attribute 1 Attribute 2 …

A

B

C

Decision Table

Outcomes Alternatives Outcome 1 Outcome 2 … A

B

C

Payoff Table

555

Desirable Properties of Decision Rules

 Transitivity

 If alternative A is preferred to alternative B and alternative B is preferred to alternative C, then alternative A is preferred to alternative C

 Column Linearity

 The preference relation between two alternatives is unchanged if a constant is added to all entries of a column of the decision table or payoff table

 Addition/Deletion of Alternatives

 The preference relation between two alternatives is unchanged if another alternative is added/deleted from the decision table or payoff table

 Addition/Deletion of Identical Columns

 The preference relation between two alternatives is unchanged if a column with the same value in all alternatives is added/deleted to the decision table or payoff table

777

Satisficing/Minimum Aspiration Level

Select any alternative which satisfies the minimum aspiration levels

(the minimum acceptable criteria) of all values

Values

Alternatives Safety ≥Medium

Price ≤13k

Reliability ≥Medium A High $15k High

B Medium $11k Medium

C High $13k Medium

May not be optimal because not all alternatives will be considered

as long as one satisfactory alternative is found

888

Maxmax Payoff

Select the alternative which results in the maximum of maximum

payoffs; an optimistic criterion

Outcomes

Alternatives O1 O2 O

A $1,000 $1,000 $1, B $10,000 -$7,000 $ C $5,000 $0 $ D $8,000 -$2,000 $

Maximum Payoff

$1, $10,

Payoff Table

B > D > C > A

101010

Outcomes

Alternatives O1 O2 O3 O

A $1,000 $1,000 $1,000 $8, B $10,000 -$7,000 $500 $8, C $5,000 $0 $800 $8, D $8,000 -$2,000 $700 $8,

Payoff Table

Maximum Payoff $8, $10, $8, $8,

B > A = C = D

Maxmax payoff violates addition/deletion of identical columns

111111

Maxmin Payoff

Select the alternative which results in the maximum of minimum

payoffs; a pessimistic criterion

Outcomes

Alternatives O1 O2 O

A $1,000 $1,000 $1, B $10,000 -$7,000 $ C $5,000 $0 $ D $8,000 -$2,000 $

Minimum Payoff

$1, -$7,

Payoff Table

A > C > D > B

Maxmin payoff violates column linearity and addition/deletion of

identical columns Docsity.com

131313

Outcomes

Alternatives O1 O2 O

A $1,000 $1,000 $1, B $10,000 -$7,000 $ C $5,000 $0 $ D $8,000 -$2,000 $ E -$1,000 $4,000 $

Payoff Table

Outcomes O1 O2 O $9,000 $3,000 $ $0 $11,000 $ $5,000 $4,000 $ $2,000 $6,000 $ $11,000 $0 $1,

Regret Table

Maximum Regret $9, $11, $5, $6, $11,

C > D > A > B

Minmax regret violates addition/deletion of alternatives

141414

Laplace

Calculate the average of each alternative by assuming that the

outcomes are equally likely to occur, and select the alternative with the

largest average

Average

$1, $1,166.

Outcomes

Alternatives O1 O2 O

A $1,000 $1,000 $1, B $10,000 -$7,000 $ C $5,000 $0 $ D $8,000 -$2,000 $

Payoff Table

D > C > B > A

161616

Hurwicz score = Max. payoff ∙α + Min. payoff ∙(1-α)

α

Alternative A B C D 0 1000 -7000 0 - 0.1 1000 -5300 500 - 0.2 1000 -3600 1000 0 0.3 1000 -1900 1500 1000 0.4 1000 -200 2000 2000 0.5 1000 1500 2500 3000 0.6 1000 3200 3000 4000 0.7 1000 4900 3500 5000 0.8 1000 6600 4000 6000 0.9 1000 8300 4500 7000 1 1000 10000 5000 8000

Hurwicz Scores of Alternatives with Respect to α

A: Hurwicz score = 1000

B: Hurwicz score = 10000∙α + (-7000)∙(1-α) = 17000α- 7000

C: Hurwicz score = 5000∙α + 0∙(1-α) = 5000α

D: Hurwicz score = 8000∙α + (-2000) ∙(1-α) = 10000α- 2000

1717

α=0.2 α=0.4 α=5/ ≈0. When 0≤α<0.2, A is the best alternative When 0.2≤α≤0.4, C is the best alternative When 0.4≤α≤5/7, D is the best alternative When α>5/7, B is the best alternative Docsity.com