Introduction to Decision Making

Dr. Ioannis N. Lagoudis ilagoudis@misi.edu.my

lagoudis@mit.edu

Problems •  Personal: – Job – Car purchase

•  Community/local/national: – Drugs – Road/Airport/Hospital construction

Decision Making Process

Understanding  the  Problem  

Formula4ng  the  Problem  

Uncertainty  Analysis  U4lity  Analysis  

Op4miza4on  

Understanding the Problem •  Defining the problem •  Identifying the stakeholders in the decision

making process •  Knowing the available alternatives

Formulating the Problem •  Identify the events •  Document the information that is available: – From the beginning – At a later stage

•  Visualize events and information based on time.

Uncertainty Analysis •  Mainly with the use of probabilities •  Probability estimation is critical •  Probabilities can be derived based on: – Historical data – Expert opinion – Subjective estimation of stakeholders involved in

the decision making process

Utility / Value •  Identify values to alternatives: – Objective – Subjective

Optimization •  Estimate the different alternatives •  Compare among the different alternatives •  Use sensitivity analysis to evaluate the

robustness of the alternatives •  Choose the “best” alternative

Types of Problems • There are two broad categories: – Simple – Complex

Frequent Problems during Decision Making

•  Identify the variables that are of importance •  Filtering the right variables •  Variable evaluation •  Number of involved stakeholders

Decision Making Tools •  Statistics Regression Analysis •  Structural Equation Modeling (S.E.M.) •  Analytic Hierarchy Process (A.H.P.) •  Multi-attribute Utility Theory (M.A.U.T.) •  Decision Tree Model •  Simulation •  Linear Optimization

Statistics •  Mean •  Median •  Quartiles •  Probabilities •  Histogram

•  Variance •  Standard Deviation •  t distribution •  Hypothesis testing •  Sample

Regression

•  Simple

–  y=β0+β1χ1

•  Multiple –  y=β0+β1χ1+β2χ2+…+βnχn+ε

110 xy ββ +=

y  

x  

β1  

β0  

Structural Equation Modeling

•  Allows the comparison of independent variables

LatentVariable“A”

LatentVariable“A”

LatentVariable“B”

LatentVariable“B”

I1I1 I2I2 I3I3 I4I4 I5I5 I6I6

δ δ

λ λ

θ

LatentVariable“A”

LatentVariable“A”

LatentVariable“B”

LatentVariable“B”

I1I1 I2I2 I3I3 I4I4 I5I5 I6I6

δ δ

λ λ

θ

SecondOrderFactor“AB”

SecondOrderFactor“AB”

Γ

BasicBasic Second OrderSecond Order

LatentVariable“A”

LatentVariable“A”

LatentVariable“B”

LatentVariable“B”

I1I1 I2I2 I3I3 I4I4 I5I5 I6I6

δ δ

λ λ

θ

LatentVariable“A”

LatentVariable“A”

LatentVariable“B”

LatentVariable“B”

I1I1 I2I2 I3I3 I4I4 I5I5 I6I6

δ δ

λ λ

θ

SecondOrderFactor“AB”

SecondOrderFactor“AB”

Γ

BasicBasic Second OrderSecond Order

Analytic Hierarchy Process

GOALGOAL

Criteria (A)Criteria (A) Criteria (B)Criteria (B) Criteria (C)Criteria (C) Criteria (D)Criteria (D)

A1A1 AnAnA2A2 B1B1 BnBnB2B2 C1C1 CnCnC2C2 D1D1 DnDnD2D2

Level 1

Level 2

Level 3

Level 4 Alternative 1Alternative 1 Alternative 2Alternative 2 Alternative 3Alternative 3

Attributes

GOALGOAL

Criteria (A)Criteria (A) Criteria (B)Criteria (B) Criteria (C)Criteria (C) Criteria (D)Criteria (D)

A1A1 AnAnA2A2 B1B1 BnBnB2B2 C1C1 CnCnC2C2 D1D1 DnDnD2D2

Level 1

Level 2

Level 3

Level 4 Alternative 1Alternative 1 Alternative 2Alternative 2 Alternative 3Alternative 3

Attributes

Multi-Attribute Utility Theory

GOAL Criteria (a)

Criteria (c)

Criteria (b)

a1

a2

an

b1

b2

bn

c1

c2

cn

Decision Tree Models

Α  

C  

Β  

Value  in  €  

Value  in  €  

Value  in  €  

Value  in  €  

Op4on  Β  

p1  

p2  

p3  

p4  

Simulation •  Use of software •  Allows for detailed formulation on problems •  Enables scenario analysis via the creation of

random numbers •  SIML8, VENSIM, ARENA, MICROSOFT

EXCEL

Linear Optimization •  Optimization tool •  Uses mathematical

modeling

•  Max 130W + 100P –  1.5W + 1.0P ≤ 27 –  1.0W + 1.0P ≤ 21 –  0.3W + 0.5P ≤ 9 –  W ≤ 15 –  P ≤ 16 –  W,P ≥ 0

Thank you!