Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 4
Modeling and Analysis
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
4-1
Learning Objectives
• Understand basic concepts of MSS
modeling.
• Describe MSS models interaction.
• Understand different model classes.
• Structure decision making of alternatives.
• Learn to use spreadsheets in MSS
modeling.
• Learn to structure linear program modeling.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Learning Objectives
• Examine search methods for MSS
models.
• Handle multiple goals.
• Understand terms sensitivity,
automatic, what-if analysis, goal
seeking.
• Know key issues of model
management.
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Dupont Simulates Rail Transportation
System and Avoids Costly Capital
Expense Vignette
• Promodel simulation created
representing entire transport system
• Applied what-if analyses
• Visual simulation
• Identified bottlenecks
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MSS Modeling
• Key element in DSS
• Many classes of models
• Specialized techniques for each
model
• Allows for rapid examination of
alternative solutions
• Multiple models often included in a
DSS
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Simulations
•
•
•
•
•
Explore problem at hand
Identify alternative solutions
Can be object-oriented
Enhances decision making
View impacts of decision alternatives
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DSS Models
•
•
•
•
•
•
•
Algorithm-based models
Statistic-based models
Linear programming models
Graphical models
Quantitative models
Qualitative models
Simulation models
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Static Models
• Single photograph of situation
• Time can be rolled forward, a photo at
a time
• Usually repeatable
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Dynamic Model
•
•
•
•
Represent changing situations
Time dependent
Varying conditions
Occurrence may not repeat
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Decision-Making
• Certainty
– Assume complete knowledge
– All potential outcomes known
– Easy to develop
– Resolution determined easily
– Can be very complex
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Decision-Making
• Uncertainty
– Several outcomes for each decision
– Probability of occurrence of each
outcome unknown
– Insufficient information
– Assess risk and willingness to take it
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Decision-Making
• Probabilistic Decision-Making
– Decision under risk
– Probability of each of several possible
outcomes occurring
– Risk analysis
• Calculate value of each alternative
• Select best expected value
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Influence Diagrams
•
•
•
•
•
•
Graphical representation of model
Provides relationship framework
Examines dependencies of variables
Any level of detail
Shows impact of change
Shows what-if analysis
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Influence Diagrams
Variables:
Decision
Intermediate
or
uncontrollable
Result or outcome
(intermediate or
final)
Arrows indicate type of relationship and direction of influence
Certainty
Amount
in CDs
Interest
earned
Sales
Uncertainty
Price
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Influence Diagrams
Random (risk)
~
Demand
Sales
Place tilde above
variable’s name
Preference
(double line arrow)
Sleep all
day
Graduate
University
Get job
Ski all
day
Arrows can be one-way or bidirectional, based upon the
direction of influence
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Unit Price
~
Amount used in advertisement
Income
Units Sold
Profit
Expense
Unit Cost
Fixed Cost
FIGURE 5.1 An Influence Diagram for the Profit Model.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
18
Science
related
college
succeed
Nonscience
related
college
Graduation
fails
IT
Basic
science
Medicine related
practical college
literature
Languages and
admin.
IT jobs
teaching
Hospitals and
labs
teaching
Admin.
Remain at university
Modeling with Spreadsheets
• Flexible and easy to use
• End-user modeling tool
• Features what-if analysis, data
management, macros
• Seamless and transparent
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Decision Tables
• Decision tables are a convenient way
to organize information in a
systematic manner. It can be done in
a spreadsheet and thus looks like a
table
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Decision Tables
• Multiple criteria decision analysis
• Features include:
– Decision variables (alternatives)
– Uncontrollable variables
– Result variables
• Applies principles of certainty,
uncertainty, and risk
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Example.
An investment company is considering investing in one of three alternatives:
bonds, stocks, or certificates of deposit (CDs). The company is interested in one
goal: maximizing the yield on the investment after one year. If it were interested
in other goals, such as safety or liquidity, the problem would be classified as
one of multi-criteria decision analysis. The yield depends on the state of the
economy sometime in the future (called the state of nature), which can be in
solid growth, stagnation, or inflation. Experts
estimated the following annual yields:
• If there is solid growth in the economy, bonds will yield 12%, stocks
15%, and time deposits 6.5%.
• If stagnation prevails, bonds will yield 6%, stocks 3%, and time
deposits 6.5%.
• If inflation prevails, bonds will yield 3%, stocks will bring a loss of 2%,
andtime deposits will yield 6.5%.
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Treatment the risk
The most common method for solving this risk analysis problem is to select the
alternative with the greatest expected value.
Assume that the experts estimate the chance of solid growth at 50%, the
chance of stagnation at 30%, and the chance of inflation at 20%. The decision
table is then rewritten with the known probabilities (see next table). An expected
value is computed by multiplying the results (outcomes) by their respective
probabilities and adding them. For example, investing in bonds yields an
expected return of
12(0.5)+6(0.3)+3(0.2)=8.4%
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Decision Tree
• It is a schematic presentation of
alternatives,
their
potential
consequences with their probabilities
and payoffs. It is usually used to
describe
several
interrelated
decisions over several years.
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Decision Tree
• Graphical representation of
relationships
• Multiple criteria approach
• Demonstrates complex relationships
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Classification
–Classification: classifies data (constructs a model) based on the training set
and the values (class labels) in a classifying attribute and uses it in classifying
new data.
• Given a database D={t1,t2,…,tn} and a set of classes C={C1,…,Cm}, the
Classification Problem is to define a mapping f:DC where each ti is assigned to
one class.
– Actually divides D into equivalence classes.
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Training Dataset
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Output: A Decision Tree for
“buys_computer”
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MSS Mathematical Models
• Link decision variables, uncontrollable
variables, parameters, and result
variables together
– Decision variables describe alternative
choices.
– Uncontrollable variables are outside
decision-maker’s control.
– Result variables are dependent on
chosen solution and uncontrollable
variables.
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Multiple Goals
• Simultaneous, often conflicting goals
sought by management
• Determining single measure of
effectiveness is difficult
• Handling methods:
– Goal programming
– Linear programming with goals as
constraints
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Example
• Is employee salaries. This constitutes
a decision variable for management.
It determines employee satisfaction
(i.e., intermediateoutcome), which in
turn determines the productivity level
(i.e., final result).
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Sensitivity, What-if, and Goal
Seeking Analysis
• Sensitivity
– Allows for adaptability and flexibility
– Eliminates or reduces variables
– Can be automatic or trial and error
• What-if
– Assesses solutions based on changes in
variables or assumptions
• Goal seeking
– Backwards approach, starts with goal
– Determines values of inputs needed to achieve
goal
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The role of heuristics in
modeling
• The role of heuristics in modeling is to
arrive at satisfactory solutions more
quickly and less expensively than
trying to get optimal solution.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Search Approaches
• Heurisitic
– Repeated, step-by-step searches
– “Good enough” solution, but, eventually,
will obtain optimal goal
– Examples of heuristics
• Tabu search
– Remembers and directs toward higher quality
choices
• Genetic algorithms
– Randomly examines pairs of solutions and
mutations
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Simulation
• Technique for conducting experiments with a
computer on a model of a management system
• Frequently used DSS tool
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
47
Simulations
•
•
•
•
•
•
Imitation of reality
Allows for experimentation and time compression
Can include complexities, but requires special skills
Handles unstructured problems
Optimal solution not guaranteed
Methodology
–
–
–
–
–
–
–
Problem definition
Construction of model
Testing and validation
Design of experiment
Experimentation
Evaluation
Implementation
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Simulations
• Time-dependent or time-independent
• Visual interactive modeling
– Graphical
– Decision-makers interact with simulated
model
– may be used with artificial intelligence
• Can be objected oriented
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Define visual simulation and compare it
to conventional simulation.
• Visual simulation uses graphical representation to
show the situation to the end-user. It does
everything of a conventional simulation, which is
any technique for conducting experiments (such
as what-if analyses) with a digital computer on a
model of a management system, but does it using
visually pleasing representation.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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model base management
system
• A model base management system
calls for a software package with
capabilities similar to those of a
DBMS. An effective MBMS will make
structural and algorithmic aspects of
model organization and associated
data processing invisible to users of
the MBMS.
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Model-Based Management System
• Software that allows model organization
with transparent data processing
• Capabilities
–
–
–
–
–
–
DSS user has control
Flexible in design
Gives feedback
GUI based
Increase in consistency
Communication between combined models
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•
Distinguish between a static model
and a dynamic model. Give an
example of each.
In a static model we look at one set of
input data and one set of output
data at one point of time. In dynamic
models one looks at several
possible inputs and outputs over an
extended period.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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•
What is the role of heuristics in
modeling?
The role of heuristics in modeling is to
arrive at satisfactory solutions more
quickly and less expensively than
trying to get optimal solution.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
4-54
•
Define visual interactive modeling
(VIM).
VIM uses computer graphic displays to
present the impact of different
management decision.
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• What is a model base management
system?
A model base management system calls for a
software package with capabilities similar
to those of a DBMS. An effective MBMS
will make structural and algorithmic
aspects of model organization and
associated data processing invisible to
users of the MBMS.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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•
What is the relationship between
environmental analysis and problem
identification?
A manager scans the environment to
identify any problems that may exist
and require addressing. In a way,
the manager is “looking for trouble”
(or opportunities!).
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• What are the major types of models
used in DSS?
•
• The major types are: optimization,
financial, statistical, simulation, static,
or dynamic. They can be standard or
custom made.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• What is an influence diagram? What
is it used for?
•
• It is a diagram that shows all the
variables in a model, what type they
are, and how they are related to each
other. For example, it will show you
that profit is determined by revenues
and expenses
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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