Intelligent Decision Support Methods
From the book-Intelligent
by Vasant Dhar and Roger Stein
Decision Support Methods
Information Systems

“I know of no commodity more valuable than information.”

Management Information System (MIS)
• Transaction Processing Systems
– Accurate Record Keeping
• Decision Support Systems (DSS)
– Model-Driven DSS
– Data-Driven DSS
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Intelligence Density

DEF: A Metric for Knowledge Work Productivity.

Knowledge Intensive organizations transform raw data
into something useful-knowledge-and deliver the
knowledge to the part of the organization where it can be
used most effectively.

Intelligence Density: How quickly can you get the essence
of the underlying data from the output?
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The Vocabulary of Intelligence Density

Quality of Model
• Accuracy, Explainability, Speed, Reliability..

Engineering Dimension
• Flexibility, Scalability, Ease of Use,...

Quality of Available Resource
• Learning Curve, Tolerances for Noise, Complexity,...

Logistical Constraints
• Independence from Experts, Computational Ease, Development Time,..
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Dimensions of Problems and Solutions

Intelligence Density Dimensions: Quality of Systems

How Well is the System Engineered?

Quality of Available Resources

Logistical Constraints
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Intelligence Density Dimensions:
Quality of Systems (1/2)

Accuracy
• measures how dose the outputs of a system are to the correct or best
decision. Can you be confident that the errors(results that are not
accurate)are not so severe as to make the sys-tem too costly or dangerous
to use?

Explainabilitv
• is the description of the process by which a conclusion was reached.
Statistical models explain the output to some degree in the sense that each
independent variable influences or ‘explains’ the dependent variable in
that it accounts for some portion of the variance of the dependent variable.
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Intelligence Density Dimensions:
Quality of Systems (2/2)
• Other systems, where rule-based reasoning is involved, show exp1icitly
how conclusions are derived, yet others, such as neural networks, generate
opaque mathematical formulas. These are sometimes referred to as 'black
boxes’, because for the user they are the mathematical equivalent of the
magician's black box: Data go in at one end and results come out the other,
but you cannot (easily) see the rationale behind the conclusion.

Response speed
• is the time it takes for a system to complete analysis at the desired level of
accuracy. The flip side to this dimension is confidence in the sense that
you can ask how confident you are that a certain period of time, within
which the system must provide an answer, will be sufficient to perform the
analysis. In applications that require that results be produced within a
specified timeframe, missing that time frame means that no matter how
accurate and otherwise desirable the results are, they will be useless in
practice.
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How Well is the System Engineered? (1/3)

Scalability
• involves adding more variables to the problem or increasing the range of
values that variables can take. For example, scalability is a major issue
when you're interested in going from a prototype system involving 10
variables to one with 30 variables. Scalability can be a real problem
when the interactions among variables increase rapidly in unpredictable
ways with the introduction of additional variables(making the system
brittle)or where the computational complexity increases rapidly.

Compactness
• refers to how small (literally, the number of bytes) the system can be
made.Once a system has been developed and tested, it needs to be put into
the hands of the decision makers within an organization. It must be taken
out into the field, be that the shop floor, the trading floor, or the ocean
floor.
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How Well is the System Engineered? (2/3)

Flexibility
•

is the ease with which the relationships among the variables or their domains can
be changed, or the goals of the system modified. Most systems are not designed to
be used once and then thrown away. Instead they must be robust enough to
perform well as additional functionality is added over time. In addition, many of
the business processes that you might model are not static (i.e., they change over
time). As a result, the ability to update a system or to have the system adapt itself to
new phenomena important.
Embeddability
•
refers to the ease with which a system can be coupled with or incorporated into the
infrastructure of an organization. In some situations, systems will be components
of larger systems or other databases. If this is the case, systems must be able to
communicate well and mesh smoothly with the other components of the
organization infrastructure. A system that requires proprietary software engineer,or
specific hardware will not necessarily be able to integrate itself into this
infrastructure.
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How Well is the System Engineered? (3/3)

Ease of use
• describes how complicated the system is to use for the businesspeople
who will be using it on a daily basis. Is it an application that requires a lot
of expertise or training, or is it something a user can apply right out of the
box?
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Quality of Available Resources

Tolerance for noise in data
• the degree to which the quality of a system, most notably its accuracy, is
affected by noise in the electronic data.

Tolerance for data sparseness
•

is the degree to which the quality of a system is affected by
incompleteness or lack of data.
Tolerance for complexity
• is the degree to which the quality of a system is affected by interactions
among the various components of the process being modeled or in the
knowledge used to model a process.

Learning curve requirements
• indicate the degree to which the organization needs to experiment in order
to become sufficiently competent at solving a problem or using a
technique.
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Logistical Constraints

Independence from experts
• is the degree to which the system can be designed, built, and tested
without experts. While expertise is valuable, access to experts within an
organization can be a logistical nightmare and can be very expensive.

Computational ease
• is the degree to which a system can be implemented without requiring
special-purpose hardware or software.

Development speed
• is the time that the organization can afford to develop a system or,
conversely, the time a modeling technology would require to develop a
system.
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Topics

Data-Driven Decision Support

Evolving Solutions: Genetic Algorithms

Neural Networks

Rule-Based Expert Systems

Fuzzy Logic

Case-Based Reasoning

Machine Learning
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Data-Driven Decision Support



OLTP: On-Line Transaction Processing
ISAM: Indexed Sequential Access Method, early DBMS
RDBMS: Relational Database Management Systems
• Data Normalization
• SQL: Sequential/Structured Query Language

EIS: Executive Information Systems
• Friendly & Intelligent User-interface

Data Warehousing and OLAP: On-Line Analytical Process
• LAN: Local Area Network
• Data Loader->Converter->Scrubber->Transformer->Warehouse->OLAP
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Evolving Solutions - Genetic Algorithms (I)

Optimization Problems:
• A set of problem variables
• A set of constraints
• A set of objectives

Example:
• ACME Transport, Inc., a shipping firm, needs to plan a delivery route that
will minimize the time and cost of the shipping, but at the same time ,
make delivers to all 10 of its overseas clients.
• Exhaustive Search: evaluate all possible 10! = 3,628,800 routes.
– Problem: If the number of clients increase to 25, then there are 25! = 1.55*10 25 possible
route. Therefore it will take a very fast computer (evaluate a million route per second) to
evaluate only 0.23% possible route in 4 billion years.
• Often not a LP problem
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The Example - Genetic Algorithms (II)

Possible constraints to the ACME problem
• Shipping costs must be less than 70% of fee charged.
• Customer waiting time must be less than 90 days.
• If a customer does more than $x of business with ACME then waiting
time must be less than 60 days.

Possible objectives to the ACME problem
•
•
•
•
Overall delivery time is minimized.
Overall profit is maximized.
Ship fleet wear is minimized.
Number of repeated country visits is minimized.
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The Origin - Genetic Algorithms (III)

GAs were originally developed by computer scientist John
Holland in the 1970s as experiments to see if computer
programs could evolve in a Darwinian sense.

GAs are very useful for solving classes of problems that
were previously computationally prohibitive, especially in
the area of optimization.

GAs is a heuristic techniques that cannot guarantee
optimal solutions. Only near optimal solutions can be
expected
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The Theory of Evolution - Genetic Algorithms (IV)

Basic Concept:
• Natural Selection, i.e., Survival

Different kromes will survive based on the compatibility of
their attributes with their environment. They are hunted by
their predators at night.

Each type of krome represents one solution to the survival
problem. Kromes with better attributes have higher
probability to survive and therefore reproduce,
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Introduction - Genetic Algorithms (V)

The smallest unit of a GA is called a gene, representing a
unit of information in your problem domain.

A series of these genes, or a chromosome, represents one
possible complete solution to the problem.

A decoder converts the chromosome into a solution to the
problem. (or interprets the meaning of a chromosome)

A fitness function then is used to determines which
chromosome solutions are good and which are not very
good.
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Introduction - Genetic Algorithms (VI)

A GA randomly creates an initial population of
chromosomes and evaluates their fitness.

A new generation (new population of chromosomes) is
created by combining and refining the information in the
chromosome using
• Selection
• Crossover
• Mutation

The process is repeated until a satisfactory solution is
found.
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Notes - Genetic Algorithms (VII)

Do not guarantee an optimal solution.

You can use a GA to solve problems that you don’t even
know hoe to solve. All you need to be able to do is
describe a good solution and provides a fitness function
that can rate a given chromosome.

How good a solution provided by a GA is determined by
how good the problem is formulated.
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Simulating the Brain to Solve Problems
- Neural Networks (I)

Learning preserves the errors of the past, as well as its
wisdom.

The Learning Process: Induction
• Data
• Generalization
• Model

The Example:
• Over the years, you must have a very good idea how much time you need
to spend on and how to prepare a quiz to get certain grade.
• That is, you build mental models based on the past experiences (data) by
generalization.
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The Origin - Neural Networks (II)

Neural networks were first theorized as early as the 1940’s
by two scientists at the University of Chicago (McColloch
and Pitts). Works was done in the mid-1950s as well
(McCarthy 1956; Rosenblatt 1957) when researchers
developed simple neural nets in attempts to simulate the
brain’s cognitive learning processes.

ANNs are simple computer programs that build models
from data by trial and error.

Very useful in modeling complex poorly understood
problems for which sufficient data can be collected.
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Nervous Systems - Neural Networks (III)

Our nervous systems consist of a network of individual but
interconnected nerve cells called neurons.

Neurons can receive information (stimuli) from the outside
world at various points in the network.

The information travels through the network by generating
new internal signals that are passed from neuron to neuron.
These new signals ultimately produce a response.

A neuron passes information on to neighbor neurons by
firing or releasing chemicals called neurotransmitters.
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Nervous Systems - Neural Networks (IV)

The connections between neurons at which information
transfers are called synapses.

Information can either excite or inhibit neurons.

Synaptic connection can be strengthened (learning) or
weakened (forgetting) over time with experience.

With repeated learning, one can generalize his/her
experience, modifying the response to stimuli, and thus
ultimately reach the level of reflexes.
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Introduction - Neural Networks (V)

ANN involves a system of neurons (or nodes) and
weighted connections (the equivalent of synapses) inside
the memory of a computer.

Nodes are arranged in layers:
• Input layer
• Hidden layer
• Output layer

Through learning (trial and error, propagating, other
algorithms), ANN adjusts the weights on each connections
to match the desired response (minimize the amount of
error).
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Training Steps of a Neural Network (1/2)



Step l: The network makes a guess based on its current weights and the input
data.
Step 2: The net calculates the error associated with the output (at the out,put
node). For example, if the desired output were 1, but the network output were
0, the error would be +1, based on the difference between l and 0.
Step 3: The net determines by how much and in what direction each of the
weights leading in to this node needs to be adjusted. How?
This is accomplished by calculating how much each of the individual weighted
inputs to the node contributed to the error,given the particular input value. So,
for example, if a node's output were too small, the net might need to
concentrate on (that is,increase) small or negative weights that lead up to that
node. In essence, the network feeds back the information about how well it's
doing to the neurodes in the net, and where possible problems might be.
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Training Steps of a Neural Network (2/2)


Step4: The net adjusts the weights of each node in the layer according to the
analysis in the previous step. For example, in the case where thc output was
too small, the neural network will try to increase the values of the positive
weights since that would make the weighted sum larger. This would bring the
output closer to 1, which is what you want in this case. Similarly, the neural
net should also try to decrease the size of the negative weights (or even make
them positive).
Step5:The net repeats the process by performing a similar set of calculations
(Step l-Step3)for-each node in the hidden layer below it. But since you
cannot tell the net what the desired output of each of the hidden nodes should
be (they are internal and hidden), the neural network does a kind of sensitivity
analysis to determine how large the error of each of these nodes is..
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Note - Neural Networks (VI)

No domain experts are needed, unlike Rule-based Systems
or Fuzzy Systems.

Excel at mapping relationships on to data that are noisy
and incomplete.

Need adequate learning rate step size.

Avoid over-training. (may accidentally learn from noise)
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Putting Expert Reasoning in a Box
- Rule-Based Systems (I)

Learn to reason forward and backward on both sides of a
question.

You can view much of problem solving as consisting of
rules.
•
•
•
•

Automobile/Car Repair
Medical Care
Accounting and Tax Practice
Quality Control
The most famous of RBS, XCON, developed in 1979 by
Digital Equipment Corp.
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The Basic Concept
- Rule-Based Systems (II)

CreditBank loan application example

IF employment stability is very low
AND credit history is very low
THEN credit risk is very high

The region that each rule applied as in Fig. 7.1 is called
problem space. Each cube is essentially a rule. In other
words, a rule “samples” a region of the problem space.
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The Basic Concept
- Rule-Based Systems (III)

The part before the “then” is referred to as the condition
part of the rule or the left-hand side (LHS), and the part
after the “then” as the action part, or the right hand side
(RHS).

Forward Chaining

Hypothesize

Backward Chaining
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The Basic Concept
- Rule-Based Systems (IV)

How the rules are used is flexible and is referred as the
control strategy.

Three basic components
• a rule base
• working memory
• a rule interpreter

Steps (Recognize-Act Cycle)
• Rules are matched against the data
• The interpreter selects one instantiated rule
• The selected rule is fired
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The Basic Concept
- Rule-Based Systems (V)

Differences between RBS and Decision Tree
•
•
•
•

How well you understand the problem at that time
Modification
RBS tells nothing about how to do things
One-direction Vs. Multiple directions
The order in which rules are processed affects the results
• Meta Rules
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Notes
- Rule-Based Systems

60% to 70% of the time taken to develop rule-based
systems is spent on knowledge acquisition.

Only worth considering when you have experts available.

What-if analysis using dependency network.

The difficulty: making the right rules to fire at the right
time.
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