Decision making, systems,
modeling and support
University of Khartoum
Faculty of mathematical sciences
5th IT
Lecture 3
Learning objectives
• Understanding the conceptual foundation of
decision making.
• Understanding Simon’s four phases of decision
making.
• Recognize the concepts of rationality and
bounded rationality and how they related to
decision making.
• Understand models.
• Learn how DSS for decision making can be
provided in practice.
Opening vignette: Decision making at
the U.S. Federal Reserve
• Overview of the vignette. (Alan Greenspan).
• Lessons learned from vignette:
– Individuals are responsible for making decisions.
– There may be many of alternatives to consider.
– A decision maker needs data and detailed
analysis and understanding of the data in order
to make a good decision.
– Complicated decisions require computer systems
to access data and run analyses.
Lessons learned from opening vignette
– Teams of analysts may need to sift through data
and run analysis.
– Past results and data may be insufficient to predict
future results.
– Human judgment is often necessary to achieve
superior results.
– The result of making business or government
policy decisions usually materialize in the future.
No one is a perfect predictor of the future.
Lessons learned from opening vignette
– Decisions are interrelated, A specific decision may
affect many individuals and groups within and
even outside the organizational system.
– Decision making involves a process of thinking
about the problem leading to the need for data
and modeling of the problem.
– There can be several objectives, and they may
conflict with one another.
Lessons learned from opening vignette
– Many decisions involve risk, different people have
different attitudes towards risk.
– Feedback is an important aspect of decision
making.
– A decision may be the responsibility of a group.
– Group members may have biases.
– Empowering a group leads to better decisions.
Decision making Introduction and definitions
Characteristics of decision making
• Making better decisions does not necessarily
means making decisions more quickly.
• Statistics about area suffering from quick decision
making (2001):
–
–
–
–
–
–
Personnel/Human resources (27%)
Budgeting finance (24%)
Organizational structuring (22%)
Quality/Productivity (20%)
IT selection and installation (17%)
Process improvement (17%)
Decision making Introduction and definitions
Characteristics of decision making
• DSS based on 3 key words:
– [Decision, Support, Systems]
– Decision makers should not simply apply IT tools
blindly. Rather, the decision maker gets support
through a rational approach that simplifies reality
and provides a relatively quick and inexpensive
means of considering various alternative courses
of actions to arrive at the best (or at least very
good) solutions to the problem.
Working definitions of decision
making
• Decision making is a process of choosing among
two or more alternative courses of actions for
the purposes of attaining a goal or goals.
• According to Simon (1977), decision making is
synonymous with the whole process of
management. [Planning a management process
that involves When? Where? Why? How? by
whom?].
• Other managerial functions such as organizing
and controlling involves decision making.
Decision making and problem solving
considerations
• To differentiate between problem solving and decision
making lets consider the phases of decision making.
1- Intelligence.
2- Design
3-Choice
4- Implementation.
• Some consider 1-4 as problem solving with 3 as real
decision making process.
• Some consider 1-3 as formal decision making ending
with recommendations, where problem solving
additionally including the actual implementation of the
recommendation (phase 4).
Decision making discipline
• Decision making is influenced by several major
disciplines some of which are behavioral (law,
philosophy, political science, physiology, social
psychology) and some of which are scientific
(Computer science, decision analysis, economics,
management since, operation researches).
• MSS emphasis on effectiveness (Doing the right
thing)or goodness rather than efficiency(Doing
the thing right).
Decision Styles and Decision makers
• Decision Style:
– Decision style: is the manner in which decision
makers think and react to problems. This includes
the way they perceive, their cognitive responses,
and how values and beliefs vary from individual to
individual and from situation to situation.
– There are no standard measures to these
behavioral parts.
Decision styles
• Decision making styles include
– Heuristic.
– Analytic.
– Autocratic.
– Democratic.
– Consultative.
– A person can be analytic and autocratic.
– A person can be consultative and heuristic.
Decision makers
• Individual decision makers:
– Decision are made by individuals, especially at lower
management levels and small organizations.
– There may be conflicting objectives. (example)
• Group decision makers:
– Most decisions in medium sized/large organizations
are made by groups.
– Collaborating individuals may have different cognitive
styles, personality types and decision styles.
– Supported by EIS (Enterprise information systems),
GSS, ERM, SCM, KMS, CRM.
Models and model classifications
• A major characteristics of DSS/BI systems is the
inclusion of at least one model.
• The basic idea is to perform the DSS analysis on a
model rather than on the real system.
• A Model is a simplified representation or abstraction
of reality. (it is usually simplified because reality is too
complex to describe exactly, and because much of the
complexity is irrelevant in solving a specific problem).
• Models can represent systems or problems with
various degrees of abstraction.
• Models are classified based on their degree of
abstraction as either iconic, analog or mathematical.
Iconic (Scale) Models
• An iconic model also called the scale modelthe least abstract type of models- is a physical
replica of a system. Usually in a different
scale from the original.
• An iconic model may be three-dimensional,
such as a model of an airplane, a car, a bridge,
or a production line, photographs are twodimentional iconic models.
Analog Models
• An analog model behaves like the real system but does not
look like it.
• It is more abstract than the iconic model and is a symbolic
representation of reality.
• Models of this type are usually two-dimensional charts or
diagrams. They can be physical models, but the shape of
the model differs from that of the actual systems.
• Examples are:
– Organization charts that depict structure, authority and
responsibility relationships.
– Map on which different colors represent objects, such as bodies
of water of mountains.
Mathematical Models
• The complexity of relationships in many
organizational systems cannot be represented by
icons
or
analogically
because
such
representations
would
soon
become
cumbersome and using them would be timeconsuming.
• More
abstract
models
are
described
mathematically.
• Most DSS analyses are performed numerically
with mathematical or other quantitative models.
Mental Models
• Decision makers sometimes develop mental
models, especially in time-pressure situations.
• Mental models are descriptive representation of
decision-making situation that people form in
their heads and think about.
• Their thought process works through scenarios to
consider the utility of and risk involved in each
potential alternative.
• They are typically used where there are mostly
qualitative factors in the decision-making
problem.
Benefits of Models
• MSS uses models for:
– Model manipulation.
– Models enable the compression of time.
– The cost of modeling analysis is much lower than
the cost of similar experiment conducted on a real
system.
– The cost of making errors in a trial-error in models
is much lower than in real systems.
[See other benefits in reference]
Phases of the decision-making
process
• It is advisable to follow a systematic decisionmaking process.
• Simon (1977) illustrated 4 decision phases
(intelligence, design, choice and later
implantation) (Illustrated in next slide).
• Monitoring can be the fifth phase, but it is
logically can be intelligence applied to the
implementation phase.
Reality
Simplification
Assumptions
Validation of the Model
Success
Verification, testing of
Proposed solution
Implementation
of solution
Failure
Intelligence Phase
Organization objectives
Search and Scanning Procedures
Data Collection
Problem Ownership
Problem Classification
Problem Statement
Problem Statement
Design Phase
Formulate a model
Set criteria for choice
Search for alternatives
Predict and measure outcomes
Alternatives
Choice Phase
Solution to the model
Sensitivity analysis
Selection to best [good]
Alternative(s)
Plan for implementation
Solution
Selection of a principle of choice
• Principle of choice: is a criterion that describe the
acceptability of a solution approach. In a model,
it is a result variable.
• Selecting a principle of choice is not part of the
choice phase, but involves how a person
establishes decision-making objective(s) and
incorporates objective(s) into model(s).
• Are we willing to assume high risk, do we prefer a
low risk approach?
• Difference between a criterion and a constraint.
Normative Models
• Are models in which the chosen alternative is
demonstrably the best of all possible
alternatives. (Optimization)
• Get the highest level of attainment from a
given set of resources.
• Find the alternative with the highest ratio of
goal attainment to cost.
• Find the alternative with the lowest cost
Normative Models
• Normative decision theory are based on the
following assumptions of rational decision
makers:
– Humans are economic beings whose objectives are to
maximize the attainment of goals.
– For a decision-making situation, all viable alternatives
courses of actions and their consequences are known.
– Decision makers have an order of preference that
enables them to rank desirability of all consequences
of the analysis (best worse)
Are decision makers really rational?
• A lot of debate among researchers arose from
this question
• Suboptimization concept.
Descriptive Models
• Descriptive models: describe things as they
are or as they believed to be.
– Simulation.
– Cognitive maps
– Narrative
– Financial planning.
– Waiting-line (queuing) management.
Good enough, or satisficing
• Simon has devised the law of bounded
rationality (research – next week).
• Reasons for bad decision making.
Developing (generating) alternatives
• Alternatives may be generated automatically
or manually.
• Manually generated alternatives takes time
and effort.
• Having a lot of alternative to chose from will
disrupt the process.
• Knowing when to stop generating alternatives
thus is very important.
Measuring outcome
• Measurement may be the number of satisfied
customers, the value of the overall profit.
• Value of alternatives are evaluated on terms
of goal attainment.
• AHP (Analytical Hierarchical Process) is used
when multiple-criteria is present.
Risk
• Risk is related to uncertainty.
• Risk can be internal (good employee quitting),
natural disaster (floods, volcanoes).
• The risk will affect the choice.
Scenarios
• Scenario: a statement of assumption about
the operating environment of a particular
system at a given time.
• Some scenarios benefits:
– Help identify opportunities and problem areas.
– Provide flexibility in planning.
– Identify the leading edges of changes that
management should monitor.
– Help validate major modeling assumptions.
Possible Scenarios
– The worst possible
– The best possible
– The most likely
– The average
Errors in decision making
• Validating the model before its use is critical.
• Gathering the right amount of information
with the right level of precision and accuracy
to incorporate into the decision making
process is also critical.
How decisions are supported
• Databases, data warehouses and data marts
are specially important technologies in
supporting all phases of decision making.
How decisions are supported
Intelligence
Design
Choice
Implementation
ANN
MIS
Data
mining
ES ERP
ESS ES SCM
CRM ERP KMS
Management
Science
ANN
ESS ES
KMS ERP
New technology support for Decision
making
• Web based systems
• PDAs.
• M-commerce.