1. Decision making, systems, modeling and support
University of Khartoum
Faculty of mathematical sciences
5th IT
Lecture 3

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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%)

8. 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.

9. 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.

10. Decision making and problem solving considerations
To differentiate between problem solving and decision making lets consider the phases of decision making.
1- Intelligence 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).

11. 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).

12. Decision Styles and Decision makers
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.

13. 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.

14. Decision makers Individual decision makers: Group 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.

15. 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.

16. Iconic (Scale) Models
An iconic model also called the scale model-the 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 two-dimentional iconic models.

17. 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.

18. 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 time-consuming.
More abstract models are described mathematically.
Most DSS analyses are performed numerically with mathematical or other quantitative models.

19. 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.

20. 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]

21. Phases of the decision-making process
It is advisable to follow a systematic decision-making 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.

22. Organization objectives Search and Scanning Procedures Data Collection
Intelligence Phase
Organization objectives
Search and Scanning Procedures
Data Collection
Problem Ownership
Problem Classification
Problem Statement
Reality
Simplification
Assumptions
Problem Statement
Design Phase
Formulate a model
Set criteria for choice
Search for alternatives
Predict and measure outcomes
Validation of the Model
Success
Alternatives
Verification, testing of
Proposed solution
Choice Phase
Solution to the model
Sensitivity analysis
Selection to best [good]
Alternative(s)
Plan for implementation
Implementation of solution
Solution
Failure

23. 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.

24. 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

25. 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)

26. Are decision makers really rational?
A lot of debate among researchers arose from this question
Suboptimization concept.

27. 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.

28. Good enough, or satisficing
Simon has devised the law of bounded rationality (research – next week).
Reasons for bad decision making.

29. 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.

30. 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.

31. Risk Risk is related to uncertainty.
Risk can be internal (good employee quitting), natural disaster (floods, volcanoes).
The risk will affect the choice.

32. 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.

33. Possible Scenarios The worst possible The best possible
The most likely
The average

34. 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.

35. How decisions are supported
Databases, data warehouses and data marts are specially important technologies in supporting all phases of decision making.

36. How decisions are supported
Intelligence
Design
Choice
Implementation
ANN
MIS
Data mining
ES ERP
ESS ES SCM
CRM ERP KMS
Management Science
ESS ES
KMS ERP

37. New technology support for Decision making
Web based systems
PDAs.
M-commerce.