1. Chapter 7: Models Introduction 7.1 Model progression 7.2 How to model
7.3 Why use models?
7.4 Conclusion

2. Introduction Captures the essential aspects of a system or process
Has to be clear and concise
Using mathematical notation is ideal but not essential
Model may
Provide different views on the same system
Describe a system as consisting of various components
Examples
ISO OSI
Mathematical model for access rights: Tuple (s. o. m)

3. A model for the interaction between computer attitude and computer anxiety

4. 7.1 Model progression Tentative model (Clarification stage)
Differentiated model
Address specific form of the problem
General model
Models that cater for most of the assumptions made by earlier models

5. 7.1.1 Clarification
A new problem arises that begs for a model to clarify it.
For a new paradigm
New solutions for problems that have already been solved
New solutions for existing problems

6. 7.1.2 Differentiation Are the proposed models adequate?
Investigate the assumptions, starting points and restrictions of proposed models
Try to propose a model that differs from existing models
Motivate why it is better
Why is your model needed?

7. 7.1.3 Generalisation
Develop a model that can do everything that all the others can do
Sometimes surprisingly simple, e.g. OSI

8. Work, Insight, Iteration, and luck
7.2 How to model
Work, Insight, Iteration, and luck
By design
Use common design methods and principles
Identify major components of a software system
Use dataflow techniques
By metaphor
Analogy to the real world: Actors, brokers, clients, even zombies, files, windows, editors, etc.
If car is a metaphor for something in the computer world, then driving, parking, servicing, crashing, washing or towing will be applicable as well.
Sometimes the real world example resembles the computing concept so closely that the metaphor is obvious
By formalisation
Choose tool carefully
Alternatives: Set theory, logic, algebra, formal languages, automata theory
By genius / luck
Think of your problem before going to bed and sleep on it

9. 7.3 Why use models? Simplicity Comprehensiveness Generality Exactness
Miller’s rule: A human can only have 72 concepts in short term memory at a time
Comprehensiveness
Models can systematically address all aspects of a problem
Generality
Models can address numerous variations of problem
Exactness
If the model fits the problem closely, it is likely to be accepted
Clarity
Purpose of all components and operation of every component, interaction between components should be evident