[The seven habits of highly defective simulation projects]

by

Table of Contents

LITERATURE REVIEW3

Simulation3

Uses of Simulation5

Design5

Analysis6

Training6

The Simulation Process7

Define Problem Space7

Define Conceptual Model8

Collect Data8

Construct Software Model9

Verify, Validate, and Accredit the Model9

The seven habits of highly defective simulation projects9

Trifle-worship10

Belief in answers11

Connectionism13

The black box mistake15

The dead fish fallacy18

The Jehovah problem19

METHODOLOGY22

Summary of research methods22

Research Design23

Research Philosophy25

Research Approach27

Data Collection Method29

Research Instrument32

Sampling Method37

Participants and Sample Size37

Rationale for selection of sample39

LITERATURE REVIEW

The simulation projects provide a very essential way of tying the learning outcomes together in a class of simulation. According to research, a good project requires excellent skills as it is a very challenging task. The instructors of simulation projects are always looking for interesting and innovative ideas for their project. If they don't, then they end up in making a poor simulation project. (Weinberg, 1975) provides the instructors of simulation project with couple of good applications related to simulations. The two applications that are discussed revolves around a web site design project and a supply chain project. The website design problem is based on the problem mentioned in the textbooks and the other application i.e. supply chain project is related to real company problem. both of these applications are studied and discussed in great detail and their links are given to other simulation instructors so that they can review possible solutions and handouts (Salt, 1993).

Simulation

The whole process of designing and making a real model from an imagined system and conducting experiments with this model in order to understand and grasp the behaviour of a particular system is known as Simulation. During the process of simulation, assumptions and algorithms are designed in a way that it gives a real output of an imaginary system. The designing of assumptions and algorithms is known as modelling. For example take a simple equations of Distance= Rate x Time, is an analytical solution which represents the distance covered by a particular object at a constant rate for given period of time (Rosetti, 2003).

A simulation is a simplified (but adequate) model that represents how a system works. The system can be an existing, real-world one, such as a stock market or a human heart, or a proposed design for a system, such as a new factory or even a space colony.

If a system is simple enough (a cannonball falling from a height, for example), it is possible to use formulas such as those provided by Newton to get an exact answer. However, many real-world systems involve many discrete entities with complex interactions that cannot be captured with a single equation. During the 1940s, scientists encountered just this problem in attempting to understand what would happen under various conditions in a nuclear reaction (Senge, 1990).

Together with physicist Enrico Fermi, two mathematicians, John von Neumann and Stanislaw Ulam, devised a new way to simulate complex systems. Instead of trying fruitlessly to come up with some huge formula to "solve" the whole system, they applied probability formulas to each of a number of particles—in effect, rolling the dice for each one and then observing their resulting ...