ITGS Syllabus

Tuesday, May 01, 2007

Topic 213

Modelling and simulation: digital experimentation, demographic and environmental simulation by Nitish

Definitions for Modeling:
Modeling: a preliminary sculpture in wax or clay from which a finished work can be copied
Model: the act of representing something (usually on a smaller scale)

Definitions for Simulation:

Simulating a design through software programs that use models to replicate how a device will perform in terms of timing and results.

Simulation – creating computer versions of real-life

Simulation is mimicry. Never as good or true as reality but as good as the basic model governing it (our synthesized perception of reality) can be. Notice that modeling and simulation are necessarily close-tied together. Simulation allows us to experiment with a "reality" that is, for example, too dangerous to let happen, not yet realized or hypothetical, in the course of development (where we want to know more about e.g., feasibility, performance, reliability, etc.), and so on. (WWW)

Simulation is very important for network performance evaluation. In fact studies show simulation is a very important tool for decision making too. Simulation can be used in various fields - contention, collisions, momentary network overload, crash of a node, etc.

Impact of Simulation:
We hear simulation studies regarding nuclear waste disposal, water management in developing countries, and so on. We all use simulators for our training, studies and most importantly, decision making.

However, simulation is still considered by many as the "method of last resort." When all other methods fail, we use simulation. To go one step further, in some cases (flawed) simulation results are used as justification, or should I say alibi, for decisions that otherwise would not stand any criticism. Is it time to coin the expression "lies, damn lies, and simulation?"

The size of the lie depends on the knowledge that we can put in the model. In this way, we can cover the whole field from realistic performance assessment to pure conjecturing. The first applies to "hard" systems (technical like cars, airplanes or computers without taking human interaction into account); the second to "soft" systems (with living, e.g., human, components like we find in sociological models, economic models, interaction models, etc.) It all depends on how far we can get with validation, i.e., proving that the model or the simulation is "right" by comparing the results with real-life experiments. Unfortunately, in many cases, the required experiments are impossible to carry out, at least with the prerequisite degree of detail and accuracy that is needed for creating sufficient trust in the model. It is especially in these cases where the modeler/simulationist has to be candid about the liberties he has taken and the relative merits of his/her results.

How will a code of conduct change common practices and how much time will be needed to achieve that?

A code of conduct, of course, will not automatically change common practice. Only if there is an understandable penalty on misbehaving (however small the penalty is, e.g., membership of professional society being rescinded after warning), will such a code have any influence. The other side of this is that as soon as the public starts up malpractice cases for professionals acting against the Code(s) of Conduct, the professionals will, in general, hurry to conform. It must be noted, however, that even in professions where this occurs already (medical doctors, accountants, etc.) there still are (groups of) individuals that do not toe the line. Human nature and "money is the root of all evil," I presume. In some cases, you need a federal (or global?) authority to force people to behave (case in point: the SEC in the US).


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