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Simulation and Modelling > Introduction > System, Models, and Ways to study a system

System: A collection of entities (people, parts, messages, machines, servers, …) that act and interact together toward some end.

"A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and expressed in its functioning." - from Wiki

Model: A model is not the real world but merely a human construct to help us better understand real world systems. Usually, it is smaller and sometimes much smaller than the original but the exact copy of something that posses similar properties.

State of a System: A set of variables used to describe the behavior of the system at a particular time. State of a system is the collection of variables and their values that are necessary to describe the system at a particular time .

Might depend on desired objectives, output performance measures
Website model: Number of client requests, number of server processes, network bandwidth etc.
Game model: Weapons own by each characters, energy level etc.

Ways to study a System:

Analytical solution:

An analytical solution involves framing the problem in a well-understood form and calculating the exact numerical solution. A numerical solution means making guesses at the solution and testing whether the problem is solved well enough to stop.

E.g., estimating total $$$ spent per month on buying magazines
E.g., characterizing average waiting time in a bank by Queuing Theory

Advantages:

Clean answers in a cost-effective way
Can (relatively) easily explore how to optimize the performance
Getting nice close formed solutions which gives an instant insight
Don't require heavy computation which is of course time and resource intensive

Disadvantages:

Only work with tractable models (not applicable to most real systems)
Only explore the specific performance metric (you may as well be interested in many other metrics, simultaneously)
Bias in selection of study population.
A priori unknown level of experimental effect.
Measurement errors (income; energy intake)