August 24, 2010

What are the Characteristics of Simulation Systems?

Simulation Systems can be characterized in numerous ways depending on the characterization criteria applied. Some of them are listed below.
Deterministic Simulation Systems
Deterministic Simulation Systems have completely predictable outcomes. That is, given a certain input we can predict the exact outcome. Another feature of these systems is idempotency, which means that the results for any given input are always the same.
Examples include population prediction models, atmospheric science etc.
Stochastic Simulation Systems
Stochastic Simulation systems have models with random variables. This means that the exact outcome is not predictable for any given input, resulting in potentially very different outcomes for the same input.
Static Simulation Systems
Static Simulation systems use statistical models in which time does not play any role. These models include various probabilistic scenarios which are used to calculate the results of any given input. Examples of such systems include financial portfolio valuation models. The most common simulation technique used in these models is the Monte Carlo Simulation.
Dynamic Simulation Systems
A dynamic simulation system has a model that accommodates for changes in data over time. This means that the input data affecting the results will be entered into the simulation during its entire lifetime than just at the beginning. A simulation system used to predict the growth of the economy may need to incorporate changes in economic data, is a good example of a dynamic simulation system.
Discrete Simulation Systems
Discrete Simulation Systems use models that have discrete entities with multiple attributes. Each of these entities can be in any state, at any given time, represented by the values of its attributes. . The state of the system is a set of all the states of all its entities.
This state changes one discrete step at a time as events happens in the system. Therefore, the actual designing of the simulation involves making choices about which entities to model, what attributes represent the Entity State, what events to model, how these events impact the entity attributes, and the sequence of the events. Examples of these systems are simulat
ed battlefield scenarios, highway traffic control systems, multiteller systems, computer networks etc.
Continuous Simulation Systems
If instead of using a model with discrete entities we use data with continuous values, we will end up with continuous simulation. For example instead of trying to simulate battlefield scenarios by using discrete entities such as soldiers and tanks, we can try to model behavior and movements of troops by using differential equations.
Social Simulation Systems
Social simulation is not a technique by itself but uses the various types of simulation described above. However, because of the specialized application of those techniques for social simulation it deserves a special mention of its own.
The field of social simulation involves using simulation to learn about and predict various social phenomenon such as voting patterns, migration patterns, economic decisions made by the general population, etc. One interesting application of social simulation is in a field called artificial life which is used to obtain useful insights into the formation and evolution of life.

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