Simulations are simplifications of complex objects (like the planets) or phenomena (like tornadoes) for a stated goal. They often use varying sets of values to reflect how a phenomenon changes. Using a computer, we can simulate everything from a science lab to a nuclear explosion to a zombie apocalypse, and computer simulations are used in industries like weather forecasting and financial planning.

In order to develop a simulation, you have to remove certain real world details (like language barriers in a historical simulation event) or simplify how something functions. 

Abstraction in Simulations

Simplifying details to highlight a main point, where have we heard this before? 🤔

That's right, simulations are an example of abstraction.

There are many benefits to creating a simulation. They can be used to represent real-world events and conditions, like the force of gravity or the atmospheric conditions of a battle, so you can investigate and draw conclusions about them without dealing with some of the complications of the real world. Simulations are the most useful when observing the phenomenon in real life would be impractical, like if what you wanted to study was too big (Big Bang, continental drift) or too small (atoms, elements).

However, simulations also have some disadvantages. They run the risk of being too simple or conveying the wrong message about what you're trying to study (simulating the planets with tennis balls, for example, may lead people to think they're closer to each other and more similarly sized than they actually are).

In order to fit all the planets onto the screen, this simulation of planetary motion makes the planets closer to each other than they are in the real world. Image source: ESA on Giphy

Simulations may also contain bias based on what the simulation creator chose to include or exclude.

Random number generators can help simulate real-world variability in these simulations: it's a little like rolling a pair of dice.