Computer simulations and large-scale structure formation

~Scientific models and computer simulations are indispensable to scientific practice. Through~their use, physicists are able to learn about how the world works, and to discover~new information. However, there is a challenge in understanding how physicists can~generate knowledge from their use, stemming from the fact that simulations are necessarily incomplete representations and partial descriptions of their~target systems. In order to construct a simulation, one must make idealizations,~approximations, and abstractions.~In this talk, I focus on the role of idealization and representation in large-scale structure formation simulations. This case provides the opportunity to study the precise ways that idealization~and representational trade-offs enter into the construction of simulations, and how they~may determine values for simulation parameters. I argue that the use of simulation code~that is flexible enough to de-idealize representations plays a specific role in reasoning about~results in the context of astrophysics. This is particularly salient when the simulations aim to~connect a vast array of independent astronomical observations/phenomena to~cosmologists' more global arguments.