  Many philosophers of science have deviated from ordinary scientific language here and have shied away from thinking of MC simulations as genuine simulations. Grüne-Yanoff and Weirich (2010) offer the following reasoning: “The Monte Carlo approach does not have a mimetic purpose: It imitates the deterministic system not in order to serve as a surrogate that is investigated in its stead but only in order to offer an alternative computation of the deterministic system’s properties” (p.30). This shows that MC simulations do not fit any of the above definitions aptly. On the other hand, the divide between philosophers and ordinary language can perhaps be squared by noting that MC simulations simulate an imaginary process that might be used for calculating something relevant to studying some other process. Suppose I am modeling a planetary orbit and for my calculation I need to know the value of π. If I do the MC simulation mentioned in the last paragraph, I am simulating the process of randomly dropping objects into a square, but what I am modeling is a planetary orbit. This is the sense in which MC simulations are simulations, but they are not simulations of the systems they are being used to study. However, as Beisbart and Norton (2012) point out, some MC simulations (viz. those that use MC techniques to solve stochastic dynamical equations referring to a physical system) are in fact simulations of the systems they study. 