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Monte Carlo experiments may include assumptions such as the distribution, mean, median etc, but its not necessary. However, the very fact that the user decides whether to include assumptions and constraints or not doesn't this makes it a deterministic environment?

For example, if I decide to follow a stochastic process, then I already know that the outcome will be random. I'm not aware of the outcome itself, but taking no assumptions is already an assumption.

Therefore even if the process is stochastic it does not undo that the process itself is a "laboratory event". The most famous example of a stochastic process is the dice throwing. Given a formula to do a simulation, it inevitably be incomplete and it will not describe any possible case, such as different levels of gravity, any possible terrain, the case of walls, dice materials and structural problems, therefore we are forced to put constraints in order to construct our "laboratory".

Could you clarify me these definitions and explain me whether my point of view is true or false?

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  • $\begingroup$ are you asking how we can use deterministic sequences to model random phemomena? $\endgroup$ – user408433 May 23 '17 at 14:51
  • $\begingroup$ Wikipedia on stochastic simulation states "A stochastic simulation is a simulation that traces the evolution of variables that can change stochastically (randomly) with certain probabilities." The fact that certain probabilities are given, doesn't make it a deterministic process since we predetermine the randomness of the process and the possible variable evolutions? $\endgroup$ – Commissar Vasili Karlovic May 23 '17 at 16:09
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    $\begingroup$ You need to separate the model from the phenomenon: yest, the model has fixed parameters (its a description of the process), but the actual phenomenon being model evolves randomly. $\endgroup$ – user408433 May 23 '17 at 16:26

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