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The Monte Carlo simulation is characterized by its use of random sampling to obtain numerical results and operates by defining a range of values for each input variable through minimum and maximum parameters. This technique allows for the exploration of various scenarios by randomly generating values within the specified ranges for the input variables. By running numerous iterations of the simulation, Monte Carlo provides a distribution of possible outcomes, enabling decision-makers to understand the variability and risk inherent in their models.

The other types of simulations mentioned do not specifically entail the use of minimum and maximum parameters in the same way. Deterministic simulations produce a single fixed outcome based on predefined inputs without randomness, while statistical simulations typically focus on analyzing data based on statistical methods but do not inherently incorporate the range of minimum to maximum inputs like Monte Carlo. Dynamic simulations involve changing systems over time but do not necessarily operate under defined input ranges in the same random sampling manner as a Monte Carlo simulation.