How does the Monte Carlo simulation assess outcomes?

The Monte Carlo simulation assesses outcomes by selecting variable values randomly and running multiple iterations of the model. This approach allows for the incorporation of uncertainty and variability in the inputs, reflecting a range of possible outcomes based on different scenarios. By simulating numerous random samples, the method captures the breadth of potential results and helps in understanding the likelihood of various outcomes.

This randomness is essential because it considers the impact of different risk factors and scenarios that could affect the model's results, providing a more comprehensive view of potential future states than a single deterministic calculation. By generating thousands or even millions of iterations, the simulation can create a distribution of possible outcomes, from which probabilities and expected values can be derived. This makes it particularly useful in fields like finance and risk assessment, where future conditions can be unpredictable.

Other approaches that rely on fixed input values or deterministic assumptions limit the analysis to a singular outcome without accounting for the inherent uncertainties of real-world scenarios, which is contrary to the Monte Carlo method's capability to evaluate a range of possibilities.