What kind of scenarios are typically tested using stochastic simulations?

Stochastic simulations are primarily used to model systems that involve inherent randomness and uncertainty. In these types of simulations, outcomes are not predetermined; instead, they are generated through random processes. This allows for the analysis of how different random variables and uncertainties can impact the results of a model.

By using randomly generated outcomes, stochastic simulations can provide insights into the range of possible scenarios a system might encounter, including the probabilities of various outcomes occurring. This is particularly useful in fields such as finance, operations research, and risk management, where decision-makers need to account for uncertainty in their forecasts and strategies.

In contrast, scenarios with guaranteed outcomes would not fit the stochastic framework, as they lack the element of randomness. Similarly, pre-determined or sequential outcomes would not capture the variability that stochastic simulations seek to explore. Thus, the focus on generating random outcomes is what makes option B the correct choice in the context of stochastic simulations.