a way to calculate the probability of thousands of potential outcomes through sheer brute force"

Monte Carlo simulation is a statistical technique that uses random sampling to model and analyze complex systems or processes that involve uncertainty. The primary goal of Monte Carlo simulation is to assess the range of possible outcomes and their associated probabilities in a given situation. The technique developed by mathematicians Stanislaw Ulam and John Von Nuemann, both members of the Manhattan Project,  has been in use across industries for decades. 

Imagine two dice represent the unpredictability of the weather. One die determines the likelihood of rain (1 to 6), and the other die represents the wind strength (1 to 6). The total outcome of both dice will give you the specific weather conditions.

Roll the dice repeatedly to simulate different weekends. Each time, you get a unique combination of rain and wind conditions.

For each set of weather conditions, decide whether to go ahead with the picnic or cancel. You might decide not to go if it's very windy or if there's a high chance of rain.

Roll the dice hundreds or thousands of times, simulating a multitude of possible weekends. Keep track of how many times the weather is picnic-friendly or not.

After many rolls, you'll have a collection of scenarios. Now, you can see the likelihood of having a successful picnic based on the various combinations of rain and wind conditions.

Armed with this information, you can make an informed decision about whether to plan the picnic, considering the likelihood of good weather. You might also identify specific weather conditions that pose the most risk to your picnic plans.