The more complex a situation is, the harder it can be to estimate the probability of a particular event happening. Well-designed simulations are a way to estimate a probability in a complex situation, especially when it would be difficult or impossible to determine the probability from reasoning alone.
To design a good simulation, we need to know something about the situation. For example, if we want to estimate the probability that it will rain every day for the next three days, we could look up the weather forecast for the next three days. Here is a table showing a weather forecast:
|
today
(Tuesday) |
Wednesday |
Thursday |
Friday |
| probability of rain |
0.2 |
0.4 |
0.5 |
0.9 |
We can set up a simulation to estimate the probability of rain each day with three bags.
- In the first bag, we put 4 slips of paper that say “rain” and 6 that say “no rain.”
- In the second bag, we put 5 slips of paper that say “rain” and 5 that say “no rain.”
- In the third bag, we put 9 slips of paper that say “rain” and 1 that says “no rain.”
Then we can select 1 slip of paper from each bag and record whether or not the simulation predicts that there will be rain on all three days. If we repeat this experiment many times, we can estimate the probability that there will be rain on all three days by dividing the number of times all three slips say “rain” by the total number of times we perform the simulation.