The article explores when we can predict the probability of one thing happening based on the probabilities of other things. If the probabilities of related events can be separated, we can make predictions more easily. This separation helps us understand how different parts of a system interact. By breaking down a system into separate parts, we can predict future probabilities more accurately. However, real-world observations can make this separation challenging, making it hard to monitor dynamic systems accurately.