I'm a big fan of Monte Carlo simulations, and highly encourage their use. But is important to remember that there are a few cases where a Monte Carlo model (or, as far as that goes, any simulation probably) can be pretty off, perhaps to the point of being useless, or even misleading in a damaging way:<p>Off-hand, I can think of two big ones:<p>1. Missing variables. Of course this doesn't matter if you're only working with one variable, like estimating the value of pi. But if you have a multi-variable model, and your model is missing one or more variables that affect - in real life - the thing you're trying to simulate, then the model may be less than worthless. This is why it's so crucial to be sure that you've identified all the variables. Unfortunately, for complex real-world scenarios, that's often very difficult.<p>2. If the model (rather, the output from the model) itself affects the real-world domain you're simulating.<p>That said, MC is a very powerful and useful technique and it's worthy of being in everyone's tool-belt.