A Monte Carlo Simulation involves the use of random Numbers and probability to find solutions to complex problems.

蒙特卡洛模拟涉及了使用随机数和概率来找到复杂问题的解决方案。

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So a Monte Carlo simulation uses essentially random inputs (within realistic limits) to model the system and produce probable outcomes.

于是 Monte Carlo 法使用基本上是随机的输入（带有一些现实的限制）去为系统建模，并产生可能的结果。

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To get a handle on a Monte Carlo simulation, first consider a scenario where we do not need one: to predict events in a simple, linear system.

要理解Monte carlo法，首先考虑一个实际上并无必要的场景：预测一个简单线性系统中的事件。

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So Sharpe did a Monte Carlo simulation of 1 million different 30-year investment outcomes, getting roughly the same difference in return.

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To do this, you could run a "Monte Carlo simulation" in which you plug historical weather data into a computer and then roll a pair of virtual dice 10, 000 times.

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Add in the effects of chance (what is known as the Monte Carlo simulation method), and you end up with what should be a plausible range of timescales and costs.

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