# 1. Principles of Monte Carlo Simulation

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# Monte Carlo Simulation

## 12 lessons , 3 exercises

Overview

Monte Carlo simulation can be useful in many situations and industries. Learn what it is and how it works in this lesson.

Summary

1. Lesson Goal (00:08)

The goal of this lesson is to learn about the principles of Monte Carlo simulation.

2. When Monte Carlo Simulation is Useful (00:15)

Monte Carlo simulation can be used in any business situation where there is uncertainty over future outcomes. For example, when developing a product, we want to know if the new product will be profitable. There is uncertainty over the demand for the product, the development time of the product, and the costs associated with this development.

A simple method of dealing with this issue is to calculate profit in different scenarios, such as a best-case, worst-case, and base-case scenario. However, this method can’t account for every possible scenario, and it doesn’t tell us anything about how likely each scenario is.

3. Understanding Monte Carlo Simulation (01:22)

A simulation is a model of a possible outcome for a business process. A Monte Carlo simulation is a repeated simulation of a business process that is used to analyze all the possible outcomes of the process, and the probability of outcomes of interest.

When using Monte Carlo simulation, we simulate the problem a large number of times. This ensures that all the possible outcomes are likely to appear in the simulation. It also allows us to analyze an outcome of interest by determining how often it occurs in our large body of simulations.

4. Monte Carlo Simulation Examples (02:24)

This course considers three examples of Monte Carlo simulation. The first example analyzes the outcomes from betting on a roulette wheel. The second considers the length of a product development process. The third focuses on the potential profits from a new product.

Transcript

Monte Carlo Simulation

Contents

02:55

04:55

04:57

06:27

06:15

02:48

06:44

05:53

04:50

06:10

07:25

#### 12. Determining the Appropriate Distribution

06:29

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