>> Welcome back to An Introduction to Spreadsheets and Models. My name is Don Huesman. I'm the managing director of Wharton Online. In the late 1940s, early computer scientist John Von Neumann worked here at the University of Pennsylvania to develop a program that included probabilistic variables in models. He developed that program for use on ENIAC, the first general purpose electronic digital computer. ENIAC is actually still on display here on Penn's campus in Philadelphia in a building a few blocks away from where I'm speaking to you. Here's a picture of ENIAC in Von Neumann's time. Programming the computer to run calculations was done physically at first, by wiring specific connections between nodes on the computer. The electrical requirements of running the machine were so large, that when programs on ENIAC were run, lights dimmed across all of West Philadelphia. One of the more important programs that Von Neumann wrote and ran on ENIAC supported the work being done at Los Alamos in the 1940's and 50's and engineering designs from nuclear bombs. At Los Alamos, scientists were using deterministic computer models to forecast the actions of neutrons in a nuclear reaction. Von Neumann programmed ENIAC to run models that included an algorithm, that is a computer program, which introduced probability into the Los Alamos model. The algorithm came to be known as the Monte Carlo simulation. The name was taken from the gambling center in Europe and was intended to suggest the gamble that is involved when taking action in uncertain circumstances. As it happens, most circumstances in business involve variables that are uncertain or at least many do. Today, Monte Carlo simulations are available to anyone with a computer and a spreadsheet application. In this module, we will implement Monte Carlo simulations in spreadsheet models and review some examples of their use. In addition, we'll look further at the topic of linear programming reviewing the types of problems revealed by linear programs. We will also learn how to use Excel to determine the best decisions to make in scenarios we have modeled given a set of assumptions about the environment and a specific objective. I will be using Excel for these demonstrations, including the data analysis toolpak. If you don't have Excel, similar capabilities are available through Google Sheets and the XLMIner Analysis Toolpak. I will also be using another Excel add in called Solver, to demonstrate linear programming models in spreadsheets.