Modeling risks with little experience seems like a crap shoot. But, modeling variables where we have experience can be equally dangerous due to familiarity bias. When familiarity bias creeps into risk modeling, it can be a serious problem. What is Familiarity Bias? A concise definition of familiarity bias is that we tend to underestimate risks …
Presenting the results of a Monte Carlo simulation can be challenging when your audience isn’t inclined to receive probabilistic information. Decision makers often want a single number for net present value, project completion date, or profit. How do we as the modeler/analyst present this information so it is understood and appreciated for the extra information …
Simulating a cost estimate can be used to get an understanding of the potential range of outcomes. Cost estimates by their nature contain uncertainty and lend themselves well to simulation. In this article we’ll look at how to simulate an estimate and some items that need to be considered. Estimating costs for simulation comes in …
Building a spreadsheet model for a business case, product profitability projection, etc. often entails estimating some input factors. The estimates are often a most likely value, an estimated average, or median. These are single-point estimates since you’re only using one number (a single data point) for each input. Finally, you get an output number from …
Monte Carlo simulation for business purposes is a great tool to have in your toolbox. If you’re a business owner or a product line owner in a larger enterprise, chances are you’ve created a spreadsheet to model a business case or something similar. Anytime you have a spreadsheet that contains estimates that are inputs to …
Decision making under risk and uncertainty is a fact of life. There are many ways of handling unknowns when making a decision. We will try to enumerate the most common methods used to get information prior to decision making under risk and uncertainty. We’ll also look at decision rules used to make the final choice. For …