_How to Measure Anything_ by Douglas Hubbard includes a chapter on Monte Carlo simulations and comes with downloadable Excel examples: https://www.howtomeasureanything.com/3rd-edition/ (scroll down to Ch. 6)
The main example is, you're considering leasing new equipment that might save you money. What's the risk that it will actually cost more, considering various ranges of potential numbers (and distributions)?
I think it's harder to apply to software since there are more unknowns (or the unknowns are fatter-tailed) but I still liked the book just for the philosophical framing at the beginning: you want to the measure things because they help you make decisions; you don't need perfect measurements since reducing the range of uncertainty is often enough to make the decision.
> I think it's harder to apply to software since there are more unknowns (or the unknowns are fatter-tailed)
Talking purely in agile software development, there is an idea of "Flow Metrics" [1] to use for estimation, basically boiling it down to "How many stories can we finish in the next timeframe (Sprint or whatever), and what is the uncertainty associated with this number?"
I really like the idea, but haven't been able yet to test it.
Great suggestion. I'm having solar panels installed on my house partially because I ran thousands of Monte Carlo simulations on a range of variables and almost all of them pointed to a great NPV.
The main example is, you're considering leasing new equipment that might save you money. What's the risk that it will actually cost more, considering various ranges of potential numbers (and distributions)?
I think it's harder to apply to software since there are more unknowns (or the unknowns are fatter-tailed) but I still liked the book just for the philosophical framing at the beginning: you want to the measure things because they help you make decisions; you don't need perfect measurements since reducing the range of uncertainty is often enough to make the decision.