Cross-posted from Linkedin
One of the things I enjoy doing in retirement is picking up a problem I don’t fully understand and figuring it out.
Last year that meant learning enough JavaScript to build a Monte Carlo simulation toolkit from scratch. Monte Carlo methods let you model outcomes when the inputs are uncertain — think probability distributions rather than fixed numbers.
Practical uses I find interesting:
• Will a retirement portfolio survive 30 years of withdrawals and market variance? (I built one for my own portfolio in 2021 — it gave me enough confidence to actually retire.)
• How likely is a complex project to slip its schedule, and by how much?
• What does an election result distribution look like across thousands of modeled scenarios? (Nate Silver’s work is a good public example of this.)
The toolkit I built makes it straightforward to configure variables, run thousands of iterations, and visualize the output distribution — no math degree required to use it.
I’m not a software engineer by background. I’ve spent my career in networking, sales, and operations. But I’ve always believed that the most useful skill in any role is the willingness to learn whatever the problem requires.
If you’re curious about the toolkit, have a problem that might benefit from this kind of modeling, or just want to talk through whether simulation would even apply to your situation — I’d enjoy the conversation. Feel free to reach out.
