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Here’s something that will either comfort or disturb you: the mathematical technique underpinning your retirement strategy was invented by an unwell mathematician playing solitaire in his sick bed.
In 1946, Stanislaw Ulam found himself laid up recovering from encephalitis with nothing to do but shuffle cards. Being the sort of guy who couldn’t boil an egg without pondering the thermodynamics involved, he started wondering about the mathematical probability of winning at solitaire.
He tried working it out properly with equations and formulae, but the calculations became so complicated that he gave it up as a bad job. Then came his stroke of genius: why not just play the game hundreds of times and keep track of how often he won?
This novel “let’s just try it and see” approach caught the attention of John von Neumann, who realized it could solve their impossibly complex nuclear weapons calculations at Los Alamos. They needed a code name, and in a stroke of either genius or profound cheek, they called it “Monte Carlo” after the famous French casino.
That same technique, born from illness, boredom, and a deck of cards, now runs the projections for your retirement portfolio. Monte Carlo simulations test your life savings against thousands of possible futures: market crashes, raging inflation, and the possibility you’ll live to 105 complaining about the weather.
The old approach assumed markets would reliably deliver 7% returns every year, which anyone who’s paid attention to financial history will recognize as spectacularly optimistic. Markets don’t glide smoothly upward like a well-mannered escalator. They lurch about like a drunk on a trampoline.
Those colorful charts your financial advisor shows you, with squiggly lines fanning out like confused spaghetti? That’s Monte Carlo playing out every nightmare scenario so you don’t have to experience them in real life.
So next time you review your retirement plan, spare a thought for Stanislaw Ulam, a man who couldn’t simply play cards like a normal person, and whose boredom-induced revelation became the foundation for modern financial planning. Sometimes the most useful endeavors arise from the most unlikely circumstances and carry wonderfully tongue in cheek names.
Mark, I really enjoyed this. Like many readers, I’ve seen Monte Carlo simulations countless times, but I never knew the fascinating story behind their origin. It’s remarkable that an idea born from a deck of cards and a curious mind has become such an important part of retirement planning. Thanks for making a complex topic both informative and entertaining.
Like others, I have used monte carlo for my retirement savings. You have to make assumptions about different variables, which is the flaw of all these forecasts. Get the assumptions wrong and you get bad and misleading results.
Jerry, classic garbage in, garbage out — a problem as old as coding itself. The tricky part with retirement projections is that the assumptions you use are easily bent to fit our preferred version of reality.
Thanks for a of bit of history, Mark. By nature, I like order and certainty. By contrast, big, changing systems–like Jeff’s weather reference, finance and my garden–don’t. They are on a steady, entropic path toward their own hidden, disorderly destiny. That thought keeps me humble.
Edmund, I’m fairly stoic about it. I’ve long accepted that imposing order on a random universe is a losing battle, so I hold my plans loosely: they navigate me in approximately the right direction, but I’m always mindful of the next curveball that’ll knock me off course.
For those of you who track storms during hurricane season, the day-by-day impact area expands due to the uncertainty factors folded into the Monte Carlo analysis used to track the most likely path.
Jeff, thanks for the knowledge nugget. I never realized Monte Carlo simulation was used in weather forecasting.
As an automotive mechanical designer, we used Monte Carlo simulations every day. They were used in tolerance stack-ups to determine the probability distribution of assembled dimensions based on expected machining variations and to predict how well components would fit together.
Magoo, Monte Carlo is clearly far more deeply embedded in everyday life than I ever appreciated. On a personal note, I had to ask an AI before I fully grasped what you were talking about, but it gave me a lovely little insight into engineering in the process.
Mark, The funny thing is that Monte Carlo in engineering and Monte Carlo in retirement planning are really solving the same problem: nobody knows exactly what the future will be, so we run lots of possible scenarios and see what happens. 😂
Mark, here’s a link to the Monte Carlo calculator, in case anyone is interested;
https://ficalc.app/
Very Cool, Thanks!
Thanks Dan!