Monte Carlo vs “the number”: why your plan is a probability

Last reviewed July 2026 · 5 min read

Retirement math is usually sold as a single figure: “you need 25× your spending,” or “the 4% rule says you’re fine.” A number is comforting — you either have it or you don’t. The problem is that the future it assumes does not exist. Markets do not deliver their average return every year, and two retirees with identical savings and identical average returns can end up in completely different places. A retirement plan is not a number. It is a probability.

Where “the number” comes from

The 4% rule is the classic: withdraw 4% of the portfolio in year one, raise it with inflation, and historically a diversified portfolio survived 30 years. Invert it and you get 25× spending as “the number.” It is a useful rough cut — but it compresses an enormous range of historical outcomes into one threshold, and it says nothing about your mix of accounts, taxes, Social Security timing, or a retirement longer or shorter than 30 years.

What a Monte Carlo simulation actually does

Instead of assuming one future, a Monte Carlo simulation generates thousands of them. Each simulated path draws a different random sequence of market returns and inflation, consistent with the assumptions you chose (average return, volatility, how stocks and bonds move together). Your full plan — withdrawals, taxes, benefit claiming, conversions — is run through every path. Some futures are kind; some are 1970s-style brutal. The output is a distribution: in what share of these futures does the money last? That share is the success rate.

Why the average is not your destiny

The reason a single projection misleads is sequence-of-returns risk. A retiree who hits a bear market in years one through three sells depressed assets to eat, and the portfolio may never recover — even if the following decades are excellent and the 30-year average return matches the plan exactly. Another retiree with the same average but the bad years at the end barely notices. Averages hide order, and in retirement, order is nearly everything. Monte Carlo makes order visible by simulating thousands of different orderings.

How to read a success rate

A 90% success rate means: in 9 out of 10 simulated futures, under these assumptions, the plan never ran out of money. It does not mean there is a 10% chance you will be destitute — real people cut spending when markets fall, while the basic simulation stubbornly spends on schedule. It is also conditional on the assumptions: feed the model returns that are too rosy, or ignore that real markets have fatter tails than a textbook bell curve, and the printed 90% overstates your safety. Treat the success rate as a way to compare plans and to spot fragility, not as a certified probability of your one actual future.

The percentile fan is often more informative than the headline number. The median path shows the boringly typical outcome; the 5th-percentile path shows what a bad draw looks like and when trouble arrives. Two plans with the same success rate can have very different bad tails — one fails gently at 93, another collapses at 78.

From probability to decisions

Thinking in probabilities changes the questions. Instead of “do I have the number?” you ask: how much does success change if I retire one year later, spend $5,000 less, delay Social Security, or convert to Roth in the gap years? A plan that holds up across thousands of futures — and across different market assumptions — is robust. One that only works in the average future is a forecast wearing a plan’s clothing.

Try it in Deorbit Plan

Every run in Deorbit Plan is a Monte Carlo simulation: the success gauge at the top of the dashboard shows the share of simulated paths that never deplete (with the path count behind the estimate), and the net-worth fan chart draws the p5–p95 and p25–p75 bands around the median so you can see the bad tails, not just the middle. Change return and volatility assumptions in the Market Assumptions panel and watch the probability move. Then open the Strategy Lab: the max-spend solver runs a bisection over full Monte Carlo runs to find the highest spending that still meets your target success rate — the “number,” rebuilt as a probability statement.

Educational content only — not financial, tax, or investment advice.

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