Monte Carlo vs. Straight-Line: Why Your Retirement Number Is a Range

The Problem with a Single Number

Almost every retirement calculator you have ever used works the same way. You enter your current savings, your expected return (say, 7%), your savings rate, and your retirement age. The calculator multiplies and compounds and produces a number: "You will have $1,847,000 at age 65."

This is a straight-line projection. It assumes that every year, your portfolio grows by exactly 7%. No bad years. No good years. Just a smooth, consistent 7% forever.

The problem is that markets do not work this way. In reality, your portfolio might return 22% one year, -18% the next, 14% the year after, and -6% the year after that. The average of those four years might be close to 7%, but the path matters enormously — especially in the years just before and just after retirement, when your portfolio is at its largest and most vulnerable to sequence-of-returns risk.

A straight-line projection tells you the median outcome if everything goes according to plan. It tells you nothing about the range of outcomes you might actually experience.

What Monte Carlo Actually Does

Monte Carlo simulation is a method for modeling uncertainty by running thousands of randomized scenarios and observing the distribution of outcomes.

In the context of retirement planning, a Monte Carlo model generates thousands of hypothetical return sequences — each one a plausible path that markets could take based on historical volatility and return distributions. Some sequences are lucky: strong returns early, modest returns late. Some are unlucky: poor returns in the first decade of retirement, when the portfolio is largest and withdrawals are beginning. Most fall somewhere in between.

For each simulated path, the model asks: did this portfolio survive? Did it last 30 years (or 40, or 50) without running out of money?

The result is not a single number but a probability distribution. A well-built Monte Carlo model might tell you: "Under these assumptions, your portfolio survives in 87% of simulations." Or, presented visually, it shows a fan chart — a cone of possible futures — where the width of the cone represents the uncertainty in your outcome.

How to Read a Probability Fan Chart

The fan chart — also called a cone of uncertainty — is the standard visualization for Monte Carlo retirement projections. Here is how to interpret it.

The center line (P50) is the median outcome: half of all simulated scenarios ended above this line, half below. This is roughly equivalent to what a straight-line projection shows, assuming the straight-line return matches the median simulated return.

The upper band (P90) shows the 90th percentile outcome — the result in the top 10% of scenarios. This is not the best possible outcome; it is a realistic optimistic outcome. If markets cooperate and you get favorable return sequencing, you might end up here.

The lower band (P10) shows the 10th percentile outcome — the result in the bottom 10% of scenarios. This is not a catastrophic outlier; it is a realistic pessimistic outcome. Poor early returns, a bad decade, inflation running higher than expected — this is where you land.

The width of the fan tells you something important: how sensitive your outcome is to luck. A narrow fan means your plan is relatively robust to market variation. A wide fan means your outcome depends heavily on the sequence of returns you happen to experience — and that you should consider building more resilience into your plan.

What This Means for Your Planning

The practical implication of Monte Carlo thinking is that retirement planning should be about building resilience, not optimizing for the median.

A plan that succeeds in 95% of simulations is meaningfully different from a plan that succeeds in 72% of simulations — even if both have the same median outcome. The difference is the margin of safety.

How do you improve your probability of success? The most powerful levers are: a lower withdrawal rate (even dropping from 4% to 3.5% dramatically improves outcomes), a flexible spending strategy (willingness to cut spending in bad years), a larger equity allocation (higher expected returns, with higher volatility), and some capacity for supplemental income in early retirement.

Worthune's FIRE scenario runs a 500-simulation Monte Carlo model and shows you the P10, P50, and P90 bands for your specific inputs. The goal is not to make you anxious about uncertainty — it is to make you honest about it, so you can build a plan that holds up under realistic conditions rather than ideal ones.