The issue with deterministic planning
Many people start retirement planning with a FIRE calculator, a spreadsheet model, or a robo-advisor projection. You enter your savings, spending, and an expected return, and it gives you one clean answer.
But retirement risk is driven less by the long-term average and more by what happens when markets perform poorly early, while you’re withdrawing.
What deterministic projections assume
A deterministic projection is a single path that ignores how outcomes change when returns arrive in a different order.
What actually happens in markets
Monte Carlo-style simulation shows a range of outcomes because returns arrive in different orders.
The problem with averages
A single-path projection implicitly assumes returns arrive smoothly. Real returns are lumpy. The order matters.
Two retirees can have the same long-term average return and end up with opposite outcomes depending on whether the first decade is good or bad.
Why “conservative” returns don’t fix it
Lowering the return assumption doesn’t add risk modeling. It just moves the same single line up or down.
You still can’t answer the questions people actually care about:
- How often does this plan run out of money?
- How sensitive is the plan to a bad first decade?
- Which changes reduce the probability of failure?
What Monte Carlo adds
Monte Carlo simulation runs many plausible market paths instead of one, using projections of market returns and volatility (from institutions like JP Morgan or historical averages), varying the magnitude and order returns arrive.
The output isn’t a single projected ending balance. It’s a distribution of outcomes, including paths where the plan fails.
This approach isn’t experimental. It has been the industry standard in professional financial planning for decades because it makes uncertainty explicit instead of hiding it behind an average.
Why this is the industry standard
Monte Carlo simulation isn’t controversial inside the financial-planning profession. It’s the default way modern advisor software evaluates retirement sustainability, because it explicitly models uncertainty and sequence-of-returns risk.
Major planning platforms used by RIAs and broker-dealers describe retirement outcomes in terms of probability of success, not a single projected balance:
- RightCapital documents that its retirement Probability of Success metric is computed by running a Monte Carlo simulation and measuring how often the plan avoids depletion (official documentation).
- eMoney Advisor describes Monte Carlo simulation as a core part of its retirement analysis, including longevity risk analysis and confidence-age metrics (product announcement).
- MoneyGuidePro sample plan reports reference “Monte Carlo trials” and present results as a Probability of Success or Confidence Zone rather than a single forecast (sample plan report).
- Industry commentary from the CFA Institute notes that probability-of-success metrics derived from Monte Carlo simulations are the most common output in financial-planning tools (CFA Institute blog).
The point isn’t that Monte Carlo produces certainty. It’s that it replaces a misleading single-path projection with a model that shows how often a plan actually fails under plausible market conditions.
Bottom line
Deterministic models are convenient, but they answer a simplified question that doesn’t match how retirement risk actually works.
If you care about whether a plan survives bad markets — not just what happens on an average path — you should look at probabilistic models like Monte Carlo.
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