TL;DR. A spending-plan rule decides what your base annual spending is in any given year of the simulation. The seven rules trade off lifestyle stability against portfolio responsiveness: stable rules keep your standard of living smooth but can sprint into a depleted portfolio in bad sequences; responsive rules absorb market shocks by cutting spending, which fattens the failure tail at the cost of more year-to-year variation.

Rule comparison

Risk-Based Guardrails (chance-of-success targeting)

Most rules above watch a proxy for risk: your withdrawal rate, the CAPE ratio, your portfolio vs. its starting value. Risk-Based Guardrails watches the thing you actually care about: the chance of success itself.

What "chance of success" means here. Each year the engine runs a fan-out of forward scenarios from your current portfolio and counts the share in which your plan would have lasted your full plan length. That share (a frequency over history, not a crystal-ball probability) is your chance of success. "80%" means your spending is set so the plan survived in about 80% of those scenarios.

How the guardrails move spending (raise promptly, cut reluctantly).

• Raise: when success climbs to your raise threshold (default 100%, i.e. it would have survived everything), you're under-spending, so spending steps up toward the target.
• Cut: you only tighten up when success falls to your cut threshold (default 25%). The cut then lowers spending just until success climbs back to your recovery level (default 45%), set between the cut threshold and the target. Because more safety always means less spending, a higher recovery means a deeper cut (lower spending, more safety) and a lower recovery means a gentler cut (more spending, but nearer the edge). It deliberately stops short of the target, so a cut trims reluctantly instead of slashing all the way back.
• Hold: in between, spending just keeps pace with inflation.

The horizon shrinks as you age (a 4% draw is safer with 12 years left than 30), which is what lets the guardrails ratchet spending up over a successful retirement. Horizon comes from your Simulation Duration in Time Settings. There's no separate longevity input.

How it stays fast. Sizing spending to a target success rate could mean running hundreds of sub-simulations inside every year of every scenario, a simulation within a simulation. Instead, the engine builds a single chance-of-success model once: it relates how well-funded you are (your assets vs. the present value of your remaining spending, net of income like Social Security) and your remaining horizon to the chance of success, then looks each year up against that model across the full sweep of historical cycles. That makes a Risk-Based Guardrails run a quick background analysis with a progress bar. Because the model is built from your starting portfolio, it's a close estimate rather than an exhaustive re-simulation: accurate for typical plans, with a little more give when your account mix shifts a lot late in retirement.

What the progress bar is doing. Before it can count scenarios, the run has two setup steps, and the panel names each one: Preparing market scenarios while it builds the paths, then Calibrating your guardrails while it builds the chance-of-success model. On a historical backtest these pass quickly. Combined with Monte Carlo returns they are most of the run — the guardrails are re-solved for every iteration — so the counter can take a few minutes to start moving. That is why 1,000 iterations is usually the right choice for this plan: higher counts mostly add setup time.

Cached results. A completed Risk-Based Guardrails analysis is saved and shown again instantly when you come back; it only re-runs when something that feeds the simulation changes (accounts, spending, people, glide path, custom market series, and so on). If you edit your inputs while looking at an older result, the year-by-year deep-dive will ask you to re-run the analysis so the details always match your current plan.

Spending Floor and Spending Ceiling

The optional Spending Floor and Spending Ceiling are guardrails that clamp whatever the spending rule produces in a given year. They are entered in today's dollars; each year the engine multiplies them by cumulative inflation before applying them, so a $60,000 floor keeps the same real purchasing power across the whole simulation.

The mechanic is simple. Every rule above computes a candidate spending value for the year, and then the engine clamps it:

• If the candidate is below the floor (after inflation), use the floor.
• If the candidate is above the ceiling (after inflation), use the ceiling.
• Otherwise, use the candidate as-is.

Clamping happens on every plan, including CPI and Non-Inflation Adjusted, but it only matters when the rule produces values that drift off the starting amount. That mostly happens with the variable rules.

Why they matter for variable plans

Variable rules (Percent of Portfolio, VPW, CAPE, Guyton-Klinger, Z-Value) tie spending to the portfolio. That responsiveness is the point. It's also what makes those rules produce spending numbers you would not actually live with. A Percent-of-Portfolio plan at 4% on a $1M portfolio says "spend $40k"; if markets drop 50% the next year, the same rule says "spend $20k". A floor at $30k stops the cut at $30k. A bull run that doubles the portfolio suggests "spend $80k"; a ceiling at $55k stops the climb there.