Indeed, implementing risk-based guardrails, as recently discussed by Derek Tharp on Kitces.com, could be extremely useful (see: https://www.kitces.com/blog/guyton-klinger-guardrails-retirement-income-rules-risk-based). Risk-based guardrails use the probability of success metric to adjust spending, while solving most of the shortcomings of classical guardrail methods (e.g. Guyton-Klinger). It’s considered to be more accurate and adaptable to real-world scenarios and is highly recommended to mitigate sequence risk, as retirees would experience smaller income reductions compared to Guyton-Klinger during historical market downturns.

Interesting. It seems that more and more CFPs claim that risk-based guardrail is the preferable withdrawal strategy for retirement planning, especially for early retirees. Take a look at: 1. https://www.youtube.com/watch?v=G4D-niuPYU4. 2. https://www.youtube.com/watch?v=syzZqrmrsy4. 3. https://www.morningstar.com/retirement/how-retirement-income-guardrails-can-ease-clients-worries

Would definitely love to see this feature added. As noted above, this does seem to be the direction in which a lot of modern retirement advice is heading. I am currently in the process of deciding which software to go with long term, and if this became a reality, it would be an absolute no-brainer for me.

Great suggestion. A must-have.

The strategy is dynamic and flexible and unlike traditional models that assume constant rates of return or fixed withdrawal percentages, it adjusts to real market conditions.

The method adjusts withdrawal rates in response to portfolio performance, using “guardrails” to prevent withdrawals from becoming too aggressive or too conservative. It achieves this by adjusting the withdrawal rate to maintain a desired probability of success, so if the portfolio is underperforming, it lowers the withdrawal rate to keep the probability high (of course, it uses Monte Carlo simulations to assess the probability of success).

Another huge advantage is the option to model variable expenses by adding in specific future costs or adjusting the withdrawal to allow for one-time or periodic increases in spending.

As far as I know, it is used as the main strategy in IncomeLab.

Read: https://www.kitces.com/blog/risk-based-monte-carlo-probability-of-success-guardrails-retirement-distribution-hatchet/

Currently, this risk based guardrails strategy seems to be exclusive to retirement planning tools like IncomeLab or Timelinie, which are expensiv and basically only available through professional advisors. if ProjectionLab could include it, this would mean a very unique selling point vs. all other personal tools.

This video shows the functionality on Income Lab pretty in-depth. Could be an orientation for a possible ProjectionLab feature. https://www.youtube.com/watch?v=NtGZ9DznM3o

This is extremely computationally expensive since it involves doing Monte Carlo simulations within Monte Carlo simulations. Based on how long it takes PL to do the 192 normal MC simulations, the wait time would make it unusable. It would be similar to repeatedly doing the current Monte Carlo feature about 100-1000 times in a row. PL would have to substantially upgrade its back-end hardware to do this feature.

Dynamic guardrails and dynamic spending allowances and guidance would put PL way ahead of competitors. Boldin is supposedly working on this. This feature takes this from a modeling tool to an implementation tool.

IncomeLab does not have a Monte Carlo tool. It only displays the results of the guardrails for one historical “run” at a time.

It seems like this is almost asking for a complete redo of PL. Guardrails tells you how much you can spend. PL is currently based on specifying what you want to spend.

I believe that statement may be outdated or incorrect.

According to IncomeLab’s own May 2024 release notes, the platform supports a mix of Historical, Traditional Monte Carlo, and Regime-Based Monte Carlo simulations when testing plans and implementing guardrails.

The notes explicitly state that you can apply these analysis methods—individually or in combination—to test both spending capacity and guardrail performance. This makes clear that IncomeLab is not limited to a single historical run; Monte Carlo simulations are actively integrated into the guardrail engine.

You can watch their recent video on this topic: https://www.youtube.com/watch?v=Wqr1E6HP8UY (“How Accurate Are Monte Carlo Forecasts in Retirement Income Planning?”)

As far as I understand, implementing risk-based guardrails doesn’t require nested Monte Carlo simulations. The typical process involves running a single set of Monte Carlo simulations, then analyzing that output to:

1. Monitor the probability of success (PoS) over time for a given withdrawal level, using the simulated portfolio paths;

2. Trigger guardrail actions when PoS crosses a predefined threshold (e.g., dropping from 90% to 75%);

3. Adjust the withdrawal amount to restore the target PoS—by iteratively testing new spending levels against the existing simulation results, without re-running the simulation.

To my knowledge, there’s no need to re-run full simulations 100–1,000 times. Withdrawal adjustments are made by navigating the results of the initial simulation set, using those outcomes to estimate how changes in spending would impact the probability of success—all without performing new Monte Carlo runs.

It depends on what you want PL to show. Tools for financial advisors are designed to “sell” a financial plan. They want to show results that would make a potential client want to hire the financial advisor after a concise presentation. This is what IncomeLab does. The financial planner shows: “This is what your plan looks like in the Great Depression. This is what it looks like during 1960-70s stagflation. This is what it looks like during the dot-com crash.” And then it shows a timeline for each case with the income level going up and down, or a separate graph that shows the portfolio value progressing between the guardrails.

When making the graphs, there’s an initial binary search to find the spending level that achieves an 80% MC probability of success. And then to make the graph of the guardrails at each spending step, it needs to do a binary search to find the portfolio values that achieve 99% and 25% MC probability of success with that spending level. It is only computationally expensive if you want to make those graphs, mainly calculating the exact guardrail amounts at each spending step (monthly in IncomeLab).

You can do guardrails manually right now. Just run MC and if you are 99%+ you have hit an upper guardrail and should increase spending. If you are at 25% or less you have hit a lower guardrail and should reduce spending. Also, while it is correct that IncomeLab uses MC for its guardrails strategy, it does not have a MC tool like PL does. You can’t do a MC analysis of your plan and get plots and stats of percentiles of Net Worth, Total Expenses, and Withdrawal Rates like you can in PL. Its output is what I described above to “make the sale” for the advisor. The typical presentation with a client shows the graphs of how it handles the bad years and shows maybe one or two graphs of good sequences such as starting in 1948 or 1982, followed by showing the savings from the Roth conversion tool and maybe the benefits of the Social Security claiming strategies.

Seeing a different tool with a shiny feature that looks cool creates a desire to have that feature, but that doesn’t necessarily make it a good fit for PL. It would certainly be great if PL could add some sort of dynamic spending feature. I’m not sure that risk-based guardrails are the best way to do it. What makes PL special is its flexibility. A more generalized method of doing dynamic spending would be better than just being a clone of IncomeLab.

Richard, thanks for your thoughtful response. You’re right, it’s key to make sure dynamic spending features fit with ProjectionLab’s unique strengths. Here are my thoughts:

You’ve hit on a key challenge: complexity. While core guardrail logic doesn’t need complex, “nested” Monte Carlo simulations, building detailed, dynamic visuals for fine-tuned spending would be tough for PL. Giving users real-time, interactive feedback (while keeping things smooth) takes a ton of work.

Good point about IncomeLab. It has a different focus, using Monte Carlo simulations behind the scenes to guide spending. It doesn’t offer PL’s deep analysis. Instead, it shows straightforward, real-world examples for advisors and clients. Still, it uses advanced Monte Carlo methods, both traditional and regime-based, to set its guardrail spending.

I see things a bit differently here. I agree PL shouldn’t just copy other tools, but I don’t think generalized dynamic methods are as effective as probabilistic guardrails, especially for tackling Sequence of Returns Risk (SORR). Here’s my take on how dynamic spending strategies stack up (static withdrawal rules, like the 4% rule, aren’t on this list because they only adjust for inflation, not market performance).

• Fixed Percentage of Current Portfolio Withdrawals – Ties your income directly to a fixed percentage of your current portfolio value. It’s simple, but income is highly unstable: a 20% market drop means a 20% income drop, directly impacting your lifestyle.
• Inflation-adjusted + Freeze Rules – Let your income adjust by freezing inflation increases or cutting spending if markets are bad. They’re better than static plans. But honestly, their rules (like when to freeze or cut) often feel arbitrary, making them less precise and optimized than smart, probabilistic methods.
• Basic Decision Rules (e.g., Guyton-Klinger) – Dynamic, adjusting via clear, pre-set triggers (like, “cut spending if your portfolio drops 20%”). That flexibility helps, making them better than just fixed withdrawals. But a big problem: huge, sudden spending swings when markets go south. Those fixed triggers miss the bigger picture, though. A 20% drop hits differently depending on whether it’s early or late in retirement, or if the market bounces back fast. They just react to a number, not your plan’s overall health or its actual probability of success. This often leads to “off” adjustments – too harsh on your lifestyle or not effective enough to last.
• CAPE Ratio-Based Adjustments – They’re all about how expensive or cheap the market is (that’s the Cyclically Adjusted Price-to-Earnings (CAPE) ratio), which helps you decide how much to pull out. So, spending goes up or down based on whether the market’s historically high or low. They’re pretty cool because they look ahead and can help you avoid starting retirement right when markets are super pricey. But here’s the thing. While CAPE is awesome for guessing long-term returns, its signals can be pretty fuzzy for exact, year-to-year spending tweaks. Relying solely on it for quick, tactical withdrawal calls can be tough, since it won’t tell you exactly when a pricey market will crash or how far.
• Actuarial/RMDs-Based Withdrawals – Dynamic strategies that figure out your annual withdrawals based on your remaining life expectancy and current portfolio value. You basically can’t run out of money with these, as you’re always just taking a percentage of what’s left. They tackle sequence risk by naturally reducing withdrawals in downturns, but your income can really jump around with the market, just like taking a fixed percentage.
• Advanced Rules / Optimized Guardrails – Think of these as super smart rule-based systems. Their triggers and how much they adjust aren’t just random guesses. They’re fine-tuned using tons of backtesting and Monte Carlo simulations. They offer a way more sophisticated kind of dynamic adjustment, often looking at trickier conditions than just a basic portfolio drop. Honestly, the line between these and modern risk-based guardrails can get a bit blurry, since both are really shooting for the best possible results.

When it comes to dynamic methods, risk-based guardrails (and other smart, probabilistic strategies) are really gaining ground. That’s because they’re way better at tackling Sequence of Returns Risk (SORR)—you know, when bad early market returns trash your portfolio. Here’s how they do it:

• Probabilistic Precision: Instead of just freaking out over portfolio drops, they constantly watch your Monte Carlo Probability of Success (PoS). That gives you a super precise, financially smart signal. If your PoS dips below a set level (say, 90% to 75%), spending tweaks just enough to get things back on track. No pointless cuts, and your money’s protected when things are dicey.
• Adaptive Optimization: They’re super flexible with real-time markets, aiming to get you the most spending over your whole retirement. This often means you can start taking out more, and end up better off in the long run, because their adjustments are smart moves, not just random cuts.

These strategies are gaining traction because they directly address SORR—the biggest threat to retirement sustainability. By linking spending to PoS, they’re smarter, more adaptive than simpler triggers.

Good news is, PL already has the bones for flexible dynamic spending. We could easily stretch that to let you set PoS targets or tie spending directly to Monte Carlo results. That means custom guardrails, so no one’s stuck with a ‘one-size-fits-all’ model.

Bottom line: it’s not about turning PL into IncomeLab, but folding the best probabilistic guardrail stuff into PL’s unique, customizable setup. That’ll keep PL leading the pack in both deep analysis and practical planning.

Appreciate the discussion!

While it would be very interesting to get into a deep discussion on the pro and cons of various withdrawal strategies, I think it would be worthwhile to focus on what this suggestion is actually trying to get done.

The original poster suggested automatically calculating a spending amount that matched a desired MC PoS. In addition, it would automatically calculate the portfolio guardrails corresponding to the upper/lower MC PoS with that spending amount. This is essentially the “Retirement GPS” feature in IncomeLab.

As a DIYer, I see that I can already do the substance of this in PL. I can tweak my spending until the Chance of Success tool gives me the PoS that I am looking for. This is my initial spending amount. As time goes by, I can run the Chance of Success tool with my plan, and if the PoS is too high or too low for my chosen guardrails, I can make an adjustment to spending to get the PoS back inside my guardrails. The one thing this doesn’t do is tell you ahead of time what the guardrail portfolio values are, but those are not fixed and decrease over time as the plan length gets shorter, so I’m not sure how much value there is in knowing the exact values for right now.

This feature is most useful for advisors working with clients since it gives a better way to discuss a plan. Target spending amount and guardrails are a better client conversation than PoS for this and PoS for that. However, I don’t know how much PL is targeting advisor-focused features.

The next question is what else would this be used for? Would risk-based guardrails be usable in the Chance of Success tool? The PoS would always be 100% due to the guardrails, but the plots and stats could provide some useful information.

Is there another use case that I am missing?

​ You nailed the ‘Retirement GPS’ idea 😉

So, about trying to do these dynamic adjustments by hand in PL… yeah, you could crunch the numbers yourself, but that totally misses the point of automation. It’s like trying to get across the country with just paper maps instead of a GPS: super tedious, only reactive, and you don’t get that live guidance. This feature is all about making things automated, simple, and proactive.

In your manual setup, you’re picking guardrail rules (like a specific PoS threshold). Since your portfolio’s value changes with the market, manually tracking those triggers would require ongoing effort. This feature automates that, giving you real-time dollar values and making it way easier to take action quickly without guessing, especially when markets shift.

Beyond just tracking simple triggers, this feature also automates the complex periodic recalculations inherent in risk-based guardrails based on PoS and market conditions. You won’t typically need to rerun simulations yourself to see if your guardrails are still on track, because your plan updates in real-time. This makes it easier to adjust spending or investments on the fly without needing to keep redoing the math yourself.

These benefits are just as huge for DIYers. It’s all about giving us those pro-level tools to manage our own finances way more clearly.

Risk-based guardrails are designed to work with the Chance of Success tool, providing great insights.

You’re right that guardrails are designed to keep a plan from running out of money, but their relationship with PoS is more nuanced than it always being 100%. PoS represents the likelihood of success based on the initial spending plan across thousands of simulations, reflecting how well it holds up under various market scenarios without adjustments. Even with guardrails in place, PoS won’t automatically be 100% because they only kick in when things start to go off track. For example, if your plan starts with an 80% PoS and the market drops, triggering a spending cut, PoS could improve—but it won’t jump to 100% just because the guardrails are activated. Guardrails don’t directly affect the initial PoS, they’re more like a reactive safety net.

Beyond merely checking if your plan runs out, it’s a dynamic storytelling tool showing exactly how your high (but not always 100%) PoS holds up across thousands of simulations. It breaks it down by looking at:

• PoS Trigger Mechanics: How often PoS adjustments (like cutting spending to move from 50% PoS back to 80%) actually happen and their impact.
• Spending Trajectories and Volatility: See your actual income paths across different scenarios, showing how much spending fluctuates.
• Adjustment Frequency and Magnitude: How often guardrails kick in, and the typical dollar amounts/percentages of cuts or raises.
• Portfolio Resilience: How guardrails protect your portfolio from running out, showing how it stays viable.

This feature unlocks some cool uses:

• From Modeling to Ongoing Guidance: Turns PL from a “what-if” tool into a proactive system that shows you how to manage your money dynamically, not just if your plan works.
• Optimizing Lifetime Spending: Helps you maximize the sustainable amount you can spend throughout your retirement.
• Adaptive Decision-Making for Life Events: Enables smarter, real-time decisions during big life changes, by showing how your plan responds.
• Scenario Comparison & Stress-Testing: Lets you compare different guardrail strategies or stress-test your plan against tough times like 2008.
• Reduced Emotional Stress: Provides major stress reduction with clear, data-driven actions during market downturns.

Thanks for the discussion – it really helps us nail things down!

You guys are putting in what’s obviously thoughtful opinions and ideas. TBH, most of it is over my head. But I am in need of some kind of withdrawal strategy and I don’t know what to do.