PWR v Kolmogorov v MC simulator

This is a follow up to a past post where I put up a Monte Carlo simulator against a partial differential equation to see how they stacked up in "fail rate probability" over a spend rate range I probably care about (2% to 5%) with an age that makes sense to me (60) and with a return profile that stands in as a proxy for a 60/40 real return after taxes and fees (4% r, 10% std dev).  Now here I am adding the PWR ("perfect withdrawal rate," see previous several posts) into the fray. The PWR here uses the same return profile as above but I've added to PWR the same stochastic longevity distribution assumption as the Kolmogorov PDE (87.25 mode, 9.5 dispersion; in other words I let the periods over which the PWR was calculated in 10000 iterations vary with the shape of a longevity distribution). A difference here is that I am reluctant to use the phrase "fail rate probability" for PWR.  Rather, I am saying that in the distribution of PWRs that comes out of calculating it 10000 times, on the CDF that results, x% of PWRs were higher than the one at the spend rate in question so instead of a 5% fail rate, I'd be saying 5% of PWRs were lower than the one in question. The reader can interpret the meaning. I'm just charting it:

Conclusion: no hard conclusions, just checking out what it looks like. I'm not surprised that all three are relatively close since they are all more or less playing the same game underneath the covers, There are minor differences but: a) they are small enough that it probably is not worth interpreting, b) small changes in assumptions would probably drive a major divergence, and c) it would probably be worth my while to see what happens past .05 spend rate.