Cryonics Cost-Benefit Analysis

3rd Aug 2020

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Does this analysis take into account the fact that young people are most likely to die in ways that are unlikely to result in successful cryopreservation? If not, I'm wondering what the numbers look like if you re-run the simulation after taking this into account. As a young person myself, if I die in the next decade I think it is most likely to be from injury or suicide (neither of which seems likely to lead to successful cryopreservation), and this is one of the main reasons I have been cryocrastinating. See also this discussion.

Hello again, I put your consideration into this section. Basically, if you trust yourself completely & are younger than 26 years, wait until you're 26 (but the benefit is tiny), otherwise, it's still optimal to sign up now.

Thanks! I think I would have guessed that the optimal signup is around age 35-55 so this motivates me to dig closer into your model to see if I disagree with some parameter or modeling assumption (alternatively, I would be able to fix some mistaken intuition that I have). I've made a note to myself to come back to this when I have more free time.

After improving the cost-benefit analysis in multiple small ways (e.g. adding more fleshed out x-risk concerns), it is actually optimal to sign up at age 50 *if one has complete self-trust*. If one doesn't have complete self-trust, signing up immediately is still optimal.

I spent time working in fatal car crash investigation (reading crash reports and doing engineering analysis, nothing as gory as you're probably picturing), and car crashes often involved massive head trauma or would, at a minimum, require *hours* of lag time before the cryonics team could make it there. I'd say at a complete guess that only about 10% involved people dying in hospital later on (i.e. under circumstances that a cryo team could get to them in time to prepare the body).

My impression of the technology is that it's too much in its infancy to be able to say with any sort of confidence that a body that had been left with minimal treatment for a good 8-10 hours would be in a good state for preservation. And my understanding is that after only a few minutes/hours the brain starts to really degrade.

This is a major reason I'm not considering yet. I also live in a country without a good cryo organisation, and the exchange rates make the fees for Alcor quite a lot when I am not convinced I'd get the value. I also think the 5% figure is way too high.

I mean admittedly, pascal's wager comes into play a bit here, but I'm not convinced that my current jurisdiction is a good place to die and be cryopreserved, and I have no plans to move.

I have been putting this off because my medical knowledge is severely lacking, and I would have to estimate how the leading factors of death influence the possibility to get crypreserved mainly by subjectively evaluating them. That said, I'll look up some numbers, update the post and notify you about it (other people have been requesting this as well).

cross-posted from niplav.site## Cryonics Cost-Benefit Analysis

— Patricia Taxxon, “Deconstruct” from “Foley Artist”, 2019— Jonathan Coulton, “Want You Gone” from “Portal 2: Songs to Test By (Volume 3)”, 2011Many would-be cryonicists cryocrastinate, i.e. they put off signing up for cryonics until a later point in their life. This has often been explained by the fact that signing up for cryonics requires high conscientiousness and can be easily be delayed until another point in life.

— person who was crematedHowever, it hasn't yet been explored whether this procrastination might be rational if eventually followed through with: Many cryonics organisations have high membership fees, which might be avoided by waiting.

To find this out, I first present a point-estimate model of whether (and if yes, when) to sign up for cryonics. The model is written in Lua. I then proceed and create a Guesstimate model to determine the distribution of the expected value of signing up.

## Other Cryonics Texts

This write-up is not intended as an introduction to the concept of cryonics. For a popular introduction to the topic that clarifies many common misconceptions about the practice, see Urban 2016.

For more basic information about the topic, there are the Cryonics FAQ by Ben Best, a former director of the Cryonics Institute, as well as Alcor's Cryonics FAQ, which should answer most questions people usually have about cryonics.

If you have decided that you indeed do want to sign up for cryonics, the best existing resource is the Cryonics Signup Guide.

## Caveats

This cost-benefit calculation depends on various factors that carry significant uncertainty with them, and necessarily contains numerous simplifications and inadequacies. The calculated values ought to be equipped with gigantic error bars. I encourage others to make their own calculations and models, compare results and bet on the relevant probabilities.

Specifically, this analysis attempts to be relatively conservative, think of the lower range of a 50% confidence interval. For example, this leads to excluding singularity scenarios with lifespans of billions or trillions of years at enormous quality. This is a balancing act, some people might criticize that the number of worlds with indefinite life extension and cryonics revival but without cosmic endowment is very small. These concerns might be correct, but bare resemblance to Pascal's mugging-like scenarios. In order to avoid such concerns, I focus on

relativelyunspectacular visions of the future. If cosmic-endowment scenarios are more likely than I suspect, this only strengthens the case for signing up.## Cost-Benefit Calculation for Cryonics

— Robin Hanson, “Break Cryonics Down”, 2009To find out whether to sign up for cryonics at all, one needs to make a cost-benefit calculation. This has been attempted before, but that analysis has been rather short (disregarding several important factors) and it might be productive to approach the topic independently.

The costs of cryonics are comparatively easy to calculate and contain little uncertainty: The price of cryopreservation and life-insurance are widely known, and can be easily added together. The benefits of cryopreservation, however, contain a lot more uncertainty: It is not at all clear that the technology for resuscitation will be developed, cryonics organizations and humanity survive to develop such technology, or that the future will be interested in resuscitating people from cryopreservation.

The model presented makes the assumption that a person has a given age and has the option of waiting for signing up for cryonics every year up to their expected year of death. So, for example, a person that is 20 years old now is able to plan signing up when they are 20 years old, 21 years, 22 years and so on up to 78 years. The value of cryonics is calculated, and the value of a regular death is tacitly assumed to be $0.

`curage`

contains the current age of the user of the program.`actval`

is an actuarial table that contains at the nth position the average life expectancy of a person that is n years old at the moment for a western nation (in this case Germany).## The Disvalue of Waiting

Two important factors play into the value (or disvalue) of waiting to sign up for cryonics: Motivation drift and the possibility of dying before signing up.

## Motivation Drift

`prob_signup`

is a function that calculates the probability of signing up for cryonics after having waited up to having a certain age. It seems clear that people loose motivation to finish plans over time, especially if they are unpleasant or complex.A good example for this is people being motivated at the start of the year to do regular exercise: How many of those actually keep their promises to themselves? They might start off exercising, but after the first few weeks the first people drop out, and and a couple of months there is nearly nobody left still going to the gym except the ones who already did it before. It seems like there is a strong regression to the mean in regards to action: Most regular actions are replaced by inaction, most strong values are replaced by apathy over time. A similar phenomenon seems likely for signing up for cryonics: At first, people are very enthusiastic about signing up, but then loose interest as time progresses.

It isn't obvious to me how strong motivation drift is and how it develops over time (some people might regain motivation after some time), but intuitively it seems like a geometric distribution. The reasoning is as follows: Imagine that a thousand people have the motivation to perform a given action n years into the future. Every year, a certain percentage p of the people who were still motivated loses interest in performing that action and drop out. After n years, the number of people who perform the action is 1000∗pn (the percentage of people still motivated is pn). This also works if some people get motivated again to do something after a number of years, as long as their decisions are independently and identically distributed.

When trying to find out what the value of p is for oneself, one can imagine a thousand independent identical copies of oneself planning on executing a complex plan one year ahead. How many of those would actually follow through on that plan? Intuitively, I'd say that it can't be much higher than 95%, possibly much lower, especially for something as complex and time-consuming as signing up for cryonics.

Interestingly, this does not mean that the decision of whether to be cryogenically preserved or not is then set in stone as soon as possible:

Cryonics memberships are very easy to cancel, very often a simple email and a cessation of paying membership fees suffices. Signing up for cryonics earlier protects against regression to the mean, which means apathy or lack of motivation towards cryonics, but does not protect against changing ones mind about cryonics: If one becomes convinced it's bullshit later, one can easily get out (much more easily than getting in). On the other hand, there might be a feeling of considerable sunk cost due to already paid membership fees and the acquired life insurance.In this analysis, it will be assumed that once one is signed up for cryonics, one stays signed up for it.

## Dying Before Signing Up

If you die before signing up, all possible value (or disvalue) of cryonics gets lost. So we want to calculate the probability of dying before having a certain age given being currently

`curage`

years old.Mortality rates are usually calculated using a Gompertz distribution. I determined the b and eta values by eyeballing Wolfram Alpha and using a calculator in Tomasik 2016.

`gompertz`

returns the probability of reaching`age`

starting from birth, but I need the probability of reaching`age`

given one is already`curage`

years old. With Bayes theorem one can calculate thatPr[X≥age|X≥curage]=Pr[X≥curage∩X≥age]Pr[X≥curage]=Pr[X≥age]Pr[X≥curage]

Pr[X≥curage∩X≥age] is equal to Pr[X≥age] because being older than

`age`

is (in this calculation) a subset of being older`curage`

, and A⊂B⇒A∩B=A. Some precautions have to apply in the case that the probabilities of reaching`age`

is not completely independent of the probability of reaching`curage`

, but those are difficult to estimate and will not be implemented here.This way, one can implement the probability of living until

`age`

given`curage`

the following way:## Longevity Escape Velocity

Longevity Escape Velocity (short LEV) is the name for the possible year when anti-aging technology becomes so good that people can be rejuvenated faster than they age. Although the concept is considered idle speculation in many circles, many futurists justify not signing up for cryonics because they expect that LEV will arrive during their lifetime, and see no reason to sign up for a cryonics membership they are probably not going to need anyway. In this text, I will consider LEV by assuming there will be a certain year after which the probability of death from aging is practically zero.

I somewhat arbitrarily set this year to 2080, though many futurists seem more optimistic:

However, there are some reasons why one might want to stay signed up to cryonics even after LEV—there might be types of accidents where large parts of the body are destroyed in ways that both lead to death and that regenerative medicine at the time can't handle, and so one will want to be preserved until the missing bodyparts can be re-created and resuscitation is possible. Another risk to life even after LEV is new and for some time deadly diseases. This analysis does not include such considerations (yet).

## Calculating the Cost

Calculating the cost is comparatively straightforward, but there are some hidden variables (like opportunity costs and social costs) that have to be considered (not all of these in this text).

The raw cost for cryonics depends heavily on the organisation choosen for preservation, the price ranges from ~$20000 to ~$250000. In this case, I choose the costs for neurocryopreservation at Alcor, though this analysis should be extended to other organisations.

Raw cryonics cost can be split into three different parts: membership fees, comprehensive member standby costs and the cost for cryopreservation.

## Membership Fees

Membership fees for Alcor are calculated using the age of the member and the length of their membership.

## Direct Fees

— Alcor Life Extension Foundation, “Alcor Cryopreservation Agreement - Schedule A”, 2016The following assumptions will be made in the implementation:

The implementation is quite straightforward:

## Comprehensive Member Standby

— Alcor Life Extension Foundation, “Alcor Cryopreservation Agreement - Schedule A”, 2016Emphasis mine.

— Alcor Life Extension Foundation, “Alcor Cryopreservation Agreement - Schedule A”, 2016I will assume that the cryonics member starts paying a CMS fee starting 10 years before their actuarial age of death.

## Preservation Cost

There are several different methods of funding cryonics, the most popular of which seems to be life insurance. I haven't spent much time investigating the price finding mechanisms of life insurance companies, so I make the assumption that the insurance companies price their products adequately, so one doesn't have much of a financial advantage by choosing life insurance as opposed to simply saving money & paying the cryonics membership in cash. I also assume that life insurance companies can accurately price in the arrival date of LEV.

— Alcor Life Extension Foundation, “Alcor Cryopreservation Agreement - Schedule A”, 2016I assume that the person considering signing up lives outside of the U.S (but not in China), since a lot more people live outside the U.S than inside of it. I also assume that the person wants to sign up for neurocryopreservation. With these assumptions, the function that returns preservation costs becomes quite simple:

## Other Possible Costs

There is a number of different additional costs that have not been considered here because of their (perceived) small scale or difficult tractability.

These include opportunity costs for the time spent informing oneself about cryonics (tens of hours spent), opportunity costs for the time spent signing up (tens of hours spent), social costs by seeming weird (though cryonics is easy to hide, and most cryonicists seem to be rather vocal about it anyways), and alienating family members who necessarily come into contact with cryonics (considering the "Hostile Wife Phenomenon").

## Calculating the Benefit

Calculating the benefit of cryonics carries a great uncertainty, but basically it can be divided into seven distinct components: The probability of being preserved, the probability of revival, the amount of years gained by cryonics, the value of one lifeyear, the probability of living to the year when one will sign up, the probability of then dying before LEV, and the expected quality of preservation.

Here, I will only take point estimates of these values.

## Value of a Lifeyear in the Future

Much ink and pixels have been spilled on the question of the quality of the future, very little of it trying to make accurate or even resolvable predictions. Anders Sandberg summarizes the current approaches:

—Anders Sandberg, “Grand Futures”, 2023One way to look at the question could be to find clear metrics that encapsulate the most important human values and then fund a prediction market to bet on these metrics. This could include the power of humanity to make most important decisions regarding its development and resource management, diversity among human beings, average happiness and lifespans and other variables such as inequality regarding resources (see also Shulman 2013 for a more extensive list of metrics for flow-through effects, which could be used to evaluate the general quality of life in the future). Muehlhauser 2017a and Muehlhauser 2017b find that along 5 different metrics, human well-being has been improving increasingly rapidly since the industrial revolution.

But a much simpler way of approaching the topic could be the following: One takes arguments from both sides (proclaiming positive futures and negative futures) and prematurely concludes that the future is on average going to be neutral, with a high variance in the result. But some problems present themselves: In different value systems, "neutral" means very different things. Strictly speaking, a utilitarian would see human extinction as neutral, but not net neutral (the utility of a world without any sentient beings is exactly 0, which is presumably lower than the current value of the world), anti-natalists consider an empty world to be a positive thing, and most people working on preventing human extinction would consider such a world to be a gigantic loss of opportunity, and therefore net negative.

There seems to be no simple way to resolve these conflicts, otherwise it would have been written down up to now. But it seems like most people would take the current state of affairs as neutral, with improvements in happiness, meaning and wealth to be positive, and decreases in those to be negative. Also, they don't see dying tomorrow as a neutral event.

## Caveats on Future Life Years

Here I will assume that

Greaves 2017 argues against pure positive discounting for health (i.e QALYs):

— Hilary Greaves, “Discounting future health” p. 7, 2017She also argues against applying considerations from diminishing marginal returns to health (although it must be noted that this analysis does not explicitely use QALY numbers for cryonics, since they have not been collected by healthcare departments yet):

— Hilary Greaves, “Discounting future health” p. 3, 2017Additionally, problems with interpersonal utility comparison do not apply in this case, since the externalities from signing up for cryonics are disregarded in this analysis.

## QALYs and VSL

There are two different methods of putting a value on human life: the VSL and the QALY. The Wikipedia page on VSL lists $181893 for the value of a year of life in Australia, and $50000 as the "de facto international standard most private and government-run health insurance plans worldwide use to determine whether to cover a new medical procedure". This number seems like a good conservative estimate.

Interestingly, this approximately equals a year of waking hours worth the minimum wage in some countries (($10

167*52=$58240)).Intuitively, the probability distribution over the value of a year of life in the future should then look like this:

this graph is not based on real data and only here for illustrative purposesBut one can take another factor into account: Most negative future scenarios don't lead to resuscitation (civilisational collapse, stable totalitarianism, existential catastrophes like AI failure, nuclear war, biotechnological disaster and natural catastrophe all reduce human capabilities or keep them constant, preventing the development of resuscitation technology). In most of the negative futures, there are either no more humans around or people don't have time, energy or resources to bring people back from cryonic preservation (if indeed they still

arein preservation by that point), and for malicious actors, in most scenarios it is easier to create new people than to bring preserved people back.This effect might be dampened by the consideration that most possible futures have net-negative value, but on the other hand, nearly all of those futures don't lead to resuscitation.

Furthermore, Christiano 2013 outlines two reasons that indicate that the future will be good:

— Paul Christiano, “Why might the future be good?”, 2013This would mean that the probability distribution over the value of a lifeyear in the future conditional on being resuscitated could look like this:

this graph is also not based on real data and only here for illustrative purposes## Negative Scenarios

However, I can think of 3 very specific (and thereby highly unlikely) scenarios where people could be resuscitated into a (for them) net-negative world.

## Ascended Economy

The ascended economy is a scenario where the development of capitalism diverges significantly from the desires of humans, leading to most (if not all) of humanity becoming extinct. It seems highly unlikely, but possible that cryopreserved humans are placed into the hands of an algorithm that invests the money in the relevant funds to resuscitate the cryopreserved humans at a certain point. This algorithm could receive little (or no) information on what to do with the resuscitated humans afterwards, leading either to these humans quickly dying again because of an economy where they are worthless, or being kept alive solely for fulfilling the contract that is embedded in the algorithm. This might lead to insanity-inducing boredom as the humans are kept alive as long as algorithm manages to, possibly hundreds or thousands of years. This would have net-negative value for the people resuscitated.

## Malevolent Future Actors

A superintelligence becomes a singleton and starts behaving malevolently because of a near miss in its implementation or or because it has been set up by a malevolent human. This would lead to cryopreserved people being resuscitated, having their brains scanned and executed as a brain emulation, copied and put into very painful conditions.

## Information from the Past is Valuable

In a future where agents that don't care about humans find the cryopreserved remains of humans, they might be interested in extracting information from those brains. If it is not possible to extract this information without reviving the cryopreserved people, they might resuscitate them and then interrogate these revived people for a very long time, with little regard for their well-being.

## Being Revived as an Emulation and Forced to Work

A relatively common worry that prevents people from signing up for cryonics is a scenario in which their brain is scanned and they are transformed into a whole-brain emulation, which will then be used for slave labour or worse. This is often accompanied by linking qntm 2021.

For this to occur, several things need to happen:

For 2 to occur, somebody needs to be interested in emulating the person in question. This could be because they are part of the cryonics organisation, and want to fulfill the contract that the cryonics organisation and its members have signed, or because they want to use the person for slave labour. In the latter case, they will usually seek the most profitable option, and several forces work against the preserved person being that option:

A basic tension is that if whole-brain emulation is wide-spread and the incentive is economic, then it'll use more competitive brains from living humans that are easier to scan. If whole-brain emulation is obscure, it's unlikely that it and cryonics will come into contact in a way that harms cryonics patients (at the very least, it would be counted as grave robbery and body snatching).

That said, if scanning living or recently deceased humans is illegal or very hard, but uploading and using cryogenically preserved humans is legal or less difficult, then this might be a legitimate worry, because a preserved human can't take any actions to prevent this scenario.

## Steps for Reducing the Risk from such Scenarios

— Alcor Life Extension Foundation, “Cryopreservation Agreement” p. 15/16, 2012Although not a failsafe measure, steps can be taken to reduce the risks from hellish scenarios above by making arrangements with cryonics organisations. This may include not wanting cryopreservation to continue in an ascended economy, objecting to revival as an emulation or revival after more than a certain number of years (to prevent being resuscitated in an incomprehensibly strange and alien world).

## Other Thoughts

Many people argue that the value of a year of life in the future might be much lower than in the present, because friends and familiy are not around, and it is very likely that the future will be extremely alien and unfamiliar.

These are valid considerations, but can be dampened a bit: Humans have shown to adapt to very different and varied circumstances, and humans today feel that modern life in big cities with regular calendars and highly structured lives without any worries about survival is normal, while for most humans who ever lived, this would be anything but. One can speculate that very similar facts will also hold for the future (becoming increasingly unlikely the further resuscitation lies in the future). There would certainly be a big culture shock in the future, but it seems not qualitatively different from the shock people have when they visit different countries today. It is possible that future societies might try to help people with this kind of future shock, but that is of course far from certain.

It is true that most cryonicists will not be able to convince their friends and family to sign up for it too, so they will be alone in the future at first. People today sometimes leave their friends and even families to move to other places, but those people seem to be the exception rather than the norm. However, people nearly always move on with their life, even as they get divorced, get estranged from their friends or see their children less regularly—they don't seem to prefer death to continuing their lives without specific people. This consideration doesn't seem to be a true rejection.

After these considerations, I conservatively set the value of a lifeyear in the future to $50000.

## Probability of Revival

— Gwern Branwen, “Plastination versus Cryonics”, 2014Besides these estimates, there exist also two related questions on the prediction website Metaculus. "Before 1 January 2050, will any human cryonically preserved for at least 1 year be successfully revived?" has a median probability of 16% (n=117), "If you die today and get cryonically frozen, will you "wake up"?" receives 2% (n=407). I am not sure where the difference comes from, perhaps either from worries about the quality of current preservation or from a low trust in the longevity of cryonics organisations. This google sheet contains 7 estimates of success: 0.04%, 0.223%, 29%, 6.71%, 14.86%, 0.23% and 22.8%, with various different models underlying these estimates.

Calculating the mean of these results in a chance of ~13%:

0.2%+15%+13%+77%+85%+6%+23%+9%+17%+0.2%+1100∗18%+59∗12%+117∗16%+407∗2%+0.04%+0.223%+29%+6.71%+14.86%+0.23%+22.8%1700≈13.83%

It would certainly be interesting to set up a prediction market for this question, or get a team of superforecasters to estimate it, but basically, it seems like for a young or middle-aged person, the estimated probability is around 10%. However, the people surveyed are often sympathetic to cryonics or even signed up, and people in general are overconfident, so being conservative by halving the estimate seems like a good idea.

This is quite pessimistic: It assumes that cryobiology will make no progress

whatsoeverin the fidelity of the preserved tissue (remember, the probabilities given are usually for the success of a preservation given that it happensnow).## Years Gained

Conditional on being revived, what is the average life expectancy?

If revival is achieved, it seems highly likely that aging and most degenerative diseases have been eradicated (it makes little sense to revive a person that is going to die again in 10 years). Also, most revival scenarios hinge upon either the feasibility of very advanced nanotechnology, which seems to be highly conducive to fixing aging, or on whole brain emulation scenarios, which would likely make aging unnecessary (why degrade a digital brain?).

If revival happens, there are still risks from accidents and homicide or suicide that can kill the resuscitated cryonicist, as well as existential risks that face all of humanity.

The website Polstats illustrates the risks purely from accidents and homicides using data from the Information Insurance Institute. They arrive at "a much more impressive 8,938 years" average life expectancy. An answer on Mathematics StackExchange to the question "What's the average life expectancy if only dying from accidents?" arrives at 2850 years.

Ascani 2019 conservatively estimates a life expectancy of ~1000 years for each individual human after LEV.

The life expectancy of cryonics is thus is (just taking the average of these three values) 8938+2850+10003≈4260.

To conclude, it seems like resuscitated cryonicists will on average live around years, although there is room for debate on this number.

That number should be qualified further in an "Age of Em" scenario: that scenario will contain less natural risks (emulation can be backed up, they live in a simulated world where homicide risks and car accidents make no sense), but an em also suffers from the risk of not having enough money to continue being run, and from the fact that the em era might not last several subjective millennia. Furthermore, it might be that multiple copies of the emulated cryonicist are executed. This scenario deserves further consideration (see also Hanson 1994). I will not take into account the possibility of multiple copies of the same person, and assume that only one emulation is being run (to avoid tricky problems in aggregation).

## Existential Risk

Existential risk affects three different variables in the cost-benefit analysis:

—Anders Sandberg, “Grand Futures” p. 488-489, 2023This, in turn, depends on the development of the probability of existential risk over the next 10k years. Estimating existential risk appears to be quite tricky: We can take average extinction rates across many species, or across species in the homo (≈7×10−5 per year), but that assumes that

homo sapiensis a typical species or a typical member of the genus homo, which is a bold statement about the first species to single-handedly cause a mass extinction.(Among other things.)

The probabilities, pathways, causes, risk factors, inhibitors, badness, varieties and reference classes for existential risk have been extensively belabored, so I won't roll up that entire discussion again. Unfortunately, most estimates of existential risk concern themselves only with this century, and don't make statements about medium term (i.e., the next 10k years) probabilities of extinction.

For the 21st century I'll take the number by Ord 2020: ~16.5% (equivalent to ~0.25% per year).

For the time after that, I'll lowball the number at 2⋅10−5 per year. I don't have a great good justification for this, but I find space colonization fairly plausible, which reduces x-risk, and I also find it plausible achieving transformative AI could bring humanity out of the "time of perils". See also the Ragnarök question series.

So, we can write a simple function that calculates the total extinction risk before a year:

## Probability of Being Preserved

It seems like not all people who sign up for cryonics remain cryonicists until their death, and not all cryonicists who die as members actually get preserved.

There seems to be very little data about this question, but as an extremely conservative estimate I would put the ratio of members of cryonics organizations who actually get preserved at 90% (this number doesn't make any statement about the quality of preservation). I have mailed Alcor asking for the real value, but they haven't responded yet. A cryonics member can increase this number by being diligent about their cryonics arrangement, living near the preservation facility before death, informing family members about their arrangement, trying to lead a safe life and keeping contact to their cryonics organisation.

## Quality of Preservation

A common reason for cryocrastination seems to be the belief that deaths at an earlier age have causes that make successful cryopreservation less likely, and that it is therefore not worth it to sign up early.

To determine whether this is correct, one can investigate the leading causes of death by age group and estimate their penalty on successful cryopreservation. Note that my medical knowledge is very slim, and I might be missing many obvious factors.

But isn't this already priced in into the probabilistic estimates of success? Basically, yes. But In order to tease out the optimal point for signing up for cryonics, I will have to include them in this analysis again. That is unfortunate, but I think the benefit in information is worth the cost in (slight) bias, and it adds to the conservatism of this estimate. Interested readers are encouraged to try to modify the probability of success and observe the resulting changes in value.

I obtain the 10 leading causes of death by age group from a 2018 CDC report.

The causes of death, and their effect on successful cryopreservation (as a percentage; reduction in probability of successful resuscitation counterfactually to ideal conditions, e.g. controlled voluntary deanimation), as well as sometimes explanation for reasoning for the number:

shudder), but a lot is probably very damaging to the brain and/or collateral to homicide in the rest of the family? Perhaps also by the infant being killed by parents (probably shaking/suffocation)These numbers are entered into a Lua table of the following format:

For the age groups starting from age 15, NCHS 2018 provided the number of deaths by age group (I don't understand why they had to start at age 15 and not just include the whole data).

For the missing first 4 categories (0 to 1 year, 1-4 years, 5-9 years, and 10-14 years), total deaths were calculated under the assumption that the top 10 causes of deaths account for 73.8% of the total number of deaths in that age group (see Xu et al. 2020 p. 2).

For every age group, it was assumed that the average preservation quality for the remaining causes of death was 60%.

I can now write another function that calculates the expected quality of cryopreservation given that one signs up at a certain age.

This can be done by "simulating" signing up at a certain age, and then observing which deaths one might have died, and their implications for cryopreservation.

This is achieved by iterating through

`deathcause_impact`

and only observing deaths if they're above the signup age:If the signup age is in the given age group, one needs to calculate a weighing factor for the time the cryonicists will spend in the given age group:

Then, in case the age group lies further ahead in the future than

`age`

, one can calculate the deaths weighted by impact on cryopreservation and prevalence (and, in one case, the factor for the time spent in the age group):This adds up the deaths that have occured, as well as the deaths weighted by (hypothetical) preservation quality.

Now,

`weighteddeaths`

should contain a number whose meaning is roughly "number of deaths that lead to successful cryopreservation, relative to optimal conditions, under real world death circumstances", and`alldeaths`

should contain a number that means "number of deaths that lead to successful cryopreservation, under ideal circumstances".The factor that now interests us is

`weighteddeaths/alldeaths`

, so the function executesNow we can simulate whether, in this model, age of signing up has any impact on the quality of preservation:

Apparently, the differences in quality of preservation by age are negligible, although the low expected quality of preservation is quite shocking.

The low amount of variation is probably due to the fact that most people die of old age and not due to accidents during their lifetime.

## Surviving Until LEV

The benefit of cryonics is only realized in one case: One lives to the planned year of signing up, but then dies before LEV. Both dying before signing up or living until LEV after having signed up make the value of cryonics $0.

^{[1]}One can calculate the probability of this scenario by multiplying the probabilities of living until signup with the probability of then dying before LEV.To calculate the probability of living to a given age, we can use the gompertz distribution again:

The probability of dying before LEV is 0 if LEV has already occurred:

Othewise, we assume that one has signed up for cryonics at

`age`

and now wants to know the probability of dying until LEV. That is the same as 1−Pr[Living until LEV], or the probability of living until`curage+(levyear-curyear)`

given one has already lived until`age`

.## Extinction Risk After Revival

Not only does extinction endanger the cryonicist's revival, it also (in expectation) shortens their lifespan

afterrevival. To estimate, it is important to know whether the revival happensbeforeorafter2100 (the arbitrary cutoff date when the hinge of history is over and humanity has passed the time of perils). The years of revival before 2100 are in expectation less valuable than the years after 2100.This can be easily expressed by taking the extinction risk during the lifespan after revival and subtracting the extinction risk before revival, no complicated calculations required.

## Putting it All Together

One can then simply calculate the benefit of signing up for cryonics at a specific age:

The cost is easier to calculate, as it has fewer factors:

The the value of signing up for cryonics is simply the expected cost subtracted from the expected benefit:

## Results

The complete code for the model can be found here.

## Standard Parameters

With the parameters presented above, it turns out that it is optimal to sign up for cryonics right away, mainly because the motivation drift punishes the procrastination quite heavily.

## Currently 20 years old

At the age of 20 years, the value of signing up for cryonics the same year is $848035 (~8.5⋅105$)accordingtothismodel,prolongingthedecisionuntiloneis30reducesthisnumberto$600000( 6 \cdot 10^5$), and waiting until 40, 50 and 60 years yields a value of $399948 (~4⋅105$),$253943( 2.5 \cdot 10^5$) and $142394 (~$$1.4 \cdot 10^5$), respectively.

## Currently 40 years old

The values of signing up for cryonics are much higher for a 40 year old. Performing the signup immediately at age 40 is worth $3009892 (~$$3 \cdot 10^6$) at age 40 and is the best time to do it.

## Without Motivation Drift

Since many people question the idea of motivation drift and trust themselves in the future a lot, one can simulate this trust by setting the

`decay`

parameter to 1.In this model, a very different picture emerges:

It is now optimal to wait for 30 years, with an added value of ~$800. This is probably due to very slight variations in the quality of cryopreservation at different ages of death.

So in the case of high self-trust, it seems possible that limited amounts of cryocrastination might indeed be rational, although the benefits are so small that they might be swamped by even slight changes to the factors for the quality of cryopreservation.

And, in case anybody was wondering, at age 26 the model also recommends deferring to age 52:

For ages 20-25, it recommends waiting until the age of 52, and if you're older, it usually recommends to sign up at 54 (at ages above 54 it tells you to sign up immediately).

## The Critic's Scenario

Somebody who is very critical might object and argue that the probability of success is much lower, and even if cryonics succeeds, it will not lead to extremely long lifespans. Let's say they also don't believe in value drift. Such a person might propose the following assignment of variables:

In this case, signing up for cryonics has negative value that converges to 0 the older one gets:

Please note that the following graph should have negative values on the y-axis. This should get fixed sometime in the future.

## Other Modifications

It is possible to think of many other modifications to the parameters in the script, including the probability of cryonics success, the value of a lifeyear, the amount of years gained, or even bigger modifications such as adding models for the probability of the development of life extension technology in the near future.

The reader is encouraged to modify the variables and execute the script to determine whether it is advantageous for them to sign up for cryonics, and if yes, whether cryocrastination would be a good idea.

## Appendix A: A Guesstimate Model

The website Guesstimate describes itself as "A spreadsheet for things that aren’t certain". It provides Monte-Carlo simulations in a spreadsheet-like interface.

I use Guesstimate to calculate the uncertainty in the value provided by signing up for cryonics as a 20 year old. The model is available here.

## Variables

Most of the parameters were simply taken from this text, but some deserve more explanation.

## Year for Longevity Escape Velocity

— Aubrey de Grey, “Aubrey de Grey on Progress and Timescales in Rejuvenation Research”, 2018The 90% confidence interval for this variable lies in [2040;2150]: Aubrey de Grey gives a mean of 2038, I believe that number to be quite optimistic, but not completely so. He doesn't give a lower bound, but judging from the reasonable assumption that longevity escape velocity is likely not 2 years away, this seems log-normal distribution-ish, which is also what I used in the spreadsheet, with a 90% confidence interval in [2040;2150].

## Age at Death

Unfortunately, Guesstimate doesn't support Gompertz distributions, so I had to approximate the age of death by assuming that it was a log-normal distribution with the 90% confidence interval in [58.9;95.5], but mirrored along the y-axis. The data by Wolfram Alpha looks similar to the end result, and both have a mean age of death of ~83 years.

## Years Lived After Revival

This was another log-normal distribution, with a 90% confidence interval of [20;10000] years. Why the huge range? On the one hand, revival without sufficient rejuvenation technology seems unlikely, but possible; another possibility is being revived and then dying in an accident or war. The high upper range accounts for a very stable future with rejuvenation technology. Although the distribution is log-normal, the mean is still 32000 years, and the 50th percentile is around 1300 years.

## Value of Lifeyears After Revival

Here, I assumed that both negative and positive development of the future is equally likely, resulting in a normal distribution with a 90% confidence interval in [−50000;150000]. I personally believe that being revived in a future with negative value is quite unlikely, as outlined in this section, but it's always what people bring up and want to argue about endlessly (perhaps trying to convince me of their values or test whether mine are acceptable), so I included the possibility of substantial negative development.

## Provider Cost per Year

Implementing the whole

`membership_fees`

in Guesstimate seems possible, but incredibly burdensome. I approximated it using a normal distribution with a 90% confidence interval of [400;900].## Value

The result is certainly interesting: in this model, signing up for cryonics has a mean value of $18m and a median of ≈-$100k (perhaps because of longevity escape velocity arriving and making the value simply the cost for signing up), but with very long tails, especially on the positive side: a fifth percentile of -$2.15m, and a 95th percentile of

squints$58.5m—quite a range!The minimum and maximum of the simulation are even more extreme: -$39b for the minimum and $20b for the maximum (these vary strongly every time the simulation is run).

Because of these huge numbers, it makes sense to try to visualize them logarithmically. I exported the numbers for the variable 'Value' from Guesstimate and converted them into a Klong array.

Note that the scale is logarithmic to the natural logarithm (symmetrically for both negative and positive values), not the logarithm to base 10, because this makes the data more granular and therefore easier to understand.

As one can see, the distribution has turned out sort-of bimodal: Most cases of signing up for cryonics have a value of -$100k (presumably because longevity escape velocity arrives first), the rest is either very negative of very positive. To be exact,

`(+/{*|x}'flr({*x<0};incidence))%#logvalues`

≈58.7% of cases have negative value, and`(+/{*|x}'flr({*x>0};incidence))%#logvalues`

≈41.3% of cases have positive value. Of the ones with negative value, most are simply flukes where longevity escape velocity arrives first:`2286%#logvalues`

≈45.7%.## Conclusion

In this model, signing up for cryonics is still a good idea from a strict expected-value perspective. But decision processes with a precautionary principle might be much more wary of doing anything rash before futures with negative value can be ruled out.

## Now What?

— Eliezer Yudkowsky, “You Only Live Twice”, 2008In case you're now convinced that signing up for cryonics is the right thing to do for you, great! I encourage you strongly to

sign up as quickly as possible, if your finances allow you to (there are cheaper associate memberships at Alcor, which get you over most of the hump of signing up at a much lower cost, which can then be activated at a later point in time into a full membership quickly, and memberships at the Cryonics Institute, which also much cheaper than Alcor, but require much higher amount of initiative to assure a quick preservation).Generally, the Cryonics Signup Guide is the best resource for anyone to guide you through the signup process, though it lacks information for people outside of the US.

Good luck, and may we see each other one day.

If you are not convinced, that's also fine. If you think you have found a good counterargument or a flaw in this cost-benefit analysis, feel free to contact me (though note that I'm generally quite unsympathetic to the prevalent position that death and aging are not clearly and strictly bad, and might be uninterested in discussion about that particular topic).

I would also appreciate good write-ups countering the arguments for cryonics, see Crowley 2010.

## Discussions

Well, this isn't

quitecorrect. One can imagine a world in which LEV is achieved for normal aging processes,butdying of very bad injuries is still a possibility. In that case, dying from those injuries is possible, but future revival from those injuries could still happen. I won't incorporate that possibility into the model, because that would make it pretty messy. ↩︎