How long will Alcor be around?
The Drake equation for cryonics is pretty simple: work out all the things that need to happen for cryonics to succeed one day, estimate the probability of each thing occurring independently, then multiply all those numbers together. Here’s one example of the breakdown from Robin Hanson. According to the 2013 LW survey, LW believes the average probability that cryonics will be successful for someone frozen today is 22.8% assuming no major global catastrophe. That seems startlingly high to me – I put the probability at at least two orders of magnitude lower. I decided to unpick some of the assumptions behind that estimate, particularly focussing on assumptions which I could model.
EDIT: This needs a health warning; here be overconfidence dragons. There are psychological biases that can lead you to estimating these numbers badly based on the number of terms you’re asked to evaluate, statistical biases that lead to correlated events being evaluated independently by these kind of models and overall this can lead to suicidal overconfidence if you take the nice neat number these equations spit out as gospel.
Every breakdown includes a component for ‘the probability that the company you freeze with goes bankrupt’ for obvious reasons. In fact, the probability of bankruptcy (and global catastrophe) are particularly interesting terms because they are the only terms which are ‘time dependant’ in the usual Drake equation. What I mean by this is that if you know your body will be frozen intact forever, then it doesn’t matter to you when effective unfreezing technology is developed (except to the extent you might have a preference to live in a particular time period). By contrast, if you know safe unfreezing techniques will definitely be developed one day it matters very much to you that it occurs sooner rather than later because if you unfreeze before the development of these techniques then they are totally wasted on you.
The probability of bankruptcy is also very interesting because – I naively assumed last week – we must have excellent historical data on the probability of bankruptcy given the size, age and market penetration of a given company. From this – I foolishly reasoned – we must be able to calculate the actual probability of the ‘bankruptcy’ component in the Cryo-Drake equation and slightly update our beliefs.
I began by searching for the expected lifespan of an average company and got two estimates which I thought would be a useful upper- and lower-bound. Startup companies have an average lifespan of four years. S&P 500 companies have an average lifespan of fifteen years. My logic here was that startups must be the most volatile kind of company, S&P 500 must be the least volatile and cryonics firms must be somewhere in the middle. Since the two sources only report the average lifespan, I modelled the average as a half-life. The results really surprised me; take a look at the following graph:
(http://imgur.com/CPoBN9u.jpg)
Even assuming cryonics firms are as well managed as S&P 500 companies, a 22.8% chance of success depends on every single other factor in the Drake equation being absolutely certain AND unfreezing technology being developed in 37 years.
But I noticed I was confused; Alcor has been around forty-ish years. Assuming it started life as a small company, the chance of that happening was one in ten thousand. That both Alcor AND The Cryonics Institute have been successfully freezing people for forty years seems literally beyond belief. I formed some possible hypotheses to explain this:
Many cryo firms have been set up, and I only know about the successes (a kind of anthropic argument)
Cryonics firms are unusually well-managed
The data from one or both of my sources was wrong
Modelling an average life expectancy as a half-life was wrong
Some extremely unlikely event that is still more likely than the one in billion chance my model predicts – for example the BBC article is an April Fool’s joke that I don’t understand.
I’m pretty sure I can rule out 1; if many cryo firms were set up I’d expect to see four lasting twenty years and eight lasting ten years, but in fact we see one lasting about five years and two lasting indefinitely. We can also probably rule out 2; if cryo firms were demonstrably better managed than S&P 500 companies, the CEO of Alcor could go and run Microsoft and use the pay differential to support cryo research (if he was feeling altruistic). Since I can’t do anything about 5, I decided to focus my analysis on 3 and 4. In fact, I think 3 and 4 are both correct explanations; my source for the S&P 500 companies counted dropping out of the S&P 500 as a company ‘death’, when in fact you might drop out because you got taken over, because your industry became less important (but kept existing) or because other companies overtook you – your company can’t do anything about Facebook or Apple displacing them from the S&P 500, but Facebook and Apple don’t make you any more likely to fail. Additionally, modelling as a half-life must have been flawed; a company that has survived one hundred years and a company that has survived one year are not equally likely to collapse!
Consequently I searched Google Scholar for a proper academic source. I found one, but I should introduce the following caveats:
It is UK data, so may not be comparable to the US (my understanding is that the US is a lot more forgiving of a business going bankrupt, so the UK businesses may liquidate slightly less frequently).
It uses data from 1980. As well as being old data, there are specific reasons to believe that this time period overestimates the true survival of companies. For example, the mid-1980’s was an economic boom in the UK and 1980-1985 misses both major UK financial crashes of modern times (Black Wednesday and the Sub-Prime Crash). If the BBC is to be believed, the trend has been for companies to go bankrupt more and more frequently since the 1920’s.
I found it really shocking that this question was not better studied. Anyway, the key table that informed my model was this one, which unfortunately seems to break the website when I try to embed it. The source is Dunne, Paul, and Alan Hughes. “Age, size, growth and survival: UK companies in the 1980s.” The Journal of Industrial Economics (1994): 115-140.
You see on the left the size of the company in 1980 (£1 in 1980 is worth about £2.5 now). On the top is the size of the company in 1985, with additional columns for ‘taken over’, ‘bankrupt’ or ‘other’. Even though a takeover might signal the end of a particular product line within a company, I have only counted bankruptcies as representing a threat to a frozen body; it is unlikely Alcor will be bought out by anyone unless they have an interest in cryonics.
The model is a Discrete Time Markov Chain analysis in five-year increments. What this means is that I start my hypothetical cryonics company at <£1m and then allow it to either grow or go bankrupt at the rate indicated in the article. After the first period I look at the new size of the company and allow it to grow, shrink or go bankrupt in accordance with the new probabilities. The only slightly confusing decision was what to do with takeovers. In the end I decided to ignore takeovers completely, and redistribute the probability mass they represented to all other survival scenarios.
The results are astonishingly different:
(http://imgur.com/CkQirYD.jpg)
Now your body can remain alive 415 years for a 22.8% chance of revival (assuming all other probabilities are certain). Perhaps more usefully, if you estimate the year you expect revival to occur you can read across the x axis to find the probability that your cryo company will still exist by then. For example in the OvercomingBias link above, Hanson estimates that this will occur in 2090, meaning he should probably assign something like a 0.65 chance to the probability his cryo company is still around.
Remember you don’t actually need to estimate the actual year YOUR revival will occur, but only the first year the first successful revival proves that cryogenically frozen bodies are ‘alive’ in a meaningful sense and therefore recieve protection under the law in case your company goes bankrupt. In fact, you could instead estimate the year Congress passes a ‘right to not-death’ law which would protect your body in the event of a bankruptcy even before routine unfreezing, or the year when brain-state scanning becomes advanced enough that it doesn’t matter what happens to your meatspace body because a copy of your brain exists on the internet.
My conclusion is that the survival of your cryonics firm is a lot more likely that the average person in the street thinks, but probably a lot less likely that you think if you are strongly into cryonics. This is probably not news to you; most of you will be aware of over-optimism bias, and have tried to correct for it. Hopefully these concrete numbers will be useful next time you consider the Cryo-Drake equation and the net present value of investing in cryonics.
For-profit companies have more incentives than Alcor does to take risks that deliberately expose themselves to the risk of bankruptcy. When a company goes bankrupt it’s assets (and often many of its obligations) are not destroyed, rather they are often transferred to another organization.
Frozen people are liabilities, not assets.
Yes, but if a company has assets then in bankruptcy often both it’s assets and some of its liabilities get transferred. Say Alcor goes bankrupt and a judge has to decide what to do with Alcor’s bodies and its assets. The judge would be more likely to give the assets to an organization that was likely to preserve the bodies.
Assuming somebody would want to take over a bankrupt company with liabilities as nasty as not-quite-dead humans. The liabilities of a bankrupt cryo company would vastly exceed the assets. Also, you can’t get rid of those liabilities, not even part of them, in any way which isn’t a PR disaster.
Actually, these are very-much-dead humans, with the proviso that in the future it’s possible they might become undead, erm, I mean resurrected, erm, I mean not by Jesus, erm, you know what I mean :-D
This will likely get me negative karma (whatever that is) but this is the only way I know how to post here as a new member and my question is one of immediate life and death which I think trhe Less Wrong Community can guide me on.
From a Bayesian perspective should I get rabies shots after having been bitten by a cat in Turkey?
There’s some chance that getting the shots could be detrimental (hospitals everywhere have detrimental likelihoods, in Turkey all trhe more so) and there’s almost no chance at all that I actually got rabies. If I did get it, I will die, horribly and soon. But, the eay I see it, if my chances of having gotten rabies are less than 1⁄300,000 it isn’t worth the aggravation of getting the lengthy series of shots even if there was no potential downside to getting the shots. Due to the fact that there ARE potential downsides to getting the shots I would not get them if the odds of my having contracted rabies are less than 1⁄80,000.
Here are the details.
I accidentally stepped on a stray cat’s tail 3 days ago and it jumped up and bit and scrtached me through my pants, breaking skin at each location.
So, this was a cat, it was provoked, but it was in Istanbul where apparently many ferral dogs and cats have rabies (I don’t know how to define “many”). Also, it bit me on the knee, most cases of rabies involve people bitten on the head or upper extremities.
The two most relevant artricles I found are http://journals.tubitak.gov.tr/medical/issues/sag-09-39-4/sag-39-4-14-0901-6.pdf
and http://www.sciencedirect.com/science/article/pii/S1201971205001840
Being too close to the thing, my own Bayesian thinking can’t be trusted but I’m leaning toward saying that the odds that I contracted rabies are too slight to worry about and to expend resoiurces and risks for. But what do you guys thing? From an approximately Bayesian perspective.
There’s an open thread for this kind of thing. Also, there are plenty of people online with actual legit medical training. It would be better to ask them than us.
I hope you don’t die horribly. :-)
ASK A DOCTOR. Seriously, that should be the default for any medical question. If you’re worried that the doctor won’t have a Bayesian perspective, well, go see one anyway. They’re not going to force you to get the shot, and you’ll get the information necessary for you to do the Bayesian calculation.
Consider alternatives such as flying to Germany to get the shots. Take into account how you would feel if you don’t get the shots and worry that you might have rabies.
Did you die horribly?
Reminder! Although I haven’t yet written abuot the general principle, the original Drake’s Equation was bullshit. Things like this are even more bullshit since they exploit the human bias of assigning significant probabilities to everything elicited creating an unpacking bias where unpacked items are assigned much larger summed probabilities than the corresponding packed categories, meaning that the apparent probability of a conjunction goes down as you helpfully break it into more and more parts. By these means I could equally make the Moon landing appear impossible, just as I could make cryonics appear more and more likely by considering more and more disjunctive pathways to success. It also fails as probability theory because conditional dependency.
Again, general reminder: Across all cases not backed up by actual sampling, someone who offers to helpfully “elicit” a set of “conjunctive” probabilities and multiplies them together to get some low number, without considering any disjunctions, assuming conditional independence, and with no warnings about unpacking bias, is using a Fully General Counterargument that will underestimate the probability of anything. I have yet to see a good Breaking X Down for any X, unless X is a whole population (not a significant subsector of it) and the breakdown is just the actual data about X.
Viewed from which historical time?
This is not the first time you claim that, but AFAIK you never did. I’m skeptical that this is possible.
Unless you propose a plausible mechanism for two variables to be correlated, it is reasonable to assume that they are approximately independent, (Occam’s razor, principle of maximum entropy, etc.). Also, correlations can be positive or negative.
I understand the concern about unpacking bias, and read about a related experiment also by Kahneman (I think) who elicited a higher probability when he asked experts to estimate the likelihood a specific scenario (deflation of the rouble leads to a Soviet invasion of Germany and nuclear war) than a general scenario (nuclear war). So I would be cautious of handling an equation with multiple, obviously overlapping terms. I’ll update the original post when I’m back at a computer to include a health warning in the first paragraph.
I don’t think I fully understand the criticism of this piece though; are you saying the modelling approach is incoherent or simply cautioning people not to just plug it into the cryo-Drake equation without considering the unpacking bias?
You might be thinking of an earlier discussion of this issue involving car failure diagnostics: http://lesswrong.com/lw/fz9/more_cryonics_probability_estimates/82oh?context=1#82oh
I feel like the unpacking/packing biases ought to be something that should be easier to get around than some other biases. Fermi estimates do work (to some extent). I somewhat wonder if perhaps giving log probabilities would help more.
Since there are companies that have existed for centuries, the criteria for “least volatile” should probably be something other than S&P 500. http://en.wikipedia.org/wiki/List_of_oldest_companies#1650_to_1699 A company being German or Japanese seems to be almost mandatory. Should Alcor relocate to Berlin?
89.4% of the companies with more than 100 years of history are businesses employing fewer than 300 people.
Be careful about reading too much into that—“Large enterprises, those with 250 or greater employment, accounted for only 0.4 per cent of all enterprises.” according to the ONS. You’d expect to see 89.4% small companies by chance alone, although I concede that if a company is around for 100 years you might expect it to grow into a large company by inertia alone.
With respect to your other point, you are absolutely right—I wanted to show my working here to indicate how badly wrong back-of-the-envelope calculations can go in situations like this.
Bankruptcy is normally means having debts that can’t be paid, and Alcor goes out of its way to avoid having anything that could be a debt, and is careful to maintain funds that can be used to continue to keep its patients preserved. This kind of conservatism comes at some cost in its ability to grow, so it doesn’t require unusually good management to have a higher than normal chance of continuing to exist.
There seem to have been two cryonics organizations that failed (CSC and CSNY). Some patients at CSNY were unharmed by that failure, so having your organization fail doesn’t automatically imply death. Plus people have learned from those failures.
Your chances of surviving your cryo company going bust may depend on how seriously society in general takes the idea that you aren’t dead yet.
So Alcor has, essentially, something like an endowment fund the investment returns from which pay for the expenses of maintaining the cryo facilities.
That raises an interesting question of what will happen to Alcor in the case of a purely financial crisis—say a market crash or a hyperinflationary episode...
Some types of financial crises would destroy them unless they could attract new donations. But by investing in stocks and land you can protect yourself from hyperinflation.
You can’t protect yourself from hyperinflation and volatility at the same time. If you are afraid of volatility you put money into cash and fixed income instruments which are highly exposed to inflation. If you are afraid of inflation you put money into hard assets and stocks, but these are risky investments and are subject to considerable volatility.
Don’t forget that you also MUST earn sufficient return to cover the running expenses.
You can buy Treasury Inflation-Protected Securities.
Also, I’m not convinced those would protect against hyperinflation, given the kinds of things countries suffering from hyperinflation tend to do.
Yes, you can buy TIPS the return on which is, to a first approximation, zero.
Someone—it may be the journalist writing your linked article, or the professor he quotes, or you—is confused about the difference between the lifespan of a company, and how long it stays in the S&P 500. Observe:
Very well, but that does not demonstrate that the existing 500 will be bankrupt, only that they will be smaller than, say, Public Service Enterprise Inc.
Additionally, the good professor seems to have a definition of lifespan which is spectacularly non-useful for your purposes:
You are interested in bankruptcies in which the company ceases operations. Buyouts, in which there is new management or even the same management but new ownership, are irrelevant. By that standard, YouTube is out of business!
Hi RolfAndreassen,
I’m impressed you spotted that so quickly, because it was non-obvious to me. Nevertheless, I did spot the problem you are describing and attempted to correct for it in the second graph by considering only companies which went into liquidation, using a proper academic source.
This is a nitpick, but using average (I’m assuming that means arithmetic mean) is misleading since so long as at least a nonnegotiable proportion of people is answering in the double digits every answer below 1% is being treated as essentially the same, thus skewing towards higher probabilities of cryonics working.
The reference class of “companies” is the wrong class. Unfortunately I don’t think there is a good reference class. “Organizations who exist primarily to survive, rather than grow / make money” would be a good class, but I can’t think of anything else much like this.
Oh wait, people. Like survivalist organizations, humans exist primarily to survive and we consume resources at a predictable rate. On the other hand there are a lot of things that make humans different from organizations. The relevant differences: a) we die of natural causes, whereas organizations don’t; b) we have one intelligence directing our actions and choices, whereas organizations have no intrinsic intelligence and depend on their human constituency/directorship for this. Are there other relevant differences?
Maybe you should look at how long foundations survive, esp. family foundations which have a comparable business model: Caring for humans—living ones instead of not-quite-dead ones though. I’d guess that these live significantly longer. Thre are even special tax rules for those (e.g. in Germany a family foundation has to pay a virtual death tax every 30 years).
Other relevant differences might be that humans are never allowed to just ‘die’ of making bad financial decisions in countries like America—if humans make really wild spending decisions the state will at least feed and house them.
Perhaps charities would be a better reference class? If anyone can find any data I’ll happily rerun the analysis, but ‘age charities’ will give you charities concerned with age and ‘life expectancy charities’ will give you charities concerned with life expectancy; it could be a bit of a slog.
See also Jeff Kaufman on “Breaking Down Cryonics Probabilities”.
Related lesswrong discussion from when I updated my estimates a year later: More Cryonics Probability Estimates.
I want to offer positive reinforcement for your model-generating algorithm, especially the “notice that I am confused by my model’s retrodictions, question everything” step. More posts like this, please!
On this particular topic, I want to emphasize the massive amount of uncertainty about the numbers you get; the factors you can’t control for could make a 10x difference on the x-axis, and of course the data we care about (organization survival in the future) need not resemble historical data very much at all. The model is essentially only good for an outside view / sanity check. (Of course, that’s the best we can get in complicated cases, and it’s a good improvement on ignorance priors! I’m just saying to everyone, please don’t go around citing “22.8% chance of institutional survival for 415 years” in cryonics debates.)
If a new cryonics company would spring up than it would have to be a lot cheaper than Alcor to draw customers. Having a stable 40 year history is much more important to a cryonics company than most other companies.
Alcor can ask for high prices to freeze people and thereby raise capital that makes it less likely to fold.
Unless the company had an established record doing other things. If Google got into the cryonics business I would likely transfer my membership from Alcor to them.
I wouldn’t. Technology companies have an unusually low expected lifetime, and in any case frequently undergo restructurings to deal with disruptive technologies.
See also gwern’s take on the expected lifetime of Google products:
http://www.gwern.net/Google%20shutdowns
You can also model a population as consisting of many mixed subpopulations. What if you model companies as consisting of a large short-half-life population that are doomed to fail for structural or business model reasons, and a small long-half-life population that fails due to rarer issues in the wider world or institutional drift or bad fortune? Or a gradiation of different half lives?
How does it change the numbers if you condition on the fact that Alcor has already been around for 40 years?
Reminds me of John C. Wright’s comments on the subject here
If you could come up with an organization with as much emotional oomph as the Catholic Church that took cryonics seriously, that would be very impressive, but I don’t think it’s possible.
On the other side, what would it take to convince the Catholic Church that frozen people were alive enough that care should be taken to keep them frozen until they can be revived?
In Dignitas Personae section 18 and 19 the Catholic Church asserts the personhood of cryopreserved embryos and, although it objects to IVF and other techniques for several other reasons, a major objection is that many cryopreserved embryos are not revived. It specifically objects to cryopreservation carrying the risk of death for human embryos, implying that they are either living or at least not-dead, and suggests the possibility of “prenatal adoption”, and also objects to any medical use or destruction of the embryos.
So, in a narrow sense, they already believe that frozen people are alive enough to be worth keeping frozen or reviving.
Agreed, getting the Roman Catholic Church to look after the “preserved” is probably easier than creating another institution of similar robustness.
Cryonics-like procedures aren’t totally alien to the Abrahamic religions: both Jacob and Joseph were mummified, as described in the Old Testament.
I think the problem would be justifying the expense. Since catholics believe in a supernatural bodily resurrection, they would still respect the preserved but feel no need to maintain them in a chilled state.
It would depend on how they interpreted the obligation to not kill.
Well, a number of recent fights between “rational atheists”/secularists and the Catholic church have been based on the atheists and secularists complaining that the Catholic church’s interpretation of the obligation not to kill was too strong.
Gosh.
Although in reality it makes a big difference, in my model it does not—my model varies only the size of the company, since that’s all I could find good data on. I found another source saying that the age of a company was about 30% more important in predicting its survival than its size, but because it was a complicated regression I was unable to exclude terms that had absolutely nothing to do with cryonics.
It is probable that you should shade the probability of Alcor surviving up and the probability of KryoRus surviving down to account for this.
“therefore recieve protection under the law in case your company goes bankrupt. In fact, you could instead estimate the year Congress passes a ‘right to not-death’ law which would protect your body in the event of a bankruptcy even before routine unfreezing, ”
I’m not entirely sure why you reach this conclusion. Demonstrating that a cryogenically frozen man is not dead is interesting news, but what would prompt the government to pass such a law? There are many people that can and do die without some drug or periodic medical treatment even now, yet there is no ‘right to not-death’ to protect them should they fall through the usual (limited) safety nets.
One possible mechanism would be a general social shift towards more cryogenics meaning cryo voters became an important voting block. Since most rational cryo-voters can be expected to be more-or-less single issue with respect to cryonics (almost nothing will increase your individual expected utility for a given level of money more than increasing your chance of being revivified), politicians will begin to face great pressure to appease this demographic. You’ll see that this is different to the situation you describe for at least three reasons:
On those issues where the individual utility gain is greatest, the population is smallest (cures for very rare genetic conditions which are unaffordable to the average person and yet not subsidised by the government). This is probably because it is not in the interests of politicians to use political capital on a very small sub-section of the population.
On those issues where individual utility gain is small and populations are large, the individuals concerned are unlikely to be single-issue. For example public health measures undoubtedly raise my lifetime utility, but do they do so more than public education, public art or nebulous concepts like ‘freedom’? Hard to say
On those issues where individual utility gain is large and populations are large, those populations are almost inevitably located in areas where US politicians have no incentive to help them. For example, campaigning to end malaria would be both massively important and affect a huge number of people, but those people would not be US voters.
If this social shift occurs, politicians may be incentivised to offer a ‘government guarentee’ to all frozen corpsicles, in the same way all mortage lenders are government-backed or banks are unable to go bust in an uncontrolled way (assets up to a certain value are protected). So it wouldn’t so much be a ‘not-death’ right (because all three groups I describe above would still fail to be protected from death), but I was using it as a shorthand for the slightly more complex scenario I describe here.
I don’t know how likely I think this scenario is, but I think if it is going to happen, it will happen before a post-scarcity society. In the interests of being charitable to the cryogenics companies, I think it is fair to point out that this is a mechanism that could greatly improve their chance of being revivified without any technological innovation.
It’s fair to assume that a Cryonics company would be set up to endure in the long term. Otherwise they dramatically reduce the number of people willing to sign up. This is different from a startup tech company who does not have to promise its investors and consumers that it will be around for the next 50 years. It’s kind of like the opposite of a netflix account. This should give us a lot of hope because even Netflix seems to be pretty robust.
Additionally, just because a company goes out of business doesn’t mean that all its capital is thrown away. You liquidate factories and equipment etc. It’s been pointed out that frozen people are a “liability”, but this is dependent on the contract you pay with the company.
If your estate is set up to pay $X to whatever organization is housing you, then it stands to reason that cryonics companies could move frozen bodies around.
The question is not “will my cryonics company go bankrupt”, but whether the entire cryonics industry will cease to exist. That seems pretty unlikely as we become MORE technologically advanced...