Author Archives: Robin Hanson

Variolation May Cut Covid19 Deaths 3-30X

(Here I try to put my recent arguments together into an integrated essay, suitable for recommending to others.)

When facing a new pandemic, the biggest win is to end it fast, so that few ever suffer. This prize makes it well worth trying hard to trace, test, and isolate those near the first few cases. Alas, for Covid-19 and the world, this has mostly failed, though not yet everywhere.

The next biggest win is to find a cheap effective treatment, such as a vaccine. And while hope remains for an early win, this looks to be years away. To keep most from getting infected, at this point the West must apparently develop and long maintain unprecedented expansions in border controls, testing, tracing, and privacy invasions, and perhaps also non-home isolation of suspected cases. Alas, these ambitious plans must be implemented by the same governments that have so far failed us badly.

Yes, there remains hope here, which should be pursued. But we also need a Plan B; what if most will eventually be infected without a treatment? The usual answer is “flatten the curve,” via more social distance to lower the average of (and increase the variance of) infection rates, so that more can access limited medical resources. Such as ventilators, which cut deaths by <¼, since >¾ of patients on them die.

However, extreme “lockdowns”, which isolate most everyone at home, not only limit freedoms and strangle the economy, they also greatly increase death rates. This is because infections at home via close contacts tend to come with higher initial virus doses, in contrast to the smaller doses you might get from, say, a public door handle. As soon as your body notices an infection, it immediately tries to grow a response, while the virus tries to grow itself. From then on, it is a race to see which can grow biggest fastest. And the virus gets a big advantage in this race if its initial dose of infecting virus is larger.

This isn’t just a theory. The medical literature consistently finds strong relations, in both animals and humans, between initial virus dose and symptom severity, including death. The most directly relevant data is on SARS and measles, where natural differences in doses were associated with factors of 3 and 14 in death rates, and in smallpox, where in the 1700s low “variolation” doses given on purpose cut death rates by a factor of 10 to 30. For example, variolation saved George Washington’s troops at Valley Forge.

Early on, it can be worth paying such high costs to end a pandemic. But once a pandemic seems likely to eventually infect most everyone, it becomes less clear whether lockdowns are a net win. However, the dose effect that lockdowns exacerbate, by increasing dose size, also offers a huge opportunity to slash deaths, via voluntary infection with very low doses. Continue reading "Variolation May Cut Covid19 Deaths 3-30X" »

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Crush Contrarians Time?

If you are a contrarian who sees yourself as consistently able to identify contrary but true positions, covid19 offers the exciting chance to take contrary positions and then be proven right in just a few months. As opposed to typically taking decades or more to be shown right.

But, what if non-contrarian conformists know that (certain types of) contrarians can often be more right, but conformists see that they tend to win by getting more attention & affirmation in the moment by staying in the Overton window and saying stuff near what most others think at the time?

In that case conformists may usually tolerate & engage contrarians exactly because they know contrarians take so long to be proven right. So if conformists see that now contrarians will be proven right fast, they may see it as in their interest to more strictly shun contrarians.

Consider Europe at WWI start. Many had been anti-war for decades, but that contrarian view was suddenly suppressed much more than usual. Conformists knew that skeptical views of war might be proven right in just a few years. Contrarians lost on average, even though proven right.

Humans may well have a common norm of liberally tolerating contrarians when the stakes are low and it would take decades to be proven right, but shunning and worse to contrarians when stakes are high and events are moving fast.

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How Many Judges?

“Wow, that was sure a long slow legal process we just went through to get X punished for Y. Surely many such cases are never punished, because this process is just too hard.”

“I’ve heard that in some places it is much simpler and faster. If you have a complaint, you call over the local police officer, and he or she soon looks into it, and then makes a decision, usually that day. Fast and easy, no need for lawyers, courts, etc. Doesn’t that sound better?”

“No, that sounds terrible! What if that local police is corrupt, or biased, or stupid? Our checks and balances help correct for such problems.”

“Well in our system, after a slow expensive complex process, judges usually make the final decision. So what stops judges from being as corrupt, biased, or stupid as police?”

“Well there are a lot fewer judges than police, so we can focus our attention on a smaller number of them. For example, we can send in people undercover to try to bribe them, and arrest those who accept bribes.”

“But we almost never actually do that with judges. And we could also do that with police.”

“With judges we have an appeals system, where appeals judges fix other judges’ mistakes. And the process is public, so anyone can point to problems.”

“We could do an appeals system with police too – if there’s a complaint, call nearby police to see if they want to come make a quick appeals decision. And that process could be public.”

“We elect judges, or those who appoint them. That holds them accountable to citizens.”

“So why can’t we elect police, or those who appoint them?”

“Judges are more prestigious than police. They are picked for being the lawyers who are most respected by other lawyers.”

“Our actual police are also the most respected among people who apply to police academy.”

“Yeah but overall lawyers are more prestigious than police. They go to college, know big words, make more money.”

“And that makes them less corruptible or biased, and more just?”

“Well elites are more eager to conform, and are better able to conform, so either they will almost all be corrupt and biased or almost none will be.”

“Not sure I feel better about that. And aren’t they better at knowing how to tell when they can get away with things, so that they will be better at finding the loopholes where we are not checking, to be more corrupt and biased there? And doesn’t their conformity better help them coordinate to get away with stuff together?”

“Look, humans have long chosen to be ruled by prestigious elites, its our nature. So it must work somehow. We pick prestigious lawyers to run law, prestigious doctors to run medicine, and prestigious academics to run teaching and research. And those work well, right?”

“Okay, if it is better to be ruled by a smaller group of more prestigious people, making judges better than police, why isn’t it even better to be ruled by one most prestigious of all dictator? Who appoints and fires police or judges as they want?”

“No no, that’s terrible too! That’s too much concentration of power. This dictator could rule with impunity, because even if some of us know of his/her corruption or bias, we’ll be afraid to say so in public. He/she could crush us for our opposition.”

“But can’t judges crush us for opposing them?”

“No, that never happens. When have you ever heard of judges crushing opponents?”

“In a dictatorship, would you actually hear of the dictator crushing opponents?”

“I’m sure I would. And dictators don’t tend to be the most prestigious; they tend to be brutal thugs.”

“But won’t everyone say they are prestigious, out of fear of retaliation? And if it is better to spread out a dictator’s power, among many judges, why isn’t it even better to spread out that power among even more police?”

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Know When To Fold `Em

In the 18th century, Daniel Bernoulli—the son, nephew and brother of mathematicians Johann, Jacob and Nicolaus II Bernoulli, respectively—made one of the first great mathematical contributions to infectious disease control. While formally trained in medicine, Bernoulli is known for his research in biomechanics, hydrodynamics, economics, and astronomy. He also played an important role in the eradication of smallpox from Europe, which was likely introduced there in the early 16th century and was endemic (maintained constantly) by the 18th century. Variolation is an inoculation technique whereby a scab or pus from an individual with a mild smallpox infection is introduced into the nose or mouth of healthy individuals. This practice began as early as 1000 AD in China and India and was introduced into England in 1717, where it was initially controversial. While variolation reduced the mortality probability of infected individuals from 30% to 1%, there was a small chance that the procedure would lead to death from a full-blown case of smallpox.

Bernoulli developed a mathematical model with which he argued that the gain from variolation in life expectancy through the eradication of smallpox far out-weighed associated risks … Using overall survivorship estimates calculated by Edmund Halley (of comet fame), he then used equation (2) to predict the mortality rates in every age class in a steady-state population with a birth class of size 1300. Inoculation via variolation of all newborns would confer widespread immunity, yet entail some mortality due to variolation-induced smallpox. Bernoulli compared the annual mortality rates and average life expectancy predicted by his model to those predicted assuming universal inoculation and found that variolation saves lives even if the mortality rate associated with variolation is quite high (with his parameters, as high as 10.6%).

Bernoulli’s calculations clarified the benefits of widespread inoculation even when there are significant risks. England began widely administering variolation in the 1750’s, and upon the development of the smallpox vaccine in 1796, mandated the inoculation of all infants. Thanks to these efforts, smallpox was eradicated from England by the end of the 19th century. (more)

The method was first used in China and the Middle East before it was introduced into England and North America in the 1720s in the face of some opposition. The method is no longer used today. It was replaced by smallpox vaccine, a safer alternative. This in turn led to the development of the many vaccines now available against other diseases. (more)

For the last few weeks I’ve spent a lot of time trying to more carefully make the case for deliberate infection plus immediate isolation, via the effect of flattening the curve to avoid overwhelming the medical system. (See my model, and models by Zach Hess and Kevin Simler.) Especially via exposing the voluntary young and healthy while isolating the old and sick. However, a few days ago I realized there’s a much stronger reason for deliberate infection: mortality varies greatly by the size of initial infectious dose, and deliberate infection allows for much lower doses. As the above indicates, this practice is a thousand years old.

As soon as your body notices an infection, it immediately tries to grow a response while the virus tries to grow itself. From then on, it is a race to see which can grow biggest fastest. And the virus gets a big advantage in this race if its initial dose of infecting virus is larger. This effect is widely known and acknowledged by experts, and it is one of the main rationales given for wearing masks, gloves etc. That’s not just to cut the chance of an infection, but also to cut the initial dose.

Many studies have found big effects of initial virus dose on many outcomes. For covid19 we know that patients with more viruses in their blood (higher “viral load”) show more severe symptoms. And for other viruses we see that such patients also die more often. But in terms of the most direct sort of evidence, I’ve only been able to find these empirical studies connecting initial virus dose size to human death rates:

  1. Deliberate infection with low doses of smallpox is reported to have cut death rates of infected from 30% to 1-2%, or from 1 in 5-6 to 1 in 50.
  2. Among 126 African kids infected with measles, the first in a family to get it had a 14x lower death rate relative to other kids in the same families. Presumably that first kid gets it from outside the family, via a low dose, while other kids in the same family are infected at home, via a larger dose.
  3. In a Hong Kong high-rise, one resident infected many others, possibly via aerosols, but those who lived physically closer got a higher dose, and saw 3x the death rate.
  4. This New Yorker article mention 2 more cases, but I can’t yet find cites to studies.

The first case, of a deliberate low dose infection, saw effects in the range 8-30x, while the other two cases of observing a natural difference in dose saw effects of 3x and 14x, giving only lower bounds on deliberate dose effects. So while we can’t at all be sure of the deliberate dose effect for Covid19, we have good reason to expect it to be at least a factor of 3. And maybe a factor of 30 or more.

I hope to include this dose effect in my spreadsheet model soon, but clearly this effect adds greatly to other benefits of deliberate infection, including not just flattening the curve, but also creating good places for controlled experiments and medical training.

The articles quoted above says that this policy was opposed and controversial back in the 1700s, and I’ve seen how many react badly when I’ve tweeted it. Twitter recently locked the account that linked to an op-ed making a similar suggestion. Many have told me privately that I should not write on this in public. But it seems to be far too important to suppress.

We are today proud of having anesthetics, and would think it cruel to do surgery on someone without it. But long ago they had no anesthetics, and so had to be cruel. And today we’d choose to be cruel again if surgery were essential but anesthetics were unavailable, such as on a battlefield. Similarly, humanity is proud of having replaced variolation (i.e, deliberate low-dose infection) with less-cruel vaccines. But in this crisis we don’t have vaccines, while variolation remains quite feasible. We should thus stand ready to swallow our pride, and use variolation if that’s our best remaining option. (As others have been also suggesting.)

Dose effects seem good candidates for explaining much of the wide variation in observed Covid19 death rates across regions and subpopulations, in addition to age, comorbidity, selection effects, virus strain variations, genetic susceptibility differences, and overwhelming of medical systems. Medical workers plausibly get high doses, and the first few cases in a region would be from travelers who were likely infected with low doses. Places where large families live together closely would have higher doses. And lockdowns that limit non-family contact may similarly induce higher doses, raising death rates.

If lockdowns increase typical infection doses, that makes more difficult the tradeoff between on the one hand (a) that higher-dose mortality cost, (b) large costs of lockdown economic and social disruption, and (c) risks from centralizing power and losing freedoms, and on the other hand benefits of: (1) more time to grow medical resources, (2) flattening the curve of medical demand over time, and (3) hope for a complete suppression until a strong medical treatment arrives, so that most are never infected.

This tradeoff isn’t obvious to me; I’d like to see more detailed cost-benefit analyses. (The very idea of which apparently offends many.) But the bigger the mortality cuts from deliberate infection, the more I’m tempted to take that bird in the hand, rather than gamble on the two bush birds of being able to achieve even larger gains via complete suppression until a vaccine.

We now sit at a great pandemic poker table, playing a huge hand with nature. We could fold and lose the ante we’ve put in, accepting many regrettable deaths due to deliberate infection. Or we can push in 3-30 times as many chips as we have so far, in the form of human lives at risk, hoping for full suppression until strong treatment, just to win back our ante (no bigger pot at stake). Do you feel lucky, punk? I don’t.

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Do You Feel Lucky, Punk?

A recent influential report posed the key Covid-19 issue today as: to mitigate or suppress? Should we focus on “flattening the curve” under the assumption that most everyone will get it soon, or adopt even stronger measures in an attempt to squash it, so most never get it.

Some simple obvious considerations are:

  1. if successful, squashing saves many more lives,
  2. you have to do a lot more to squash than to flatten,
  3. while flattening policies need be maintained only for a few months, squashing policies must be maintained until a strong treatment is available, probably years, and
  4. squashing is far easier when you have only a few infected and when your trading, travel, and physical neighbors don’t have many infected.

Several nations, mostly Asian, seem to have successfully squashed so far, though they started when they had few infected. China and perhaps S. Korea are the main examples of squashing more than a tiny number of infected, though even they had far fewer than we do now in the West where so many are suddenly eager to squash. China had much recent experience with mass surveillance, controlling population movements, and enforcing strict rules. Even so, they screwed up badly early on, and it isn’t at all obvious that China’s squashing will keep working as they let people go back to work, or when many big neighbors get highly infected.

The main point I want to make in this post is that trying to get your Western government to suppress Covid-19 in the usual way is making a big bet on the their quality, and the quality of typical neighboring governments. And also of your public’s commitment. As in the famous Dirty Harry (non-)quote, I ask: “Do you feel lucky, punk?”

Western government agencies and expert communities so far have had a bad record dealing with Covid-19. At first they criticized China’s strong measures and focused on signaling political correctness. The US government badly screwed up the generation and regulation of tests and masks, and the West continues to fail to cut regulation preventing rapid expansion of medical personnel and resources. Western governments only changed policies when public opinion changed, and even now seem more focused on handing out cash to allies, and symbolic but useless acts like banning bicycles.

As with most policy, you must expect that the details matter a lot. So even if you see China policy as a success, you shouldn’t have high hopes if your government merely copies a few surface features of China policy. That only works if this is a simple problem, with simple solutions, and few problems are that simple. This is not just a problem of insufficient moral fervor.

You should have higher hopes if they copied the whole China policy package relatively exactly, and even higher if the Chinese officials who managed their policy implementation personally came to manage implementation here. Even then climate, cultural, or infrastructure differences might mean their policies don’t work here. But no government seems even interested in copying the exact China package, and in my recent poll, 80% of 927 opposed this last idea of Chinese management.

Dear Western citizen, your government has already demonstrated incompetence at dealing with this in the absence of public pressure, and public pressure will mainly push them to do what they guess they would be most blamed by the public for not doing if things go badly. Regardless of whether that actually works; the public may never learn what actually works.

This pandemic has already been allowed to get much bigger than any that has ever been squashed before, and it is harder to squash than most, passing via the air, living on surfaces for days, and with infected folks showing no symptoms for a week. And in contrast to China, your government doesn’t have much recent experience with the mass surveillance, movement controls, and strict rule enforcement.

And yet now at this late date, you are considering if to authorize these same governments to oversee not just large efforts to flatten the curve, but the more extreme efforts required to squash it. Even knowing that to make it work you’ll need very strong public support in a far less-communal culture than those that have so far managed to squash.

Mind you, you are now considering this not because you have great confidence in your government’s competence, or your public’s support. But mostly, it seems, because it would look morally bad for you to give up hope on the millions who will die even if we flatten the curve well. Really, do you feel lucky, punk?

Also consider: even if your local government manages to successfully squash its internal infections temporarily, what happens if half of its neighbors fail, and become mostly infected? Or what if they succeed for a while, but half of their neighbors fail? What will it take to keep external infections from overwhelming you then? Or what will it take for your government and others to coordinate to ensure that most governments succeed? Remember, these are the governments who have so far largely failed to prevent massive illegal immigration, and who continue to fail to coordinate to limit global warming, war, and ocean overfishing, or to promote global innovation.

This wouldn’t matter much if the policies for squashing looked much like the policies to flatten, so we could actually flatten but pretend for a while that we were trying to squash. But there are policies that could help to flatten that look obviously bad for squashing, such as deliberate exposure, which might cut 3/4 of life-years lost. And locking down the economy and social contacts for many years at a level that looks at all like it might succeed in squashing is going to involve enormous costs to the economy and your freedoms.

In my recent polls, 73% and 74% of 393 and 533 respondents predicted US and world (respectively) will become >25% infected before an >80% effective treatment was given to >80% of world. So 3 in 4 agree that global containment just isn’t going to happen. Yet, to show that they care, most governments are giving lip service to squashing as their goal, not flattening. How far will we all go in paying huge costs to pretend that this is at all likely?

Before we all jump off this cliff together, can we at least collect and publish some honest estimates of our chances of success? Such as perhaps via conditional betting markets? If you aren’t willing to exactly copy the whole China policy, or have them manage it, how serious could you really be about succeess?

Look, this is like starting a war. Its not enough to ask “would it be nice to win such a war”, we need to ask “can we actually win?” Don’t start what you can’t finish.

I fear suppression is a monkey trap; afraid to let go the nut of saving everyone, we’ll be trapped in the gourd of not saving nearly as many as we could have.

Added 20Mar: Note that the many responses defending suppression talk about how many lives could be saved, and how they can imagine a plan that would work, but none address the issue of how competent is our government to implement such plans. Amazing how easily people slip from “it could be done” to “my government could do this”.

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Expose The Young

23Mar: Sorry for doing this, but I found errors, & can’t stand to let them stand, so I’ve edited this substantially to fix them. The more features one adds to a sim, the more chances for bugs!

In my previous analysis of (voluntary) deliberate exposure (plus immediate isolation), I assumed that people were quarantined and exposed at random. But there seem to be good arguments for preferring either the young & health or the old & sick early. Exposing the old early gives them access to a less overwhelmed medical system, while exposing the young early allows the population to more quickly reach “herd immunity”, saving the old from later exposure.

To see which effect is stronger, I’ve changed my spreadsheet model to let the population be split into two groups who can differ in their death rate, and in whether they are quarantined or deliberately exposed.

As before, I start with one random contagious person in a US-sized population of 327M uninfected. After 7 days each contagious person becomes visibly sick, 10% of these need an average of 7 ICU days of help, and after 7 days some fraction of sick folks die, while the rest recover and are immune. Sick folks are added onto the usual 10K people who need ICU help each day, and their death rate goes as the log of the daily total number of people who need ICU help. If only 10K people total need ICU help, only 0.4% of sick folks die, but if 50K per day people need ICU help, then 3% of them die. (See below for alt death rate function.)

The number of infected people who become contagious each day is proportional to the product of the uninfected count times the contagious count. Except that there is a quarantine that always holds 10M people, with a proportion of contagious vs. uninfected the same as the larger population from which it is drawn. People in quarantine have only 2% leakage of the usual rate of infecting others. (See below for alt leakage rate.) The infection rate parameter is set so that, early on, the death so far count doubles about every 6 days. 

To allow an old and a young group, I found US estimates on the population fraction and life-years remaining for different age and gender combinations, and also on how COVID19 death rates change by age, gender, and comorbidity.  From these I’ve defined 72 groups which vary in age, gender, and a binary health-or-not, and produced estimates for each group on life-years remaining and COVID19 death rate. Sorting these groups by death rate, I can split them many ways into two groups: “young” with a lower death rate and higher remaining life-years, and “old” with a higher death rate and lower life-years. Depending on the cutoff between young and old, the ratio of their death rates varies from 36 to 6781, and the ratio of life-years remaining varies from 1.7 to 10.4. 

I compared 8 options with varying combinations of who is quarantined: all (= random), young only, or old only, and also who is deliberately exposed: none, all (=random), old only, and young only. As before, I assumed a quarantine big enough to hold 10M, and that higher death rates induce higher needed ICU days. For each option I crudely searched to minimize the total lost life years by varying the young vs. old cutoff, the number of days of deliberate exposure (E days), and the fraction of the quarantine used for those deliberately exposed (D % of Q).

The following table shows my results, as do 3 graphs at the end of this post.

The 4th and 5th columns (% Deaths and % Adj. Deaths) give the percent of deaths, and life-year-adjusted deaths for each option relative to the first (baseline) option. Note that both choosing to deliberate exposed random folks, and choosing to quarantine the old, each save about 18% of life-years lost. Combining those policies, via quarantine the old and deliberately expose the young, saves about 43%.

For all but the last two options in the table, the optimal age cutoff had 24% being “old”: healthy men 60+ and women 65+, and also unhealthy men 40+ and women 50+. For the last two options, the optimal age cutoff had 26% being “old, adding in only healthy women 60-64.

The next three columns give percent chances of dying relevant for the deliberately exposed. % Lives/E is about the average % chance of deaths avoided in others per deliberately exposed person, relative to the same quarantine policy but no one deliberately exposed. % Die not E and % Die if E are about the % chances that someone would die if they die not or did become deliberately exposed. The last column Lives/Risk gives the ratio of the decreased chances of death in others over the personal increased chance of death in the deliberately exposed.

In the last two options which exposing the young, one can offer the young a dramatic motivation for choosing to become deliberately exposed. In addition to any cash compensation, or perhaps medical priority for loved ones, each volunteers to choose deliberate exposure saves someone else a 6.5-7.2% chance of death while themselves suffering only an additional 0.20-0.21% chance. And they can brag about having passed strict health requirements to be eligible. Much like a soldier at war, they can credibly claim to be an elite who made personal sacrifices to cause much larger community gains. (E.g., US soldiers in Iraq in ’06 suffered 1/255 death rate.)

Okay, I tried some variations to test robustness. I encourage you to try variations as well:

  1. I tried increasing the quarantine leakage from 2% to 10%.  Here quarantine old, expose none, gives 82% of baseline loss, while quarantine old and expose young has 77%. So a much leakier quarantine gives smaller, but still positive, gains to deliberate exposure.
  2. I tried doubling the quarantine size, and found losses, relative to baseline, of 72.7% for quarantine old, expose none, 80.9% for expose & quarantine at random, and 40.7% for expose young quarantine old. So the gains of deliberate exposure increase with the size of the quarantine.
  3. I replaced the log death function with a two-state function: The first 10K ICU cases per day get a low death rate of 0.4%, while any more get a high death rate of 3%. Results are quite similar. The optimal young/old cutoff turn out to be the same. Adjusted deaths for quarantine old is 82.4% of baseline, for exposing at random is 87.2%, and for expose young and quarantine old is 61.5%. In that last option, each young deliberately exposed saves 1.7% of another life, paying a personal added risk of .07% chance of death. So while the ratio is similar, both amounts are smaller.
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Common Useless Objections

As I’m often in the habit of proposing reforms, I hear many objections. Some are thoughtful and helpful but, alas, most are not. Humans are too much in the habit of quickly throwing out simple intuitive criticisms to bother to notice whether they have much of an evidential impact on the criticized claim.

Here are some common but relatively useless objections to a proposed reform. I presume a moment’s reflection on each will show why:

  1. Your short summary didn’t explicitly consider issue/objection X.
  2. You are not qualified to discuss this without Ph.D.s in all related areas.
  3. Someone with evil intent might propose this to achieve evil ends.
  4. You too quickly talked details, instead of proving you share our values.
  5. Less capable/cooperative folks more like radical proposals; so you too.
  6. Most proposals for change are worse than status quo; yours too.
  7. There would be costs to change from our current system to this.
  8. We know less about how this would work, vs. status quo.
  9. If this was a good idea, it would have already been adopted.
  10. We have no reason to think our current system isn’t the best possible.
  11. Nothing ever changes much; why pretend change is possible?
  12. No supporting analysis of type X exists (none also for status quo).
  13. Supporting analyses makes assumptions which might be wrong.
  14. Supporting analysis neglect effect X (as do most related analyses).
  15. Such situations are so complex that all explicit analysis misleads.
  16. A simple variation on proposal has problem X; so must all variations.
  17. It would be better to do X (when one can do both X and this).
  18. If this improves X, other bad systems might use that to hurt Y.

Many useless objections begin with “Under your proposal,”:

  1. we might see problem X (which we also see in status quo).
  2. people might sometimes die, or be unhappy.
  3. people might make choices without being fully informed.
  4. poor folks might be worse off than rich folks.
  5. poor folks may pick more risk or inconvenience to get more $.
  6. not all decisions are made with full democratic participation.
  7. governments sometimes coerce citizens.
  8. some people would end up worse off than otherwise.
  9. some people would suffer X, so you lack moral standing if you do not immediately make yourself suffer X.

So what do useful objections look like? Try these:

  1. I reject your goals, and so see no value in your method.
  2. We can only do one thing now, and payoff from fixing this is too small, vs. other bigger easy fix X.
  3. A naive application of your proposal has problem X; can anyone think of better variations?
  4. Problem X seems robustly larger given your proposal vs. status quo.
  5. Benefit X seems robustly smaller given your proposal vs. status quo.
  6. I’d bet that if we added effect X to your supporting analysis, we’d see your proposal is worse on metric Y.
  7. According to this analysis I now provide, your proposal looks worse on many metrics, better on only a few.
  8. Here is why the parameter space where your proposal looks good is unusually small, making it unusually fragile.
  9. This reform was unusually likely to have been considered and tried  before, making it is especially important to know why not.
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Vouching Fights Pandemics

As I’ve pitched vouching as a general solution to both law and medicine, the looming coronavirus pandemic offers a good and challenging concrete test; how well could vouching handle that?

If you recall, under a law vouching system, each person is required to get a voucher who stands ready to cover them for any large legal liability, including fines as punishment for crimes. Under a medical vouching system, each person gets a voucher to pay for all their medical treatments, and also to pay large amounts to a third party when that person becomes disabled, in pain, or dead. Voucher-client contracts can specific physical punishments like torture or jail, co-liability with associates, and limits on freedoms, such as re travel, privacy, or risky behaviors. 

Regarding a looming pandemic, your voucher would know that it must pay for your medical treatment, your lost salary if you stop working, and large fines if you die or get hurt. So it would offer large premium discounts to gain powers to limit your travel and contacts, and to penetrate your privacy enough to see what contagion risks you might incur. And it would have good incentives to make risky medical choices expertly, such as if to try an experimental treatment, or to accept early deliberate exposure. 

When you live with others who you might infect, or who might infect you, you’d probably also be offered premium discounts to let the same voucher cover all of you together. But there would remain key externalities, i.e., risks of infecting or being infected by others who are not covered by the same voucher.

The straightforward legal remedy for such externalities is to let people sue others for infecting them. In the past this remedy has seemed inadequate for two reasons: 

  1. It has often been expensive and hard to learn and prove who infected who, and
  2. Ever since we stopped holding family members liable for each other, and selling debtors into slavery, most folks just can’t pay large legal debts.

The vouching system directly solves (2), as everyone has a voucher who can pay lots. And the key to (1) is ensuring that the right info is collected and saved.

First, consider some new rules that would limit people’s freedoms in some ways. Imagine people were required to keep an RFID tag (or visible QR code) on their person when not at home, and also to save a sample of their spit or skin once a week? Then phones could remember a history of the tags of people near that phone, and lawsuits could subpoena to get surveillance records of possible infection events, and to see if spit/skin samples on nearby dates contain a particular pathogen, and its genetic code if present. We might also adopt a gambled lawsuit system to make it easier to sue for small harms.

Together these changes could make it feasible to, when you discovered you had been infected, sue those who likely infected you. First, your voucher could collaborate with vouchers of others who were infected nearby in space and time, by a pathogen with a similar code. By combining their tag records and local surveillance records, this group of vouchers could collect a set of candidates of who might plausibly have infected you when and where. 

(Yes, collaboration gains from voucher groups might give vouchers more market power, but not too much, as this can work okay even when there are many competing voucher groups.)

You could then sue all these possible infectors via gambled lawsuits. For the winning lawsuits, your voucher could subpoena their split/skin to see if their pathogen codes match the code of the pathogen that infected you. When a match was found, a lawsuit could proceed, unless they settled out of court. Sharing verdict and settlement info with collaborating vouchers could make it easier for them to figure out who to sue.  

Okay, yes, there is the issue of who would agree to keep RFID tags and sufficient spit/skin samples, if this weren’t required by law. I’ve proposed that the amount awarded in a lawsuit be corrected for how the chances of catching someone varies with the freedoms they keep. Such chances would be estimated by prediction markets. The lower the estimated chance of catching a particular harm for a given set of freedoms, then the higher would be the award amount if they are caught. 

So if, given the choice, some people choose not to use RFID tags or keep spit/skin samples, they may be harder to catch, but they would pay more when they do. (Which is part of why most might choose less privacy.) As a result, clients and their vouchers will know that on average they will pay for the full cost of infecting others. Which could be huge amounts if they infect many others with deadly pathogens. Which would push vouchers to work to ensure that their clients take sufficient care to avoid that. 

And that’s my concept. During the early stages of a pandemic, a system of law/med vouchers would have incentives to try the sort of aggressive case tracing that public health professionals now try. And if such professionals existed, they could collaborate with vouchers. Once the pandemic escaped containment, this vouching system would encourage people to isolate themselves to avoid infecting others, and to avoid being infected. Their freedoms of travel and privacy would become more limited, more like the limits that an aggressive government might impose. 

But exceptions would be allowed when other costs loomed larger, just as economic efficiency demands. Compared to a centralized aggressive government, a voucher system could much more easily and flexibly take into account individual differences in inclinations, vulnerability, and preferences. The choice of freedoms would be made more practical and local, and less symbol.

With vouchers and lawsuits for infections working well to get people to internalize the infection externalities, pandemics might be limited and contained at nearly the level that a cost-benefit analysis would suggest. 

Added 07Mar: Early in a pandemic it is easier to trace who infected you, and it would make sense to let you sue someone who infected you not only for the damages you suffered, but also for the damages you had to pay others who you infected. This could create very large incentives to contain pandemics early.

Later in a pandemic people sued might reasonably argue that they should only have to pay for the harm from someone being infected earlier than they would otherwise have been, which might be no harm at all during a period before the peak when medical resources are becoming spread increasingly thin.

Added 10Mar: If later in an infection it becomes too hard to trace who infected who, even with the above reforms, then it might make sense to have more general crime-law-based rules limiting social contact. Vouching can also do well at enforcing such rules.

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Simple Sims On Pandemic Variance

I’ve said it isn’t crazy to consider cutting pandemic deaths via more infection inequality, including via deliberate exposure. Some have said I’m evil to suggest that, while others have said it just can’t work. In this post, I address those latter doubts, by offering specific sim models wherein variance and deliberate exposure save lives. 

Of course, these models can’t prove that we should now adopt such policies. Every model makes specific assumptions that may not be true. The goal here is instead to show that that these ideas aren’t crazy. If they work and make sense in specific plausible situations, then we can’t dismiss them without knowing enough about our actual specific situation.

First, let me point you all to this Javascript sim model done by Zach Hess. He built this at my suggestion, but I haven’t yet learned enough Javascript to figure it all out. (Anyone want to translate it to pseudo-code?) It distinguishes 6 disease states: never-sick, exposed, recovered, asymptomatic sick, symptomatic sick, and in-intensive-care, and 3 kinds of workers: medical, critical, and general. It allows people to be put into quarantine.  

I think, but am not sure, that this model enforces a constraint on the total number of people who can fit into quarantine, and that having more available critical and medical workers makes sick folks less likely to die. Zack finds, for his default parameter values, that deliberately exposing & quarantining critical and medical workers early ends up saving lives. I presume he’s right. 

Over the last few days, I put together this simple spreadsheet model. (Feel free to copy, change, etc.) It doesn’t distinguish critical vs. medical vs. general workers, and so doesn’t capture gains from treating those differently. My baseline model starts with one contagious person in a US-sized population of 327M uninfected. 

After 7 days each contagious person becomes visibly sick, 10% of these sick need an average of 7 ICU days of help, and after 7 days some fraction of sick folks die, while the rest recover and become immune. Sick folks are added onto the usual 10K people who need ICU help each day, and their death rate goes as the logarithm of the daily total number of people who need ICU help. If only 10K people total need ICU help, only 0.4% of sick folks die, but if 50K per day people need ICU help, then 3% of them die.

The number of infected people who become contagious each day is proportional to the product of the uninfected count times the contagious count. Except that there is a quarantine that always holds 10M people, with a proportion of contagious vs. uninfected the same as the larger population. People in quarantine have only 2% of the usual rate of infecting others. The infection rate parameter is set so that, early on, the death so far count doubles about every 6 days. 

In that baseline mode, 14.3M people die within a year. The number of contagious peaks on day 168 and daily deaths peak on day 177, when 9.7% of sick folks die. I compare that baseline model with three variations. 

  1. Here, the infection rate is cut uniformly by 5%, from 1.0 to 0.95. As a result, 11.9M people die, with 16% fewer deaths than baseline. Contagious and deaths peak on days 195 and 205, and the peak death % is 9.2%.
  2. Here, instead of having one uniform population all with the same infection constant of 1.0, they are split into two initially equal-sized types, for whom these constants are 0.6 and 1.4. So while they together initially produce the same number of infected, one type gets infected 2.3 times as easily as the other type. In this variation, 10.4M people die, with 27% fewer deaths than baseline. Contagion and deaths peak on days 167 and 175, when the peak death % is 9.2%.
  3. Here, for the first 30 days 1.3M people per day are deliberately infected and then immediately placed into quarantine for 7 days until they get sick. They displace random people who would otherwise have been in quarantine. In this variation, 11.3M die, with 21% fewer deaths than baseline. The contagious count peaks on day 53, and deaths on day 40, when the death rate is 8.5%.

These simple models show that, to cut deaths, deliberate exposure can make sense, as can ways to cut infection rates and increase variance in who is more vs. less easily infected. For more details, these 3 graphs show # contagious, death % of sick, and # newly dead, all vs. days:

Of course there might be bugs in my spreadsheet; please do point them out.

Added 8am: Let me also note that in such simple models it does not help society to deliberately infect yourself, if once infected your chance of infecting others is the same as that of an average person who was infected accidentally. In that case you just pull all the curves forward in time a bit, and by increasing the rate of new sick folks slightly you increase their death rate slightly, and thus increase total deaths.

Added 09Mar: I found a small error in my spreadsheet, and so replaced the numbers and graphs above with corrected versions.

Added 17Mar: See more sims where select old or young to for deliberate exposure here.

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For Fast Escaped Pandemic, Max Infection Date Variance, Not Average

In an open column, … to provide greater dispersion, the vehicle distance varies from 50 to 100 meters, … distance between dismounted soldiers varies from 2 to 5 meters to allow for dispersion and space for marching comfort. (More)

The troop density has decreased through military history in proportion to the increase in lethality of weapons being use in combat. (More)

Armies moving in hostile areas usually spread out, as concentrations create attractive targets for enemy fire. For soldiers on foot, it might be possible to try to induce such dispersion by having a vicious wild animal chase them. After all, in the process of running fast to escape, they might spread out more than they otherwise might. But this would be crazy – there’s no reason to think this would induce just the right level of dispersion, and it would have many bad side effects. Better just to order soldiers to deliberately space the right distance. 

For a very infectious pandemic like COVID-19, clearly not contained and with no strong treatment likely soon, the fact that medical resources get overwhelmed toward a pandemic peak creates a big value in dispersion – spreading out infection dates. But, alas, our main method is that crazy “chased by a wild animal” approach, in this case chased by the virus itself. 

That is, each person tries to delay their infection as long as possible, in part via socially destructive acts like staying home instead of working. Like soldiers running from a wild animal, our varying efforts at delay do create some variance as a side effect. But probably less than optimal variance, and at great cost. 

Yes, delay has some value in allowing more stockpiling. For example, we should (but apparently aren’t) mass training more medical personnel who can function in makeshift ICU tents. But increasing average delay is can be less valuable than increasing delay variance. Even if we can’t just tell each person when to get infected, like telling soliders where to walk, we have several relevant policy levers. 

First, as I’ve discussed before, we might pay people to be deliberately exposed, and covering the cost of their medical treatment and quarantine until recovery. Yes, if their immunity has a limited duration, then we might want to not start deliberate exposure until there’s less than that duration before the pandemic peak. But there’s still big potential value here, especially via targeting medicine and critical infrastructure workers. 

Second, this is a situation were inequality of wealth, health, and social connections is good. In the last few years, many have loudly lamented many kinds of social inequalities that make the low feel ashamed and unloved, resulting in their more often becoming lonely and sick. Some are enough friends and money that they can afford go to all the parties, while others suffer in poverty alone. And no doubt many will cry loudly when such inequality makes the low get infected before the high.

But however bad such inequality might usually be, in a pandemic it is exactly what the doctor should order, if he could. Among a community close enough to share the same medical resources, the more that individuals vary in their likeliness of catching and passing on the pandemic, the better! Those who catch it early or late will do better than those who catch it just at the peak.  So for this pandemic, let’s maybe back off on whatever we now do to cut inequality, and maybe even open up more to whatever we are not doing that could increase inequality. 

In my next post, I’ll describe some simple concrete sim models supporting these claims.

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