Monthly Archives: April 2020

Constant Elasticity Prevention

While many engaged Analysis #1 in my last post, only one engaged Analysis #2. So let me try again, this time with a graph.

This is about a simple model of prevention, one that assumes a constant elasticity (= power law) between harm and prevention effort. An elasticity of 1 means that 1% more effort cuts harm by 1%. For an elasticity of 2, then 1% more effort cuts harm by 2%, while for an elasticity of 0.5, 1% more effort cuts harm by 0.5%.

Such simple “reduced form” models are common in many fields, including economics. Yes of course the real situation is far more complex than this. Even so, reduced forms are typically decent approximations for at least small variations around a reference policy. As with all models, they are wrong, but can be useful.

Each line in the following graph shows how total loss, i.e., the sum of harm and prevention effort, varies with the fraction of that loss coming from prevention. The different lines are for different elasticities, and the big dots which match the color of their lines show the optimum choice on each line to min total loss. (The lines all intersect at prevention = 1/20, harm = 20.)

As you can see, for min total loss you want to be on a line with higher elasticity, where prevention effort is more effective at cutting harm. And the more effective is prevention effort, then the more effort you want to put in, which will result in a larger fraction of the total harm coming from prevention effort.

So if locks are very effective at preventing theft, you may well pay a lot more for locks on than you ever suffer on average in theft. And in the US today, the elasticity of crime with respect to spending on police is ~0.3, explaining why we suffer ~3x more losses from crime than we spend on police to prevent crime.

Recently, I asked a few polls on using lockdown duration as a way to prevent pandemic deaths. In these polls, I asked directly for estimates of elasticity, and in this poll, I asked for estimates of the ratio of prevention to health harm loss. And here I asked if if the ratio is above one.

In the above graph there is a red dot on the 0.5 elasticity line. In the polls, 56% estimate that our position will be somewhere to the right of the red dot on the graph, while 58% estimate that we will be somewhere above that grey 0.5% elasticity line (with less elasticity). Which means they expect us to do too much lockdown.

Fortunately, the loss at that red dot is “only” 26% higher than at the min of the grey line. So if this pandemic hurts the US by ~$4T, the median poll respondent expects “only” an extra $1T lost due to extra lockdown. Whew.

Added 26May: Follow-up surveys on US find (via lognormal fit) median effort to harm ratio of 3.6, median elasticity of 0.23. For optimum these should be equal – so far more than optimal lockdown!

Added 1Aug: Repeating same questions now gives median effort to harm ratio of 4.0, median elasticity of 0.18. That is, they see the situation as even worse than they saw it before.

Added 22Oct: Repeating the questions now gives median effort to harm ratio of 5.2, median elasticity of 0.10. The estimated deviation between these two key numbers has continued to increase over time.

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2 Lockdown Cost-Benefit Analyses

Back on Mar. 21 I complained that I hadn’t seen any cost-benefit analyses of the lockdown policies that had just been applied where I live. Some have been posted since, but I’ve finally bothered to make my own. Here are two.

ANALYSIS #1: One the one side are costs of economic disruption. Let us estimate that a typical strong lockdown cuts ~1/3 of income of econ/social value gained per unit time. (It would be more due to harm from time needed to recover afterward, and to due to for stress and mental health harms.) If one adds 9 weeks of lockdown, perhaps on and off spread out over a longer period, that’s a total of 3 week’s income lost.

On the other side are losses due to infection. I estimate an average infection fatality rate (IFR) of 0.5%, and half as much additional harm to those who don’t die, due to other infection harms. (E.g., 3% have severe symptoms, and 40% of those get 20% disabled.) I estimate that eventually half would get infected, and assume the recovered are immune. Because most victims are old, the average number of life years lost seems to be about 12. But time discounting, quality-of-life adjustment, and the fact that they are poorer, sicker, and wouldn’t live as long as others their age, together arguably cuts that figure by 1/3. And a standard health-econ estimate is that a life-year is worth about twice annual income. Multiply these together and you get an expected loss of 3 week’s income..

As these equal the same amount, it seems a convenient reference point for analysis. Thus, if we believed these estimates, we should be indifferent between doing nothing and a policy of spending 9 added weeks of lockdown (beyond the perhaps 4-8 weeks that might happen without government rules) to prevent all deaths, perhaps because a vaccine would come by then. Or, if death rates would actually be double this estimate due to an overloaded medical system, we should be indifferent between doing nothing and spending 9 added weeks of lockdown to avoid that overloading. Or we should be indifferent between doing nothing and 4 added weeks of lockdown which somehow cuts the above estimated death rate in half.

Unfortunately, the usual “aspirational” estimate for a time till vaccine is far longer, or over 18 months. And a doubling of death rates seems a high estimate for medical system overload effects, perhaps valid sometimes but not usually. It seems hard to use that to argue for longer lockdown periods when medical systems are not nearly overwhelmed. Especially in places like the US with far more medical capacity.

During the 1918 flu epidemic, duration variations around the typical one month lockdown had no noticeable effect on overall deaths. In the US lately we’ve also so far seen no correlation between earlier lockdowns and deaths. And people consistently overestimate the value of medical treatment. Also, as death rates for patients on the oft-celebrated ventilators is 85%, they can’t cut deaths by more than 15%.

We’ve had about 6 weeks of lockdown so far where I live. A short added lockdown seems likely to just delay deaths by a few months, not to cut them much, while a long one seems likely to do more damage than could possibly be saved by cutting deaths.

Of course you don’t have to agree with my reference estimates above. But ask yourself how you’d change them, and what indifferences your new estimates imply. Yes, there are places in the world that seem to have done the right sort of lockdown early enough in the process to get big gains, at least so far. But if your place didn’t start that early nor is doing that right sort of lockdown, can you really expect similar benefits now?

ANALYSIS #2: Consider the related question: how much should we pay to prevent crime?

Assume a simple power-law (= constant elasticity) relation between the cost H of the harm resulting directly from the crimes committed, and the cost P of efforts to prevent crime:

H = k*Pa,  or  dln/ dlnP = –a ,

where a is the (positive) elasticity of harm H with respect to prevention P. To minimize total loss L = H + P, you set P = (k*a)1/(1+a), at which point we have a nice simple expression for the cost ratio, namely P/H = a.

So, when you do it right, the more effective is prevention at stopping harm, then the larger is the fraction of total loss due to prevention. If 1% more prevention effort cuts 1% of crime, you should lose about the same amounts from harm and prevention. If 1% more prevention cuts 2% of crime, then you should lose twice as much in prevention as you do in harm. And if it takes 2% more prevention effort to cut 1% of crime, you should lose about twice as much in harm as you do in prevention.

This model roughly fits two facts about US crime spending: the elasticity is less than one, and most loss comes from the crimes themselves, rather than prevention efforts. Typical estimates of elasticity are around 0.3 (ranging 0.1-0.7). US governments spend $280B a year on police, courts, and jails, and private security spends <$34B. Estimates of the total costs of crime range $690-3410B.

Now consider Covid19 prevention efforts. In this poll respondents said 3.44 to 1 that more harm will come from econ disruption than from direct health harms. And in this poll, 56% say that more than twice the loss will come from econ disruption. For that to be optimal in this constant elasticity model, a 10% increase in lockdown, say adding 12 days to a 4 month lockdown, must cut total eventual deaths (and other illness harm) by over 20%. That seems very hard to achieve, and in this poll 42% said they expect us to see too much econ disruption, while only 29% thought we’d see too little.

(More on Analysis #2 in the next post.)

In this post I’ve outlined two simple analyses of lockdown tradeoffs. Both suggest that we are at serious risk of doing too much lockdown.

10am: On reflection, I changed my estimate of the lockdown from 25% to 27% of income, and my estimate of non-death harm from as-much-as to half-as-much-as the death harm. So my reference added shutdown duration is now 4 months instead of 6.

12pm: Even if recovery gave immunity for only a limited period, then as long as you were considering lockdown durations less than that period, the above calculation still applies, but now it applies to each such period. For example, if immunity only lasts a year, then these are annual costs, not eventual costs. And that’s only if infection chances are independent each period. If, more likely, it is the same people who at more at risk each year, then in later years gains from lockdowns decline.

29Apr, 3am: We are now at 73 comments, and so far all of them are about analysis #1, and none about analysis #2. Also, tweet on #1 got 18 retweets, tweet on #2 got none.

29Apr, 1pm: In two more polls. over half estimate a 10% increase in lockdown duration gives <5% decrease in deaths, for both world and US. Instead of the >20% that would be required to justify allowing twice the damage of lockdowns as health harms. See also results on the cost of masks.

28May:  I’ve updated the numbers a bit.

22Oct: This analysis from March 22, based on happiness, also suggests far more harm from the economy dip than from deaths. And I confirm my analysis with more recent estimates here.

23Oct: I’ve just shown that the above condition that =dln/ dlnP = P/H holds for any function H(P).

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Vouch For Pandemic Passports

Car pollution is an externality. Via pollution, the behavior of some hurts others, an effect that injurers may not take into account unless encouraged to by norm, contract, liability, or regulation. However, as the pollution from one vehicle mixes with that from many others, liability is poorly suited to discourage this; it is too hard to identify which cars hurt you, and there are too many of them. It seems to work better use regulations regarding car design and maintenance to limit the pollution emitted per mile driven, and to tax those miles driven.

Assault is also an externality; you can hurt someone by punching them. But in contrast to pollution, regulation is poorly suited to assault. We could require everyone to wear boxing globes and headgear, and we might ban insults and alcohol consumption, or perhaps even all socializing. But such regulations would go too far in restricting useful behavior. It works better to just hold liable those who punch others, via tort or criminal law. Yes, to discourage assault in this way, we must hold (or at least threaten to hold) expensive trials for each assault. But that still seems far cheaper than regulatory solutions.

These two examples illustrate a well-known tradeoff in choosing between (strict) liability and regulation. On the one hand, making people pay damages when they hurt others encourages them to take such harms into account, while also letting their behavior flexibly adapt to other context. On the other hand, regulation lets us avoid expensive court trials that require victims to prove who hurt who when where and how much. Though regulation induces more uniform behavior that is less well adapted to circumstances, it works acceptably well in many cases, like auto pollution, even if less well in others, like assault. (The mixed solution of negligence liability is discussed below.)

In our current pandemic, the main externality is infection, whereby one person exposes another to the virus. Conventional public health wisdom says to discourage infection via regulation: tell everyone when to get tested and isolated, and make them tell you who they met when and where. Tell them who can leave home when and for what reasons, and what they must wear out there. However, as we are all now experiencing first hand, not only are such changes to our usual behaviors quite expensive, such rules can also induce far from optimal behavior.

Recent pandemic rules have banned bike riding, but not cars or long walks. You can take only one exercise trip per day, but there’s no limit on how long. Members of a big home can all meet in their yard together, but members of two small adjacent homes may not meet in one of their yards. You can’t go meet distant friends, even if they only ever meet you. All parks are closed regardless of how densely people would be in there. The same rules were set in dense cities and in sparse rural areas. Alcohol store workers are deemed critical, even though alcohol can be mailed, but not auto repair, which cannot be mailed. Six feet is declared the safe distance, regardless of how long we stay near, if we wear masks, if we are outdoors, or which way the air is moving. Workplaces are closed regardless of the number of workers, how closely they interact, or how many other contacts each of them have. The same rules apply to all regardless of age or other illness. You may have to wear a mask, but it doesn’t have to be a good one.

Imagine that we instead used legal (strict) liability to make as many of us as possible expect to suffer personally and directly from infecting others, and to suffer more-so the worse their symptoms. In this scenario, such people would try to take all these factors and more into account in choosing their actions. For actions that risk infecting others, they would consider not only on how important such acts are to them, but also on how likely they are personally to be infected now, how vulnerable each other person they come near might be to suffering from an infection, how vigorously their activity moves the air near them, where such air currents are likely to go, how well different kinds of masks hinder infected air, and so on. If allowed, they might even choose variolation.

Of course, for the purpose of protecting ourselves from getting infected by others, we already have substantial incentives to attend to such factors. The problem is that simple regulations don’t give us good incentives to attend to these factors for the purpose of preventing us from infecting others. With regulations, we have incentives to follow the letter of the law, but not its spirit. So we don’t do enough in some ways, and yet do too much in others. But if liability could make us care about infecting others as well as ourselves, then it might simultaneously reduce both infections and the economic and social disruptions caused by lockdowns. With strong and clear enough liability incentives, we wouldn’t need regulations; we could just let people choose when and how to work, shop, travel, etc.

But is it feasible to use liability to discourage infections? Yes, if we can satisfy two conditions: (1) most people are actually able to pay for damages if they are successfully sued for infecting others, and (2) enough of those who infect others are actually and successfully sued, and so made to pay.

On the first condition, ensuring that people can pay damages if they are found guilty, it is sufficient to require people who mix with others to buy infection liability insurance, similar to how we now require car drivers to get accident liability insurance. That is, to get a “pandemic passport” to excuse you from a strong lockdown, you must get an insurance company to guarantee that you will pay damages if you are shown to have infected someone. In a sense they “vouch” for you, and so are your “voucher”. The more types of voucher-client contract terms we are willing to enforce, the more levers vouchers gain to reduce risks.

The premiums for such insurance will be low if you can convince a voucher that you have already recovered from the virus, and so are relatively immune, or that you will leave your lockdown only rarely, to safe destinations. Otherwise, a voucher may require you to install an app on your phone to track your movements, or they may spot check your claims that you have sufficient supply of good masks that you use reliably when you leave home.

Okay, but what about the second condition, that enough infectors are actually made to pay? For this we need enough data to be collected on both sides, the infector and the infected, so that one can frequently enough match the two, to conclude that this person likely infected that one at this location at this time.

Now, we don’t need to be always absolutely sure of who infected who. In ordinary civil trials, the standard is a “preponderance of the evidence”; courts need only be 51% or more sure to convict the defendant. And sometimes we add on extra “punitive” damages, up to four ties as large as basic damages, often to compensate for a lower chance of catching offenders. So if we can find evidence to convince a court at the 51% or better standard for only one fifth of offenders, but we can add four times punitive damages, then offenders who do not know if they will be caught still expect to on average to pay near the basic damage amount.

Okay, but we still need to collect enough info to see who infected who at least one fifth of the time. Is this feasible? Well it is clearly quite feasible early in a pandemic, when few have been infected. Early on, if the times and places, i.e., space-time path, consistent with you being infected then and there overlap with the space-time path when someone else was likely infectious, then it was most likely their fault. This is the “contact trace” process usually recommended by public health workers early in a pandemic.

The problem gets harder later in a pandemic, when your infected path may overlap with the infectious paths of many others. Here it might be possible to use info on which virus strain you and they had to narrow the field. But even so there may still be several consistent candidates. In this case it seems reasonable to divide the liability over all of them, perhaps in proportion to the size of the path overlap. For the purpose of creating incentives to avoid infecting others, it isn’t that important to know later who exactly infected who when.

But yes, we still need info on who was infected and infectious where and when, perhaps supplemented by data on who had what virus strains. How can we get this info? People who might get infected have incentives to collect info on their path, to help them sue if infected. But people who might infect others would seem to want to erase such info, to keep them from being sued. I’ve recently outlined a more general approach to induce the collection of info sufficiently likely to be useful in later lawsuits. But for this essay, I’ll just propose that collecting key info be another condition required to get a pandemic passport, with violations punished by fines also guaranteed by your voucher.

Let me also note that yes, legal liability doesn’t work to discourage harms if typical harms get so small that people wouldn’t bother to sue to recover damages. In this case we could use a random lottery approach to dramatically lower the average cost of suing.

So let’s put this all together. You must stay at home, locked down, unless you get a “pandemic passport”, in which case you can go where you want when, to meet anyone. To get such a passport, you must get someone to vouch for you. They guarantee that you will pay should someone successfully sue you for infecting them, if you agree to their terms of premiums, behavior, monitoring, punishment, and co-liability. Defendants who pay damages may have to pay extra, to compensate for most infectors not getting caught in this way. When several infector candidates are consistent with the data, they can divide the damages. And for low damage levels, a random lottery approach can lower court costs.

To get and keep a passport, your voucher also guarantees that you will collect info that can help others to show that you infected them, but which can also help you to sue others if they infect you, and win you bounties via showing that others did not collect required info. For example, perhaps you must track your movements in space and time, regularly record some symptoms like body temperature, and also save regular spit samples. This info is available to be subpoenaed by those who can show sufficient reason to suspect that you infected them. Such info seems sufficient to catch enough infectors.

And by catching a sufficient fraction of infectors who then actually pay on average for the harms that they cause by infecting, (strict) legal liability can give sufficient incentives to individuals to avoid infecting others. If so, we don’t need crude lockdown regulations telling people what to do when and how; individuals can instead more flexibly adapt to details of their context in deciding when and where to work, shop, travel etc. Yes, voucher rules would not let them do such things as freely as they would in the absence of a pandemic. But behavior would be more free and impose lower economic costs than under crude regulations which similarly suppress the pandemic spread.

Note that today the most common form of legal liability is actually negligence, which we can see as a mixed form between simple regulation and simple strict liability. With negligence, the court judges if your behavior has been consistent with good behavior standards, which are essentially behavior regulations. But you are only punished for violating these regulations in situations where your behavior contributed to the harm of a particular person. Today courts tend to limit strict liability to cases where courts find it hard to define or observe good behavior details, such as using explosives, keeping a pet tiger, or making complex product design choices. As courts find it harder to define and observe good behavior in a new pandemic, strict liability seems better suited to this case.

Note also that none of this requires employers to be liable for their infected employees. Someone who is sued for infecting others may turn around and blame their employer for pushing them into situations that cause them to infect others. Employer-employee contract could usefully address such issues.

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Why Does Govt Do Stuff?

Looking across the many different activities and sectors of society, how well can we predict where governments get more vs. less involved?

Though this is an oft discussed topic, I can’t recall seeing an overall theory summary. So I thought I’d write one up. Here are some big relevant factors, and areas they may explain. Most are tentative; you may well convince me to move/change/add them.

Control – Whomever runs the government prefers to control areas that can be used to prevent and resist opposition and rivals.
Predicts more: religion, military, police, law, news, schools, disaster response, electricity, energy, banking.

Scale – If supplying a product or service has strong economies of scale, network, or coordination, it can be cheaper to use one integrated organization, who if private may demand excessive prices and thereby threaten control.
Predicts more: military, “roads” (including air, boat travel support), social media, money, language, electricity, telecom, water, sewer, trash, parks, fire, software, fashion, prestige
Predicts less: housing, food, medicine, art, entertainment, news, police, jail.

Innovation – As governments seem less able to encourage or accommodate effective innovation, governments tend to be less involved in rapidly evolving sectors.
Predicts more: roads, water, sewer, track, parks.
Predicts less: military hardware, vehicles, tech/computers, entertainment, social networks.

Variety – Governments tend to encourage and be better at relatively standardized products and services, done with fewer versions, more the same for everyone everywhere at all times.
Predicts more: war, medicine, schools, disaster response, roads.
Predicts less: housing, food, entertainment, romance, parenting, friendship, humor.

Norms – Norms are shared, and we like to enforce them together, officially.
Predicts more: religion, law, war, romance, parenting, medicine, drugs, gambling, slavery, language, manners, sports.

Show Unity – As we want to show that we are together, and care about each other, we like to do the things we to do to show such care together in a unified way.
Predicts more: religion, poverty/unemployment/health insurance, school, medicine, fire, parks, housing, food, disaster response, trash/sewer, coverage expansion subsidies.

Show Off – We want to impress outsiders with our tastes, abilities.
Predicts more: research, schools, high art, high sport, roads, parks, shared space architecture, trash/sewer.
Predicts less: low art/entertainment, low sport, gossip.

Hypocrisy – When we profess some motives, but others are stronger, the opacity and slack of government agencies, and better ability to suppress critiques, makes them better able to hide such differences.
Predicts more: medicine, drugs, gambling, schools, police, jail, courts, romance, zoning, building codes, war, banking.
Predicts less: water, sewers, electricity.

If we could collect even crude stats on how often or far govt is involved in each area, and crudely rate each area-factor combo for how strongly that factor applies to that area, we could do a more formal analysis of which of factors predict better where.

Note that scale is the strongest factor suggesting that govt does more when more govt helps more. Innovation and variety suggest that also when those factors are the cause of govt involvement, but much less so if those features are the result. While norms are on average valuable, it is much less clear when govt support improves them. Most signaling likely helps each society that does it, but is done too much for the good of the world overall.

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Subpoena Futures

A subpoena … is a writ issued by a government agency, most often a court, to compel testimony by a witness or production of evidence under a penalty for failure. … party being subpoenaed has the right to object to the issuance of the subpoena, if it is for an improper purpose, such as subpoenaing records that have no relevance to the proceedings, or subpoenaing persons who would have no evidence to present, or subpoenaing records or testimony that is confidential or privileged. (More)

Parties may obtain discovery regarding any nonprivileged matter that is relevant to any party’s claim or defense and proportional to the needs of the case, considering the importance of the issues at stake in the action, the amount in controversy, the parties’ relative access to relevant information, the parties’ resources, the importance of the discovery in resolving the issues, and whether the burden or expense of the proposed discovery outweighs its likely benefit. (More)

Exceptions are quite limited: self-incrimination, illegally-obtained info, and privileges of spouses, priests, doctors, diplomats, and lawyers. The remarkable fact is that the law has little general respect for privacy. Unless you can invoke one of these specific privileges, you must publicly report to the court any info that it thinks sufficiently relevant to a current court case. You simply have no general right to or expectation of privacy re stuff a court wants to know. Courts don’t even compensate you for your costs to collect evidence or appear to testify.

And yet. Consider what I wrote March 5:

The straightforward legal remedy for [pandemic] 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. … most folks just can’t pay large legal debts.
The vouching system directly solves (2), … 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. (More)

Here, to help law deal with pandemics, I was tempted to propose specific rules re info that people must collect and preserve. Yet if courts can get any info they think relevant, why is there ever a problem with courts lacking info to deal with key harms, such as pandemic infection?

The answer is that current law allows a huge exception to its subpoena power. Courts can force you to reveal info that you have already collected, on paper, a computer, in your head, or in your physical objects. But you usually have no obligation to collect and save info now that the court might want later. As a result, many people and orgs go out of their way to not save incriminating info. For example, firms do key discussions verbally, not recorded, rather than via email. Thus you have no obligation to save spit samples or detailed records of where your phone goes, to help with future pandemic infection lawsuits.

This seems a huge and inconsistent loophole. I could understand if the law wanted to respect a more general right to privacy. Then the court might weigh the value of some info in helping court cases against the social harm from forcing its publication via a subpoena. As a result, it might sometimes block a subpoena even when the info collected would be relevant to a court case.

But I can’t see a reason to eagerly insist on access to info that seems relevant to a court case, and yet put no effort into inducing people to collect and preserve such info beforehand. So I propose that we create a legal process by which legal judgements are made on, if collected and saved, how likely some info would be to be subpoenaed, and how valuable it would be in that case.

When info would be valuable enough if collected and saved, then the court should require this. I don’t have a strong opinion on who exactly should bring a suit asking that such info be saved, or who should represent the many who would have to save that info. But one obvious system that occurs to me is to just have courts usually make ex post estimates of info value by the end of each court case, and then use “subpoena futures” prediction markets to make an estimate of that value ahead of time. (And make it legal and cheap to start such markets.)

So, if a subpoena futures market on a type of info estimates its expected court value to be above a standard threshold, then by law that info must be collected and saved. These prediction markets needn’t be huge in number, if they could estimate the average value of such info collect over a large group, which would then justify requiring that entire group collect the info. Such as everyone in an area who might infect others with a pandemic. If some subgroup wanted to claim that such info wasn’t less valuable regarding them, and so they should be excused, why they’d have to create different prediction markets to justify their different estimates.

For example, when a pandemic appears, if those who might infect others are likely vouched, then those who might be infected would want to require that first group to collect and save info that could be used later to prove who infected who. So they’d create prediction markets on the likely court value of spit samples and phone location records, and use market estimates to get courts to require the collection of that info.

Compared to my prior suggestion of just having the law directly require that such info be collected, this subpoena futures approach seems more flexible and general. What other harms that we do each other could be better addressed by lawsuits if we could require that relevant info be collected and saved?

(Btw, courts need not estimate info value in money terms. They might instead express the value of each piece of info in terms of its multiple of a “min info unit”, i.e., the value of info where they’d be just on the border of allowing it to be subpoenaed for a particular case.)

Added 7a: As mentioned in this comment, we now have this related legal concept:

Spoliation of evidence is the intentional, reckless, or negligent withholding, hiding, altering, fabricating, or destroying of evidence relevant to a legal proceeding …The spoliation inference is a negative evidentiary inference that a finder of fact can draw from a party’s destruction of a document or thing that is relevant to an ongoing or reasonably foreseeable civil or criminal proceeding.

My proposal can be seen as expanding this concept to allow a much weaker standard of “foreseeable”. And instead of allowing a presumption at trial, we just require the evidence to actually be collected.

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What Can Money Buy Directly?

Can money buy oranges? Well obviously, in an indirect sense. With money, you could travel to a place where you’ve heard oranges grow wild, search to find such a plant in the wild, dig it up and try to ship it home, see if it you can make it thrive there, and if it does, take some oranges as your reward. This might work, but success depends not just on the money you pay; it also depends much more on your effort, abilities, and other context. In principle, you might be able to execute this plan without any money, but typically more money will make such a plan a bit easier. So, yes, in this weak sense, you can “buy” oranges with money.

At an ordinary grocery store, however, you can buy oranges much more directly. You go to the produce section, look for the orange color, walk to the pile of oranges, take as many as you want, and pay the price per orange at the register. Or at a full service grocery, you might just say “six oranges please” and a grocer would go find and bag them for you. Online, you might just type in “orange”, enter “6” for quantity, and click “buy”.

These ways to buy oranges are usually pretty reliable even for an ordinary person who knows little about oranges. Using these methods, the number of oranges you get depends mainly on how much money you are willing to pay, and much less on other context. This is what I mean by buying something “directly.” And so regarding the oft-asked question “what can money buy?”, a more interesting version of this question is “What can money buy relatively directly.”

As more money makes most any plan a bit easier to achieve, the many long lists one can find of “things money can’t buy” are in one sense obviously wrong; money helps with most of them. And if they just mean that money can’t guarantee the max level of each thing, that’s obvious, but trivial, as pretty much nothing guarantees that. You can’t even guarantee you’ll get oranges if you order them from a grocery. And if that is the meaning, why pick on money, relative to anything else that might greatly but imperfectly help you get things?

Perhaps what people mean is that money isn’t the main factor that determines if you succeed with such things; money can be a distraction from more important issues. But if so, that seems to claim that you can’t buy such things directly. Which then raises the key question: for what kinds of things can the money you pay be a strong factor in determining how much of it you get? That is, what can money buy directly?

In my last post, I talked about how one can buy higher wages, via a job agent. I wasn’t saying that there are complex and subtle ways to spend money to help your career, ways that could work if only you were clever and skilled enough to understand and apply them. I was instead saying that there is a simple direct way to do this, one most anyone can understand: hire an agent (and anti-agent). That method doesn’t guarantee you any particular wage, but it does let you control how much you pay per wage increase.

In fact, I’ll go further now, and say that there seem to be ways to measure most anything, and as a result we can buy most any measured thing relatively simply and directly. That is, via a simple method that most anyone can come to understand, you can just point to what you want, put cash on the table, and then lose cash in proportion to how much you get of what you want. And the relation is substantially causal; paying more can cause you to get more, even when you have little relevant ability or understanding.

In the academic literature, this method is called an “incentive contract”. You find a way to measure the outcome you want, you offer to give someone access to levers by which they can plausibly influence this outcome, and you contract to pay them more cash the higher is this measure. You might also hold auctions or competitions to see who is best to put into this role.

We have a great many real examples today, and in history, of oft-used incentive contracts. Artists and athletes have agents paid a fraction of their earnings. Line workers are paid “piece rates” per how many items they assemble, or tomatoes they pick. Sales workers are paid commissions, per how many items they sell. Hedge fund managers are paid more if their fund makes higher returns. Lawyers on contingency fees are paid a fraction of court awarded damages. Firm managers are paid in stocks and options which rise in value when firm stock prices rise. Athletes are paid bonuses for individual and team success. Construction contractors are paid more if their work is completed by a deadline. Ships carrying convicts to Australia were paid on the number who arrived alive (which worked much better than the number who started out alive.)

Are the applications we’ve seen the only feasible ones, or could many more yet be developed? Consider beauty. Some say beauty can’t be measured, as it is “in the eye of the beholder”. But if you ask many people to rate someone’s beauty, their ratings are correlated. So imagine taking many standardized pictures and video of a client, across across their usual range of clothes and environments, and then paying many independent observers to rate their attractiveness. Do this at the start to get an initial value, and plan to do it again in, say, six months. A client might pay a beauty agent based on the change in this measure.

Potential beauty agents could bid by offering how much money they want to be paid per unit of increased beauty, how much they would pay up front to gain this role, and which particular beauty decisions they want to control, rather than merely advise, at least until the second measurement. There are probably clever ways to use auctions or decision markets to select from among these bids, but such details need not concern us now.

Yes, it would be a problem if a beauty agent could corrupt beauty measurements, or exploit their biases. But if such effects are modest, expert beauty agents can likely substantially increase a client’s beauty, relative to that client’s amateur efforts. Consider that movies don’t usually let actors pick their own clothes and hairstyle to look good in each movie; beauty experts instead make those choices. Yes, clients may care less about beauty as seen by average people, and more as seen by particular communities. But measuring such local versions of beauty should only cost a bit more.

Now consider happiness. If happiness were an entirely internal mental state that never influenced our external appearances, well then yes it would be hard to measure happiness. At least until we can better read brains. But most humans leak their feelings in many ways. So a 24/7 audio/video feed of a person, especially their facial expression and tone of voice, perhaps augmented by watch-based measures of heart rates, etc., seems plenty sufficient. Especially if processed via self and other reports, rather than artificially. Happiness could be measured pretty accurately from such things, especially for a client who wants it to be measurable, so that they can hire an agent to increase their happiness. (And especially as things like smiles and laughter probably evolved to signal happy internal states.)

A happiness agent is given control over some elements of a client’s life, and can advise on others. Especially on which other agents to hire for beauty, health, career, etc. Happiness agents pay some initial fee to gain this role, and then they are paid in proportion to the client’s measured happiness. Such agents might be big firms that combine many kinds of happiness expertise, and who can take big risks. If there are things that an expert can learn about how to be happy, things an ordinary amateur doesn’t know, then there is likely substantial scope for using agents to directly buy happiness. If so, money can buy happiness, directly.

Well this is enough for one blog post. The key conclusion: it looks feasible to much more directly buy many things we care greatly about, including beauty, happiness, health, career success, popularity, and status. Yes it would be work to set up systems to measure such things, work that could not be recouped for just from one client. But the prospect of many millions of clients should be quite sufficient.

One key question remains: why hasn’t there been more interest in such possibilities? Are these new innovations that could spread widely, or are they blocked by key fundamental permanent obstacles not yet considered in the above discussion?

Added 20Apr: Most seem to actually be comforted by the fact that it can be hard to buy things with money, and seem uninterested in finding ways to make it easier to buy things with money. I suspect they feel that better methods of this sort would give a relative advantage to people with more money, who they see as other people. While everyone could benefit from better ways to buy things with money, that matters little to those focused on relative status.

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Why Not More Job Agents?

Professional agents take substantial fractions of client wages: 3-10% in sports,10-15% in music, 10-20% in acting, and 15% for writers. Somewhat relatedly, job recruiters take 10-30% of a first year salary, and home realtors take 2-3% of home sales.

The logic is simple: you might be good at your job, but you can’t be the best at everything related to your career. There is room to be helped by people or organizations who specialize in advising, presenting, evaluating, matching, and networking for workers with your sort of career. Such help can be useful not only when you seek to change jobs, or get promoted within an organization, but at all points in your career. Even at the start, when you are deciding where to get trained in what.

The agency relation works smoothest with a clear division of labor; your tasks and their tasks. But there is also room for collaboration on shared tasks. Such as by their giving you advice on choices that are ultimately up to you.

The best agents are usually paid via an “incentive contact”, wherein they get paid a fraction of what clients get paid. This seems a big improvement on how we usually pay tutors, advisors, mentors, personal coaches, or inclined-to-advise friends and family. When you instead pay someone by the hour, or by favors traded, their interests are less clearly aligned with yours. Yes, you might judge them based on reputations or track records. But track records are rarely visible, and reputations are often only loosely related to help. You may not be much better at judging if their help and advice is good than you would be at just trying to do those things yourself.

In contrast, paying your agent a fraction of your earnings more clearly aligns their interests with yours, and also makes it easier to choose an agent. By agreeing to be your agent, someone credibly signals confidence in their and your abilities, and that you can work together. This is similar to how most lawyers will happily take your case if you pay them by the hour, but will be much picker if you ask them to be paid a contingency fee (i.e., % of the verdict). Also, all else equal, the lower the fraction of earnings they will take to do the same tasks, the higher their estimate of your earnings given their help.

While most people informally collect some career advisors and mentors, it seems something of a puzzle that more people don’t have career agents. You might claim that agents just can’t help most careers, but that seems just wrong. Maybe they don’t help a lot, but surely they could charge a little do a little. You might note that most of us have goals other than making money, but that is also true for most who have agents; the incentive contract method doesn’t need to be perfect, it just needs to be better than other methods, such as paying by the hour.

Yes, if your career is very risky, with a small chance of huge success, then being your agent is risky too. But each agent can have many clients, and agents can band together into larger firms to spread risk. As a result, risk-aversion need not greatly limit agent incentive contracts.

Now perhaps you object that an agent just couldn’t help enough to deserve 10%, or even 5%, of most salaries. But you can use an initial signing fee to separate an agent’s incentive from their net compensation. For example, assume that your and your agent’s fractional incentives must add to 100%, and that for incentive purposes the most efficient fractions are 70% for you and 30% for your agent. But also assume that the cost to an agent to put in that optimal effort is equivalent to only 10% of your salary. In this case, a better contract is for the agent to pay you a 20% up front “signing fee” for the right to be your agent and then later get 30% of your earnings. In this way your agent will on net get paid 10% of your earnings, and yet have a 30% stake in your income to give them a strong incentives.

Yes, for short term contracts such incentives only make agents work hard in ways that can produce short term gains. And we might rightly be wary of committing early on to one agent for our whole life. A simple solution here is to have each new short-term agent pay their up front signing fee to your previous agent, instead of to you. In this way each of your agents has a long term incentive about you, even if they may not always stay your agent. Your first agent may then pay you an especially large signing fee, which you might use to help pay for early career education or training (or you could paying for those part of their tasks).

To avoid the problem that a stronger incentive for your agent comes at the cost of a weaker incentive for you, it is actually possible to have the sum of your and your agent’s wage fractions add up to more than 100%. All you have to do is find a third party “anti-agent” willing to accept the remaining incentive. For example, you could get 100% and your agent get 50% of your earnings, if your anti-agent takes a -50% stake. You’d pay your anti-agent an up-front signing fee, and then they’d later pay your agent 50% of your earnings, while you just kept your earnings.

The main problem with an anti-agent is that they’d have an incentive to hurt your career. So you’d want to make sure to pick anti-agents only from organizations who are set up so that it is hard for them to hurt you. Perhaps (1) they are only your anti-agent for a short period, (2) you use cryptography so they don’t know who exactly you are, (3) they are headquartered far from where you live, and (4) they are just a financial holding firm, without employees able to do things to help the firm.

It wouldn’t be terrible to use auctions (or decision markets) to pick your agents and anti-agents, and their fees, from qualified candidates. For example, you might initially pick optimal agent and anti-agent fractions, and identities, via an initial auction for the max net singing fee given to you. You could probably use many criteria to define who is qualified, though your agents would be wary of your later using arbitrary conditions to extort the signing fees that agents were supposed to be paid. So you would have to agree on some limits to changes in qualification conditions.

If agents expect that you will may make choices that cut your earnings, they would likely pay more for agent contracts that put those choices within their sphere of control. Such as the ability to format your resume, or perhaps to veto job choices. So you’d want to think carefully about which choices agents get full control over, and which they can only advise you on. And current laws may limit these contracts in many ways.

Think about it this way: with a good initial auction to choose an agent, if the help of an agent isn’t actually on average worth its cost, then the winning bid should be someone who just pays you up front for the present financial value of a fraction of your future income. They won’t include an amount in their bid to cover costs to help you, as they don’t plan to try to help. If that’s the winner, accept them, and walk away wiser for knowing that you are better off without an agent trying to help you.

With all these options available to help set up a productive agency relation, I am honestly puzzled about why more people don’t seem interested in having agents. Especially as it tends to be the more prestigious and successful people today who have agents. Why don’t people get an agent, just to brag that they have one?

(Note that I’m not making any assumptions about how the roles of “agent” are organized or divided. They could be provided by individuals or by large firms, and could either be unified into one role or divided up into many differing roles.)

Added 19Apr: Many say they’d rather pay an agent a percentage of earnings over some reference earnings. But that’s mathematically equivalent to an agent who pays a signing fee and then gets a percentage of all earnings.

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Three Futures

Recently, 1539 people responded to this poll:

This pattern looks intriguingly bimodal. Are there in some sense two essentially different stories about the future? So I did a more detailed poll. Though only ~95 responded (thank you!), that is enough to reveal an apparently trimodal pattern:

Respondents were asked to estimate the number of future creatures that most people today would call “human”, and also the number who would likely call themselves “human”, even if we today might disagree. The y-axis here is in terms of log10 units. In those units, world population today is 9.89 and ~11.03 humans have ever lived. So the highest number here, 20, is larger than the square of the number alive today.

As you can see, respondents expect a lot more future creatures who call themselves “human”, relative to creatures we would call “human”. And substantial fractions seem to insist that these numbers are higher than any specific number you might mention (20 here). Among the rest, the most popular answer is the 11-12 range (i.e., 0.1-1 trillion “humans”). Note that this can’t be due to a belief that we face huge risks over the next few centuries; that belief suggests the answer <11.

When I set aside the highest (>20) response, and fit a mixture of two lognormals to the rest of each response distribution, I find that regarding creatures we would call “human”, 50.1% of weight goes to a median estimate of 11.9, with (in log10 units) a sigma variation of only 0.22 around that median, 39% of weight to an estimate 13.4, with a much larger sigma of 2.5, and 11% weight to >20, i.e., very high. Regarding creatures who call themselves “human”,  a 45% weight is on estimate 12.0 with sigma 1.2, a 30% weight is on estimate 16.6 with sigma 2.0, and 25% weight on >20. (Such a lognormal mix fit to the first 4 option poll gives roughly consistent results: medians of 11.6, 15.4 with 61% weight on the low estimate, when both are forced to have the same sigma of 0.75.)

Thus, responses seem to reflect either three discrete categories of future scenarios, or three styles of analysis:

  1. ~1/2 say there will only ever be ~10x as many humans as there have been (~100x as many as living now), most all creatures who we’d call “human”. Then it all ends.
  2. ~1/4 say our descendants go on to much larger but still limited populations. There are ~300x as many humans as have ever lived, and ~1000x that many weirder creatures, though estimates here range quite widely, over ~4 factors of 10 (i.e., “orders of magnitude”).
  3. ~1/4 say our descendants grow much more, beyond squaring the number who have ever lived. Probably far far beyond. But ~1/2 of these expect that few of these creatures will be ones most of us would call “human”.

The big question: does this trimodal distribution result from a real discreteness in our actual futures and the risks we will face there, or does it mostly reflect different psychological stances toward the future?

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A Perfect Storm of Inflexibility

Most biological species specialize for particular ecological niches. But some species are generalists, “specializing” in doing acceptably well in a wider range of niches, and thus also in rapidly changing niches. Generalist species tend to be more successful at generating descendant species. Humans are such a generalist species, in part via our unusual intelligence.

Today, firms in rapidly changing environments focus more on generality and flexibility. For example, CEO Andy Grove focused on making Intel flexible:

In Only the Paranoid Survive, Grove reveals his strategy for measuring the nightmare moment every leader dreads–when massive change occurs and a company must, virtually overnight, adapt or fall by the wayside–in a new way.

A focus on flexibility is part of why tech firms tend more often to colonize other industries today, rather than vice versa.

War is an environment that especially rewards generality and flexibility. “No plan survives contact with the enemy,” they say. Militaries often lose by preparing too well for the last war, and not adapting flexibly enough to new context. We usually pay extra for military equipment that can function in a wider range of environments, and train soldiers for a wider range of scenarios than we train most workers.

Centralized control has many costs, but one of its benefits is that it promotes rapid thoughtful coordination. Which is why most wars are run from a center.

Familiar social institutions tend to be run by those who have run parts of them well recently. As a result, long periods of peace and stability tend to promote specialists, who have learned well how to win within a relatively narrow range of situations. And those people tend to change our rules and habits to suit themselves.

Thus rule and habit changes tend to improve performance for rulers and their allies within the usual situations, often at the expense of flexibility for a wider range of situations. As a result, long periods of peace and stability tend to produce fragility, making us more vulnerable to big sudden changes. This is in part why software rots, and why institutions rot as well. (Generality is also often just more expensive.)

Through most of the farming era, war was the main driver pushing generality and flexibility. Societies that became too specialized and fragile lost the next big war, and were replaced by more flexible competitors. Revolutions and pandemics also contributed.

As the West has been peaceful and stable for a long time now, alas we must expect that our institutions and culture have been becoming more fragile, and more vulnerable to big unexpected crises. Such as this current pandemic. And in fact the East, which has been adapting to a lot more changes over the last few decades, including similar pandemics, has been more flexible, and is doing better. Being more authoritarian and communitarian also helps, as it tends to help in war-like times.

In addition to these two considerations, longer peace/stability and more democracy, we have two more reasons to expect problems with inflexibility in this crisis. The first is that medical experts tend to think less generally. To put it bluntly, most are bad at abstraction. I first noticed this when I was a RWJF social science health policy scholar, and under an exchange program I went to the RWJF medical science health policy scholar conference.

Biomed scholars are amazing in managing enormous masses of details, and bringing up just the right examples for any one situation. But most find it hard to think about probabilities, cost-benefit tradeoffs, etc. In my standard talk on my book Age of Em, I show this graph of the main academic fields, highlighting the fields I’ve studied:

Academia is a ring of fields where all the abstract ones are on one side, far from the detail-oriented biomed fields on the other side. (I’m good at and love abstractions, but have have limited tolerance or ability for mastering masses of details.) So to the extent pandemic policy is driven by biomed academics, don’t expect it to be very flexible or abstractly reasoned. And my personal observation is that, of the people I’ve seen who have had insightful things to say recently about this pandemic, most are relatively flexible and abstract polymaths and generalists, not lost-in-the-weeds biomed experts.

The other reason to expect a problem with flexibility in responding to this pandemic is: many of the most interesting solutions seem blocked by ethics-driven medical regulations. As communities have strong needs to share ethical norms, and most people aren’t very good at abstraction, ethical norms tend to be expressed relatively concretely. Which makes it hard to change them when circumstances change rapidly. Furthermore we actually tend to punish the exceptional people who reason more abstractly about ethics, as we don’t trust them to have the right feelings.

Now humans do seem to have a special wartime ethics, which is more abstract and flexible. But we are quite reluctant to invoke that without war, even if millions seem likely to die in a pandemic. If billions seemed likely to die, maybe we would. We instead seem inclined to invoke the familiar medical ethics norm of “pay any cost to save lives”, which has pushed us into apparently endless and terribly expensive lockdowns, which may well end up doing more damage than the virus. And which may not actually prevent most from getting infected, leading to a near worst possible outcome. In which we would pay a terrible cost for our med ethics inflexibility.

When a sudden crisis appears, I suspect that generalists tend to know that this is a potential time for them to shine, and many of them put much effort into seeing if they can win respect by using their generality to help. But I expect that the usual rulers and experts, who have specialized in the usual ways of doing things, are well aware of this possibility, and try all the harder to close ranks, shutting out generalists. And much of the public seems inclined to support them. In the last few weeks, I’ve heard far more people say “don’t speak on pandemic policy this unless you have a biomed Ph.D”, than I’ve ever in my lifetime heard people say “don’t speak on econ policy without an econ Ph.D.” (And the study of pandemics is obviously a combination of medical and social science topics; social scientists have much relevant expertise.)

The most likely scenario is that we will muddle through without actually learning to be more flexible and reason more generally; the usual experts and rulers will maintain control, and insist on all the usual rules and habits, even if they don’t work well in this situation. There are enough other things and people to blame that our inflexibility won’t get the blame it should.

But there are some more extreme scenarios here where things get very bad, and then some people somewhere are seen to win by thinking and acting more generally and flexibly. In those scenarios, maybe we do learn some key lessons, and maybe some polymath generalists do gain some well-deserved glory. Scenarios where this perfect storm of inflexibility washes away some of our long-ossified systems. A dark cloud’s silver lining.

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Covid19 Pizza-Risk Estimator

To keep us from catching and spreading Covid19, most of us are on “lockdown”, limiting non-home contact. But we aren’t completely isolated; we all do a few things that risk outside contact. And households really are the better unit of risk here; if one of you gets sick, the rest face a much higher risk.

To help households estimate and manage risk, I’ve made the following table, listing risks for 19 activities, all relative to the first: accepting delivery of and eating a pizza, paid for online. These risk estimates came from ~1000 respondents to each of 18 Twitter polls. (Technically, these estimates are medians of lognormal distributions fitted to poll response frequencies.) Yes, it would be better to get expert estimates, but I don’t have experts to poll. I hope experts who see these will publicly improve on them. Until they tell us more, we must act on what we know.

The above is actually part of a screenshot from this spreadsheet (copy it to edit it) that I’ve made to help you estimate household risk. (Anyone know how to embed it here, so each reader can edit their own version here?) On this sheet, you can combine these risk estimates with estimates of how often per week your household does each activity, and also any corrections for how you do it differently, to get your total household weekly “Pizza Risk”. That is, how many weekly risks you take equivalent to a pizza delivered & eaten.

In the spreadsheet, each row lists a risky activity, grouped into types. To use the sheet, consider the activity in each row and think of similar activities you do that risk outside contact. For each such activity, find the closest activity in the table, and for that row, enter how many times per week your household does that activity in the “Count” column. And if your activity seems to have a different risk from other households, such as because you do it for more or less time, or because it involves fewer or more outsiders, then enter a number other than 1 in the “Factor” column. For example, if doing it your way has twice the risk, enter the number 2.

If you mange to use this spreadsheet to get a Pizza Risk estimate, please complete the following two polls so we can learn about how Pizza Risk varies across households.

FYI, this post was up just 33 hours after I first tweeted the idea for this project.

Added 14Apr: Commentor Roman Kuksin did an explicit analysis of many of these risks, and finds a 0.74 correlation with the above risk estimates.

Added 18Apr: Here is another set of activity risk estimates: HT @diviacaroline.

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