Seeking Robust Credible Expertise Buyers

On Jan 19, 2000, I posted an email to the Extropians mailing list, giving the first public mention of the futarchy idea. (I also have a detailed PPT on the idea dated June 22, 2000, and the first pdf paper I posted is dated “July 2000.”) So the general idea is just over two decades old now.

Coincidentally, some new prediction platforms have been announced recently, and some have asked me why I do not act more excited about them. So this seems a good time to review my agenda.

I seek to jumpstart stable decision-advising info markets, wherein bias-robust widely-credible expertise is bought and sold. Let’s walk through these terms one at a time.

By jumpstart stable I mean that I’m seeking to start a new regular practice, not just proof-of-concept demonstrations of related technologies. I’m okay with some party subsidizing them at first, to help move to a new equilibria. But that sponsoring party either needs to stay indefinitely, or the market must soon find a way to pay its way without that subsidy. To become a regular practice, relevant parties need to see a long enough track record of how such info markets have worked and performed in their particular topic areas.

By decision-advising info I mean that my goal isn’t to add to or change general talk, gossip, and chatter, much of which is too vague to see what exactly it means, and most of which influences little outside the world of chatter. My goal is instead to influence real and important decisions, via better info. So I want to see info markets that sell clear, precise consensus estimates that can be understood in probability terms, so they can be fed into traditional decision analysis.

To better influence decisions, these estimates should also be as actionable as possible. That is, estimates should sit clearly close to actual decisions, so that decision-makers can see their relevance, and see how different estimates naturally lead to different decisions.

By bought and sold, I mean that we need two kinds of participants, buyers and sellers. While there will sometimes be an overlap, in general the people who know things, the info sellers, just aren’t the same as the people who want to know things, the info buyers. And we can’t presume that the sellers will sell info for free. Instead, buyers must offer sufficient rewards to distract sellers from alternate activities.

By markets I mean to integrate these new systems with our many other markets in our mostly market economy. This isn’t a world apart. Most individuals and organizations in our society should be free to participate, if they so choose, as either buyers or sellers of info. And we should expect money to be the usual currency used to make deals.

By expertise, I mean that estimates should be accurate, due to embodying more information. While we must accept that there will be error, i.e., differences between estimates and truth, but on average errors should be minimized. More precisely, for each topic on which the markets offer an estimate, I want that estimate to be as accurate as possible given the costs paid for it. And it should usually be possible to pay more to get more accuracy.

By credible, I mean that estimates need to not just be accurate, but also to seem accurate to key audiences. And by widely I mean credible not just to a few audiences, but to many audiences. There should be a widely held common belief in their accuracy. For the set of topics to which they are said to apply, and holding constant the cost spend, these estimates need not usually seem more accurate than other key sources, but they should rarely seem to be much less accurate.

So I’m not just trying to create a tool that some people will see as useful, if they have certain compatible abilities and attitudes, and after they’ve practiced with it and developed a personal style of usage. Not just a private advisor who might happen to be trusted by a particular decision maker. I’m instead looking for an institution that many people with different goals and agendas can share, and trust together. That is, I seek the most accurate institution that many can share, even if some Individuals think they know of better sources.

For example, the accuracy of estimates shouldn’t depend greatly on the quality of management by key central administrators. Unless most everyone can agree on a reliable way to achieve high management quality, it just isn’t enough to have some people believe in a high quality of current management, if many others are skeptical. If any parts of these markets require central management, we need ways to pick managers that which don’t require unusual and unshared confidence in particular administrators.

The key attraction of widely credible info markets is that they can be used by decision makers who seek not just to make good decisions, but also to convince key audiences that they have made good decisions. And this can help us all to more easily trust agents who make decisions on our behalf. By checking that decisions made match the estimates from related info markets, we can check on decision makers. Or if market estimates can be make directly relevant and actionable enough, we might must put them directly in charge of key decisions.

By robust, I indicate that I want estimate accuracy to be high not just sometimes, but across a wide range of topics and information contexts. And by bias-robust I mean that I want estimates that are robust to situations where many parties would like to bias and distort the estimates, consciously or unconsciously, to influence decision makers. It is no good having something that works well in the lab, or on small unimportant topics, but falls apart when the stakes get high. To be a shared institution on important topics for parties with differing goals and agendas, we need a wide perception that accuracy persists even when many parties seek to distort and manipulate the estimates.

Okay, now that I’ve explained what I want, I can better explain when I get excited.

In the last few decades, dozens of groups have written new software to support info markets of varying forms. Such software is almost always tied to a particular project, and when that project fails the software almost never becomes available for other projects. And most of these groups see software and management as the only project parts worth paying for, in cash, stock, etc. Other parts are left as an exercise for to-be-determined “users”. So I find it hard to get excited about software unless it is tied to an exciting further project. Even software that comes with new features.

Sometimes sponsors are found to help pay to collect a set of regular users (i.e., info sellers) who talk on a set of regular topics. Sometimes it is the users themselves who are the sponsors, willing to pay in time and money to express their opinions on topics of interest to them. But rarely do such projects put much effort into soliciting participation and support from particular info buyers, choosing topics close to their key decisions. And, alas, the rare projects that at least pitch to potential info buyers tend to pick system designs sensitive to management quality, and less clearly robust to manipulation efforts.

Yet to my mind it is the info buyers who should come first in info market project planning. Info sellers are second, and software last. First find a set of estimates that would be useful in advising some set of important decisions. Especially where there’s a plausible trust advantage from widely-credible estimates, so that key audiences can better trust decision makers. Find parties to whom more credible accuracy would be valuable, and ask them how much they’d be willing to pay for it. They don’t need to be convinced of such accuracy in the start, but they do need to be willing to pay once sufficient accuracy is demonstrated. If you can’t find info buyers, you can’t make info markets.

Yes, when many potential info buyers want similar info, they can each be tempted to free ride on the efforts of others. So it makes sense to look more to cases where info gains are concentrated in a few parties. Alas, an even larger obstacle to finding info buyers is that we often justify our activities in terms of info collection and processing, when those activities are better described as local politics. We pretend to want accurate info far more often than we actually do.

I’m quite willing to work with most any group that seems to have at least a chance of putting together all the needed parts. But my best guess for the most promising project is still the one I first posted on over 24 years ago: fire the CEO markets re the Fortune 500. I doubt I have another 24 years, so I do hope someone tries this before then. For this project the plausible info buyers are firm investors, represented by the board of directors, who subsidize these markets. Likely info sellers are stock analysts and stock traders, who would profit from trading in these markets.

Simple money-based conditionally-called-off stock markets should produce bias-robust widely-credible estimates, at least if trading liquidity is high enough. That has been a widely shared belief on speculative financial markets for many decades. To get high liquidity, use large market-maker based subsidies on only a few firms to start with, firms chosen via a prize system as most likely to see fire-CEO recommendations. Once these prices get enough attention, especially from CEOs trying to manipulate them to make themselves look good, their liquidity can be self-reinforcing, and subsidies can be transferred to the next set of firms.

Yes, this fire-the-CEO project faces substantial legal obstacles if anyone is allowed to participate; may have to do this one offshore. Legal issues are much less of a problem for most projects that ask firm employees and contractors to advise firm decisions, as the firm can pay for their initial stake. For those projects the main obstacle is political disruption; existing players in the firm tend to be bothered to see their advice contradicted by a system with higher proven accuracy.

Of course I can get excited by a great many other project concepts; I’ve posted on many here over the years. But to get excited about an info market concept, I need to at least hear about the intended info buyers willing to pay to get bias-robust widely-credible expertise. A mere project to develop software, or even to collect a regular set of users, not so much.

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Problem-Owners Tolerate Risk

In theory, both risk aversion and value of life year relative to income are mainly set by utility function concavity. However, as both also show puzzlingly large empirical variation, there is apparently a lot more going on than a simple context independent utility function. But what?

While pondering this question, I saw some nature videos of cute young mammals, who start out as physically and emotionally fragile, and prone to crying, but who grow to be tough and emotionally steady like their parents. I wondered: might it be useful to frame this as a transition from risk-aversion to risk-tolerance?

Of course youngsters aren’t always risk averse. Sometimes they try to play with a scorpion, or rough-house too close to a cliff. In such cases, mom may restrain their risk-taking. But when kids suffer some mild depredation, such as wanting food or other help from mom, that’s when they act most emotionally needly and fragile:

Oh my, this problem I face is way too hard for my fragile emotions. I’m young, innocent, and nearly dying of fright to think about it. Won’t someone who loves me come to protect me from this terrible anxiety and suffering?

Plausibly parents are built to find it hard to resist wanting to help such puppy-dog eyes, to get them to take care of their kids. And when parents do tend to help when kids suffer, that actually makes kids more risk-tolerant regarding choices to take precautions to prevent future suffering.

Humans are famously neotenous, retaining more childlike features further into adulthood. And living in large social groups that share food and other resources, even human adults have incentives to show puppy-dog eyes, and to feel sympathetic to those eyes when they have something to share. So it seems plausible that human nature would have adapted the fragile-tough child-adult dynamic to apply to the helpee-helper relations in groups of adults.

That is, I’m suggesting that evolution has built we humans to strategically (if unconsciously) make a key attitude choice regarding each particular situation we face: do we project a cool tough self-reliant risk-tolerance, or a more emotionally-expressive risk-averse fragile vulnerability. In other words, do we choose to act vulnerable but sympathetic, or do we choose to act more “emotionally mature”? I’m not saying this is the only factor that influences risk-tolerance, but it may be one of the largest.

Yes, people often talk as if emotional maturity is the product of learning from experience, with perhaps some help from an admirable moral will. But if emotional maturity were always the better strategy, why would evolution have ever encoded in us any other tendency? Evolution could have made the very young take on emotionally mature attitudes if it had wanted. And in fact, it does sometimes want that, such as when kids “grow up too fast” due to getting little help in taking on adult-sized problems.

The key maturity choice here is of whether to “own” each problem that we face. It makes sense to own a problem, and act more risk-tolerant toward it (and more risk-averse re preventing it), when we seek to impress others with our confidence in handling the problem, when we bid for parent/leader roles, and when we want to avoid the embarrassment of others seeing that no one comes when we ask for help. However, when getting help with our problem seems more important, and likely enough, or when showing submission to and dependence on others seems important, it can make more sense to try to get others to own our problem by acting more risk-averse and hurt by the problem. We can suggest that others are to blame for causing our problem, and even if not they are responsible to help fix it.

So does my theory fit the data? In one key study, “Psychosocial maturity proved a better predictor of risk-taking behaviour than age.” Which is striking because age (at least below age 65) is one of our two usual best predictors of risk-tolerance, the other being male gender. Some data suggests that the following groups are also more risk-tolerant: the tall, the married, and those with more kids. People who work in finance, insurance, and real estate seem more risk-averse, and buying insurance on a risk is a strong sign that one is averse to it. Those with mid-level wages and extreme high or low wealth also seem more risk tolerant. The self-employed seem more risk averse.

Emotional maturity tends to increase on average with age, range of experiences, intelligence, self-esteem, and work performance, positive attitudes toward childcare, and not being an orphan. Here are some descriptions of the concept:

An emotionally mature individual gives off a sense of ‘calm amid the storm.’ They’re the ones we look to when going through a difficult time because they perform well under stress. … When you’re less mature, the world is full of minor annoyances, and you’re unaware of your own privileges. Think about how often a day you complain about others or different situations. (more)

Accept[s] the sorrows of life whole heartedly and … show[s] distress when there is occasion to be worried, without feeling a requisite to use a false facade of bravery. (more)

If my theory is right, and much of the variation in risk aversion (and value of life) we see results from strategic context-dependent choices to act risk-tolerant or risk-averse, this makes it harder to used measured risk-aversion (and value of life) to inform policy. Yes, if true risk aversion were higher, that would justify paying more to save lives, including via stricter regulation, and also justify more redistribution and social insurance. But if much of what we see people do is done for show, then we have to try to infer the real level of risk aversion behind all that show.

My guess is that on average in a social species with lots of sharing, free-riding is a bigger problem than excess autonomy. If so, we more often try to seem more needy to gain more help, than we try to seem less needy to gain respect. And thus typical behavior will exaggerate our real overall degree of risk aversion (and value of life). But I don’t yet know how to show this. It does seem worth further study; we may well figure out some way to see.

Added 3p: Related datum: “women are more sensitive to pain and less tolerant of pain than men.”

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Risk-Aversion Sets Life Value

Many pandemic cost-benefit analyses estimate larger containment benefits than did I, mainly due to larger costs for each life lost. Surprised to see this, I’ve been reviewing the value of life literature. The key question: how much money (or resources) should you, or we, be willing to pay to gain more life? Here are five increasingly sophisticated views:

  1. Infinite – Pay any price for any chance to save any human life.
  2. Value Per Life – $ value per human life saved.
  3. Quality Adjusted Life Year (QALY) – $ value per life year saved, adjusted for quality.
  4. Life Year To Income Ratio – Value ratio between a year of life and a year of income.
  5. Risk Aversion – Life to income ratio comes from elasticity of utility w.r.t. income.

The first view, of infinite value, is the simplest. If you imagine someone putting a gun to your head, you might imagine paying any dollar price to not be shot. There are popular sayings to this effect, and many even call this a fundamental moral norm, punishing those who visibly violate it. For example, a hospital administrator who could save a boy’s life, but at great expense, is seen as evil and deserving of punishment, if he doesn’t save the boy. But he is seen as almost as evil if he does save the boy, but thinks about his choice for a while.

Which shows just how hypocritical and selective our norm enforcement can be, as we all make frequent choices that express a finite values on life. Every time we don’t pay all possible costs to use the absolutely safest products and processes because they cost more in terms of time, money, or quality of output, we reveal that we do not put infinite value on life.

The second view, where we put a specific dollar value on each life, has long been shunned by officials, who deny they do any such thing, even though they in effect do. Juries have awarded big claims against firms that explicitly used value of life calculations to not to adopt safety features, even when they used high values of life. Yet it is easy to show that we can have both more money and save more lives if we are more consistent about the price we pay for lives in the many different death-risk-versus-cost choices that we make.

Studies that estimate the monetary price we are willing to pay to save a life have long shown puzzlingly great variation across individuals and contexts. Perhaps in part because the topic is politically charged. Those who seek to justify higher safety spending, stronger regulations, or larger court damages re medicine, food, environmental, or job accidents tend to want higher estimates, while those who seek to justify less and weaker of such things tend to want lower estimates.

The third view says that the main reason to not die is to gain more years of life. We thus care less about deaths of older and sicker folks, who have shorter remaining lives if they are saved now from death. Older people are often upset to be thus less valued, and Congress put terms into the US ACA (Obamacare) medicine bill forbidding agencies from using life years saved to judge medical treatments. Those disabled and in pain can also be upset to have their life years valued less, due to lower quality, though discounting low-quality years is exactly how the calculus says that it is good to prevent disability and pain, as well as death.

It can make sense to discount life years not only for disability, but also for distance in time. That is, saving you from dying now instead of a year from now can be worth more than saving you from dying 59 years from now, instead of 60 years from now. I haven’t seen studies which estimate how much we actually discount life years with time.

You can’t spend more to prevent death or disability than you have. There is thus a hard upper bound on how much you can be willing to pay for anything, even your life. So if you spend a substantial fraction of what you have for your life, your value of life must at least roughly scale with income, at least at the high or low end of the income spectrum. Which leads us to the fourth view listed above, that if you double your income, you double the monetary value you place on a QALY. Of course we aren’t talking about short-term income, which can vary a lot. More like a lifetime income, or the average long-term incomes of the many associates who may care about someone.

The fact that medical spending as a fraction of income tends to rise with income suggests that richer people place proportionally more value on their life. But in fact meta-analyses of the many studies on value of life seem to suggest that higher income people place proportionally less value on life. Often as low as value of life going as the square root of income.

Back in 1992, Lawrence Summers, then Chief Economist of the World Bank, got into trouble for approving a memo which suggested shipping pollution to poor nations, as lives lost there cost less. People were furious at this “moral premise”. So maybe studies done in poor nations are being slanted by the people there to get high values, to prove that their lives are worth just as much.

Empirical estimates of the value ratio of life relative to income still vary a lot. But a simple theoretical argument suggests that variation in this value is mostly due to variation in risk-aversion. Which is the fifth and last view listed above. Here’s a suggestive little formal model. (If you don’t like math, skip to the last two paragraphs.)

Assume life happens at discrete times t. Between each t and t+1, there is a probability p(et) of not dying, which is increasing in death prevention effort et. (To model time discounting, use δ*p here instead of p.) Thus from time t onward, expected lifespan is Lt = 1 + p(et)*Lt+1. Total value from time t onward is similarly given by Vt = u(ct) + p(et)*Vt+1, where utility u(ct) is increasing in that time’s consumption ct.

Consumption ct and effort et are constrained by budget B, so that ct + etB. If budget B and functions p(e) and u(c) are the same at all times t, then unique interior optimums of e and c are as well, and also L and V. Thus we have L = 1/(1-p), and V = u/(1-p) = u*L.

In this model, the life to income value ratio is the value of increasing Lt from L to L+x, divided by the value of increasing ct from c to c(1+x), for x small and some particular time t. That is:

(dL * dV/dL) / (dc * dV/dc) = xu / (x * c  * du/dc) = [ c * u’(c) / u(c) ]-1.

Which is just the inverse of the elasticity of with respect to c.

That non-linear (concave) shape of the utility function u(c) is also what produces risk-aversion. Note that (relative) risk aversion is usually defined as -c*u”(c)/u’(c), to be invariant under affine transformations of u and c. Here we don’t need such an invariance, as we have a clear zero level of c, the level at which u(c) = 0, so that one is indifferent between death and life with that consumption level.

So in this simple model, the life to income value ratio is just the inverse of the elasticity of the utility function. If elasticity is constant (as with power-law utility), then the life to income ratio is independent of income. A risk-neutral agent puts an equal value on a year of life and a year of income, while an agent with square root utility puts twice as much value on a year of life as a year of income. With no time discounting, the US EPA value of life of $10M corresponds to a life year worth over four times average US income, and thus to a power law utility function where the power is less than one quarter.

This reduction of the value of life to risk aversion (really concavity) helps us understand why the value of life varies so much over individuals and contexts, as we also see puzzlingly large variation and context dependence when we measure risk aversion. I’ll write more on that puzzle soon.

Added 23June: The above model applies directly to the case where, by being alive, one can earn budget B in each time period to spend in that period. This model can also apply to the case where one owns assets A, assets which when invested can grow from A to rA in one time period, and be gambled at fair odds on whether one dies. In this case the above model applies for B = A*(1-p/r).

Added 25June: I think the model gives the same result if we generalize it in the following way: Bt, and pt(et,ct) vary with time, but in a way so that optimal ct = c is constant in time, and dpt/ct = o at the actual values of ct,et.

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The Commanding Heights of Culture

In war, each side strives to control the “commanding heights”. These are places, usually elevated, where it is easier to defend, harder to attack, and especially valuable for helping your other military units. Hilltops, walls, bridges, towns, harbors, etc. With control of commanding heights, you might win even if you don’t have as many troops, tanks, ships, planes, etc.

In a fight between factions within an organization, each faction seeks to control the commanding heights of key positions. Such as CEO, board of directors, head of finance, head of marketing, etc. An alliance can use control of these positions to push its allies into other positions of power. In this way, a faction might take control of an organization, even if it comprises only a minority of organization members. 

In business, firms strive to control the commanding heights where there is more market power, reduced competition, and entry barriers. They seek places where it is harder for rivals to displace them. This can be due to switching costs, network effects, scale economies, customer loyalty, exclusive patents, or a natural monopoly of customers or workers. Society today generally looks suspiciously on such business advantages, seeking to limit them via culture, legal liability, and anti-trust regulation. That is, we seek to flatten such heights, and failing that we often control them via regulation or direct government management. Even so, a big % of wealth today comes from control of such commanding heights of business.

In larger political and cultural conflicts, different factions fight for control over larger social levers of influence. This includes core government positions of leadership. But in a democracy, those tend to be controlled by whomever can gain a majority of the popular vote. And as voters are ignorant and fickle, gaining them can be expensive and uncertain for factions. Yes, if you can get people with money to donate to your cause, you might use that to help attract voters. But as donors are also ignorant and fickle, you also compete with other political factions to attract donations, just as you compete for voters. 

Which is why cultural and political factions also seek other more secure bases – the commanding heights of culture. For example, with an independent judiciary, politicians may not directly control who are the new judges, or their choices may be highly constrained to be acceptable to current judges. In this case, once your political faction controls most judges, you can use that base of power to ensure that only folks who prove they are loyal to your faction become judges. Then your side can set laws and their interpretation to support your political and cultural agendas. Similarly, if your faction can control the schools, or the news media, then you can use those to spread your agenda. 

Now if control over such heights were simply owned and bought with money, then they would be commanding heights of business, but not of culture. For example, if there were a business monopoly that controlled all the media, then you could get it to teach everyone your agenda, but only if you paid it more for that coverage than did your rival political factions. Yes, you might persuade its owners to donate to your cause, via giving up some business revenue to help to your cause. But that’s just competing for donors again.

We get a similar effect if you can’t directly buy control over an area, but that area is still controlled for profit. For example, labor unions might be controlled by leaders seeking mainly to personally profit, on behalf of union members who expect to personally profit from union actions. In this case, a union will only choose to ally with outside groups, or to support their agendas, when those outside groups reciprocally support that union. These sort of unions can be part of an alliance, but they are not otherwise commanding heights of political power. 

So a central feature of the commanding heights of cultural conflict is that they are not bought with money, directly or indirectly. They are instead acquired via political conflict between groups demonstrating their political loyalty to a faction. Oh there may be for-profit firms involved, but those firms are not in full control; there are also professionals who can enforce their own standards. Maybe one of the reason that many do not like such areas to be bought completely with money is that they instead prefer them to be commanding heights, places from which factions can more securely influence society.

In business, you don’t make much net profits when you are in strong competition with rivals. You might then just barely stay in business, paying almost as much to your suppliers as you get from customers. Similarly in cultural conflict, a faction can’t gain much power and security if it is constantly competing for the allegiance of fickle voters and donors. A faction instead gains stable power, and thus profits, when it controls areas not via for-profit priorities, but via political loyalties. Pushing out those who don’t show sufficient allegiance to their political side, and then using that area to promote its agenda elsewhere. 

I see three reasons why a faction might be less eager to control an area of life in this way. One is that control there doesn’t let you push your agenda very much elsewhere. For example, it is harder to push larger cultural agendas via the construction process, relative to development policies of what is built where. And it is harder to promote a cultural agenda via control over the engineering of system back-ends and internals, relative to the design of features and policies that users see and use. 

A second reason to be less eager to control an area is when there is a strong competition for who does which roles how there. For example, if positions on sporting teams are chosen via fierce competitions of sport ability, there may remain too little slack to allow politically-aligned folks in that area to favor people from their side. Making it hard for a political faction to usefully control that area. It may be similar for musicians or actors. An area is only tempting to control if some key people there have enough slack and discretion to be able to favor choosing their political allies, even when those favorites are not quite as good or productive in the usual sense there.  

A third reason to be less eager to control an area is if people there have neutrality norms that say to not use dominance in one area to favor sides in larger political or cultural conflicts. For example, most Western militaries have such a norm. That is, internal factions may struggle for control of militaries, and they might even happen to correlate with larger political factions. But they are not to use control over the military to favor their side in the larger social world. Many parts of police and legal systems have also shared similar norms. Academia, law, and journalism also once had stronger neutrality norms, before the left came to dominate them more. 

Back in 2014 I wrote:

Jobs that lean conservative: soldier, police, doctor, religious worker, insurance broker. These seem to be jobs where there are rare big bad things that can go wrong, and you want workers who can help keep them from happening. That explanation can also makes some sense of these other conservative jobs: grader & sorter, electrical contractor, car dealer, trucker, coal miner, construction worker, gas service station worker, non-professor scientist. Conservatives are more focused on fear of bad things, and protecting against them.

Now consider some jobs that lean liberal: professor, journalist, artist, musician, author. Here you might see these jobs as having rare but big upsides. Maybe the focus is on small chances that a worker will cause a rare huge success. This is plausibly the opposite of a conservative focus on rare big losses.

But consider these other liberal jobs: psychiatrist, lawyer, teacher. Here the focus may just be on people who talk well. And that can also make sense of many of the previous list of liberal jobs. It might also makes sense of another big liberal job: civil servant.

So for a while now the left has controlled the commanding heights of academia, law, journalism, art, and civil service, while the right has controlled medicine, religion, military, police, insurance, construction, and engineering. Recently the left seems to have taken control of two areas previously controlled by the right: medicine, and social tech. This seems to have resulted from the left very strongly controlling elite colleges, the source of new elites in medicine and social tech. The recent academic trend toward dropping objective test scores from college admissions will allow more admissions discretion, which enables more political favoritism.

Not only does the right seem to be on the retreat re controlling commanding heights of culture, the areas that the right still controls seem less valuable as they are (1) more behind the scenes (engineering and construction), (2) more objectively competitive (e.g., sports), and (3) have stronger neutrality norms (e.g., military, police). Perhaps the right will reconsider its neutrality norms, if it takes recent history to suggest that the left will not continue them if it takes over such areas.

As I noted before, our society has tended to seek to shrink the commanding heights of business, via anti-trust policy. But we have no similar policies to shrink the commanding heights of areas like academia, law, etc. I’m not sure how anti-trust could work there, but it seems something worth considering. 

But more fundamentally, I’d prefer to shrink these commanding heights by reducing the slack there, via increased competition. The more that we could buy all these services via paying for results, they less we’d need to let these areas self-regulate, thereby creating fertile and attractive commanding heights for factions to control. In addition to getting more useful and effective teaching, medicine, law, etc., we’d also force cultural factions to more compete for our votes, donations, and allegiance.

P.S. I love to see a board game wherein factions compete to control such commanding heights of culture.

Added July 5: A new study on which sides have which jobs.

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Toward A University Department of Generalists

The hard problem then is how to get specialists to credit you for advancing their field when they don’t see you as a high status one of them. (more)

Many of my most beloved colleagues, and also I, are intellectual polymaths. That is, we have published in many different areas, and usefully integrated results from diverse areas. Academia tends to neglect integration and generality, which hurts not only intellectual progress, but also myself and my colleagues. Which makes me especially interested in fixing this problem.

The key problem is that academics and their research are mostly evaluated by those who work on very similar topics and methods. To the extent that these are evaluated by folks at a larger distance, it is by those who control one of the limited number of standard “disciplines” (math, physics, literature, econ, etc.).

Thus we have a poor system for evaluating work and people that sit between disciplines, or that cover many disciplines. Making it harder to evaluate work that combines areas A and B, and maybe also C and D. You might be able to get an A person to evaluate the A parts, and then a B person for the B parts, but that is more work, and the person who knows how to pick a good A evaluator may not know how to pick a good B evaluator. Academics tend to think that interdisciplinary groups do worse work, held to lower standards, and this is a big part of why.

Furthermore, even when specialists can evaluate such things well enough, they have an incentive to say “Maybe that should be supported, but not with our resources.” That is, for people and work that combines A and B, the A folks say it should be supported by the B budget, and vice versa. Often to be accepted by people in A, you must do as much good work in A as someone who only ever works in A, regardless of how much good work you also do in B, C, etc.

Yet generality still gains substantial prestige among intellectuals, which gives me hope. For example, there are usually fights to write more general summaries, such as review articles and textbooks, fights usually won by the highest in status. And Nobel prize winners, upon winning, often famously wax philosophic and general, pontificating (usually badly) on a much wider range of topics than they did previously.

Academic disciplines and departments usually need to do two things: (1) evaluate people to say who can join and stay in them, and (2) train new candidates in a way that makes it likely that many will later be evaluated positively in part (1). I’m not sure there is a way to do part (2) well here, but I think I at least know of a way to do part (1).

I propose that one university (and eventually many) create a Department of Generalists. (Maybe there’s a better name for it.) To apply to join this department, you must first get tenure in some other department. You submit your publication record, and from that they can calculate a measure of the range of your publications. Weighted by quality of course. Folks with very high range are assumed to be shoo-ins, folks with low ranges are routinely rejected, and existing department members have discretion on borderline cases.

How could we calculate publication range? I’ve posted before on using citation data to construct maps of academia. From such maps it seems straightforward to create robust metrics describing the volume in that space encompassed by a person’s research. And something like citations could be used to weigh publications in this metric. No doubt there is room for disagreement on exact metrics, and I’m not pushing to get too mechanical here. My point is that it is feasible to evaluate generality, as we know how to mechanically get a decent first cut measure of a researcher’s range.

So what do people in Department of Generalists do exactly? Well of course they continue with their research, and can continue to serve the departments form which they came. But they are encouraged to do more general research than do folks in other departments. They can now more easily talk with other generalists, work together on more general projects, and invite outside generalist speakers.

Maybe they experiment with training or mentoring other professors at the university to be generalists, people who hope to later apply to join this generalist department. They might be preferred candidates to write those prestigious general summaries, such as review articles and textbooks, and to teach generalist courses, like big introductory courses. And especially to review more generalist work by others.

It would of course be hard work to get such a department going. And you’d need to start it at a university where there are already many generalists who could get along. But I have high hopes, again from the fact that academics so often fight to appear general, as in fighting to write summarizes and to pontificate on more general issues. Once there was a widespread perception that people in the Department of Generalists were in fact better at being generalists, as well as meeting the usual criteria of at least one regular department, they would naturally be seen as an elite. A group that others aspire to join, patrons aspire to fund, reporters aspire to interview, and students aspire to learn under.

And then academia would less neglect work on integration, synthesis, and generality, and work between existing disciplines. Oh academia would still neglect those things, don’t get me wrong, just less. And that seems a goal worth pursuing.

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On Our Own

I didn’t expect this result, and it seems so terribly sad. Perhaps the saddest thing I have ever heard.

While other animals have feelings on fairness, and inclinations to retaliate against unfairness done directly to them, as far as I know only humans having norms requiring generic third parties help fix such complaints. So while other animals assume that they will only get as much justice as they (and their immediate allies) can force the world to give them, we humans are led to see our larger society as responsible for creating a justice world around us, and even led to expect that it will in fact typically provide such justice.

It must then be a crushing blow to realize that this is just not so. Even for pretty big injustices, most all of us see ourselves as better off to just suffer them, instead of publicly complaining about them, the vast majority of the time. Our larger societies do not in fact provide much justice; the justice we get is in fact mostly whatever we (and our immediate allies) can force the world to give us. In terms of justice, human societies today only produce a minor correction to the basic animal situation. That is, we are on our own. Our friends and family may help, but the rest of the world will not.

Oh, some of us probably do mostly get justice from the world. The rich, the pretty, the popular, the well-connected. When they complain, enough people care, and actually do something. For them, maybe it makes sense to complain about most big injustices. But not for the vast majority of us.

Was it ever any different in human history? I suspect not, alas. Maybe, someday in the future, the human thirst for justice will lead us to create societies that actually do stop most injustice, so that people who are are treated unjustly will usually think it worth their bother to publicly complain. So that such injustices are stopped. That is, someday, we may find a way to slake our thirst for justice. But so far, we remain incurably thirsty. That is the human condition.

Added 9a 12June: Some claimed that my poll wording was oft misinterpreted, and some claimed that restricting to “bothers you a lot” did not sufficiently distinguish minor from major injustices. So I did three more polls like the above, reworded a bit to avoid the misinterpretation, and distinguishing three levels of injustice: would have paid <$100, $100-10K, and >$10K to avoid them.

Looking at median of lognormal fits to % of cases where complaining is a net win, I find 7.1% for “bothers you a lot”, 1.9% for <$100, 4.1% for $100-$10K, and 7.7% for >$10K injustices. Thus the threshold for “bothers you a lot” seems to be near $10K, and while we do find it in our interest to complain more often for larger injustices, cases where complaining wins remain rare exceptions even for large injustices. Even then, we are mostly on our own.

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No Recent Automation Revolution

Unless you’ve been living under a rock, you know that for many years the media has been almost screaming that we entering a big automation revolution, with huge associated job losses, due to new AI tech, especially deep learning. The media has cited many “experts” making such claims, most every management consulting firm has felt compelled to issue a related report, and the subject came up in the Democratic US presidential debates.

Last December, Keller Scholl and I posted a working paper suggesting that this whole narrative is bullshit, at least so far. An automation revolution driven by a new kind of automation tech should induce changes in the total amount and rate of automation, and in which kinds of jobs get more automated. But looking at all U.S. jobs 1999-2019, we find no change whatsoever in the kinds of jobs more likely to be automated. We don’t even see a net change in overall level of automation, though language habits may be masking such changes. And having a job get more automated is not correlated at all with changes in its pay or employment. (There may be effects in narrow categories, like jobs that use robots, but nothing visible at the overall level of all automation.)

Two metrics created by groups trying to predict which jobs will get automated soon did predict past automaton, but not after we included 25 mundane job features like Pace Determined By Speed Of Equipment and Importance of Repeating Same Tasks, which together predict over half of the variance in job automation. The main change over the last two decades may be that job tasks have gradually become more suitable for automation, because nearby tasks have become automated.

Our paper has so far received zero media attention, even though it contradicts a lot of quite high visibility media hype, which continues on at the same rate. It has now been officially published in a respected peer reviewed journal: Economics Letters. Will that induce more media coverage? Probably not, as most of those other papers got media attention before they were peer reviewed. The patterns seems to be that hype gets covered, contradictory deflations of hype do not. Unless of course the deflation comes from someone prestigious enough.

For Economics Letters we had to greatly compress the paper. Here is the new 40 word abstract:

Wages and employment predict automation in 832 U.S. jobs, 1999 to 2019, but add little to top 25 O*NET job features, whose best predictive model did not change over this period. Automation changes predict changes in neither wages nor employment.

And Highlights:

  • 25 simple job features explain over half the variance in which jobs are how automated.
  • The strongest job automation predictor is: Pace Determined By Speed Of Equipment.
  • Which job features predict job automation how did not change from 1999 to 2019.
  • Jobs that get more automated do not on average change in pay or employment.
  • Labor markets change more often due to changes in demand, relative to supply.
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Philosopher Kings in Blue?

When things go wrong in our lives, we are often tempted to invoke governments to fix them. So we add more systems wherein governments do things, and we make more laws to influence what other people do. However, in the messy process of translating our general purposes into particular system and rules, we often allow various groups to control important details, and turn them more to their purposes. We also get random outcomes due to randomness in which political factions happen have more control when we turn our attention to changing each particular system or rule. In addition, we often leave out details because we are hypocritical, and unwilling to fully admit our real purposes. For example, we often want to appear to oppose things more than we do, like say drug use, prostitution, or adultery.

The net effect of these many messy processes is that our government systems and rules are poorly integrated, clumsy, and vague. We don’t bother to work out many details, and we don’t decide how to make key tradeoffs between different systems and rules. For such elaboration, the public and their politicians often punt to judges and government agencies. And for details where agencies don’t set policies, they punt to individual civil servants.

To influence these agencies and their civil servants, we set bosses who can give them orders, and perhaps promote or fire them. Bosses who have other bosses all the way up to the politicians we elect. But we are afraid of new politicians taking too much hidden control over these agencies, say by firing everyone and hiring all their friends. So we often limit politicians’ powers to direct and fire civil servants. This gives agencies and civil servants more discretion, to do what they choose.

Of course in any one social equilibrium, an individual civil servant may not feel they have great discretion. But that doesn’t contradict the claim that collectively they have a lot. That is, there can be many possible government equilibria consistent with the overall government rules and larger political and social worlds. Some of this government discretion may be captured by the schools and other systems that train people to become civil servants.

To enforce rules on both civil servants, and on ordinary people, we threaten to punish people for violating rules. The civil servants we put in charge of this enforcement process are “police” (in which I include prosecutors, judges, and other civil servants with rule-enforcing discretion). And to help police in these roles, we give them various budgets and powers.

The above description so far is pretty generic, applying nearly as well to a quite minimal state as to a strong “police state”, wherein police have strong powers to punish most anyone they choose. Where any one state sits on this spectrum is determined by many factors, including (1) police monetary budgets, (2) police direct powers to invade spaces, demand info, etc., (3) police negotiating powers regarding court proceedings, and (4) the frequency and severity of rules that people frequently violate.

While once upon a time (say two centuries ago) the U.S. system looked more like a minimal state, today it looks more like a police state. Maybe not as bad a police state as the old Soviet Union, but still, a police state. This transformation is detailed in William Stuntz’ excellent book The Collapse of American Criminal Justice. Some key changes:

  1. We’ve added a lot more laws, so many that we don’t understand most, and regularly violate many.
  2. We’ve cut the use of juries and also many legal defenses, which previously helped evade guilty verdicts.
  3. Rise of big cities means county-set laws are set by folks different from those suffer, cause most crime.
  4. States, who set prison budgets but don’t control conviction rates, greatly increased prison budgets.
  5. Legal trial complexity & cost has risen greatly, and is now beyond what most can afford.
  6. Plea bargaining is now allowed, which strongly pushes people to plead guilty, even when they aren’t.
  7. The new doctrine of qualified immunity protects government officials from many lawsuits.
  8. Most complaints about police have long been investigated by the same agency that employs them.
  9. The rise in civil servant unions, especially police unions.
  10. Surveillance, tracking, and info collection has in many ways become much cheaper.

(Some of these changes resulted from courts seeking to encourage big moral movements, such as those against slavery, alcohol, drugs, prostitution, polygamy, and gambling.)

The net effect of all this is that police can, if they so choose, target most anyone for punishment. That is, for most any target, police can relatively cheaply find a rule the target violated, pressure others to testify against the target, and then finally pressure the target to plead guilty. And police collectively have a lot of discretion in how they use this power. (The rich and politically well-connected may of course be able to discourage such use of power against themselves.)

Of course, the fact that police are powerful hardly implies that they use such powers badly. It remains quite possible that, like the proverbial super-hero, they use their super-powers for good. Many people have long claimed that the best form of government is one run by good-hearted but unconstrained philosopher kings.

This is the context in which I’d like you to consider current complaints about police mistreatment of detainees. Police must make difficult and context-dependent tradeoffs between how carefully to avoid hurting detainees, and how aggressively to discourage them from defiance or escape.

These are the sort of areas where, in our system, local civil servants and their agencies have great discretion, and where the basic nature of our government and legal systems makes it hard to pull back such discretion. I’m not saying that nothing can be done; things can and should be done. But I’m pretty sure that the sort of modest changes being now considered (more training, more record keeping, “requiring” body cams, etc.) can’t greatly change what is in essence a police state. (In contrast, changing to a bounty system might do a lot more.)

Look, imagine that while interacting with police you started to insult them and call them terrible ugly names. In many places, this is probably perfectly legal. However, you’d be rightly reluctant to do this, as you’d know they have a many ways to retaliate. If their local people and culture are inclined to retaliate, and to build a “blue wall of silence” around it, there is little most people can do to protect themselves.

This is why you can’t really count on laws that say you have the right to film police, etc. We basically live in a police state, and in such a state its hard for mere rules to greatly change police behavior. We may well be gaining some benefits from such a police state, but being able to exert detailed control over police and how they use their great discretion is just not one of them.

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Missing Model: Too Much Do-Gooding

Grim view of human nature … is mistaken, a persistent and counterproductive myth. … the evidence for mass selfishness is extremely thin. … The surprising truth is that people tend to be­have decently in a crisis. To the British, the all-too-familiar example is the cheerful demeanour of Londoners during the Blitz. … New Orleans after Hurricane Katrina … rumours ran wild about the murder and rape of children inside the Louisiana Superdome; but when the national guard showed up, … met instead by a nurse asking for medical supplies. (more)

Friday I asked the author of a pandemic novel what he thought went most wrong in his fictional world. He said selfishness: blaming others, and not sacrificing enough to protect others from infection. He also said he was surprised to see people acting less selfishly than he predicted in our real pandemic.

As the above quote indicates, that’s a common mistake. In this pandemic I estimate that the bigger problem is people pushing for too much “helping”, rather than too little. That’s a common problem in health and medicine, and this poll says 2-1 that it is the more common problem:

Of course my Twitter followers are probably unusual by this metric; I’d bet most think selfishness is the bigger problem. One reason is that it can look suspiciously selfish to say there’s too much do-gooding, as if you were trying to excuse your selfish behavior. Another reason is that the theory of selfishness is simpler. In economics, for example, we teach many quite simple game theory models of temptations to selfishness. In contrast, it seems harder to explain the core theory of why there might be too much do-gooding.

This seems to suggest a good and feasible project: generate or identify some good simple game theory models that predict too much do-gooding. Not just personal signaling acts that do too much, but acts that push collective norms and decisions toward too much do-gooding. I’d be happy to help with such a project. Of course it would make only a small contribution to the problem, but still I’d guess one worth the trouble.

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Science 2.0

Skepticism … is generally a questioning attitude or doubt towards one or more items of putative knowledge or belief or dogma. It is often directed at domains, such as the supernatural, morality (moral skepticism), theism (skepticism about the existence of God), or knowledge (skepticism about the possibility of knowledge, or of certainty). (More)

Humans have long had many possible sources for our beliefs about the physical world. These include intuitive folk physics, sacred scriptures, inherited traditions, traveler stories, drug-induced experiences, gadget sales pitches, and expert beliefs within various professions. And for a very long time, we paid the most attention to the highest status sources, even if they were less reliable. This encouraged gullibility; we often believed pretty crazy stuff, endorsed by the high status.

One ancient high status group was astronomers, whose status was high because their topic was high – the sky above. It so happened that astronomers naturally focused on a small number of very standard parameters of wide interest: the sky positions of planets and comets (anything that moved relative to the stars). Astronomers often gained status by being better able to predict these positions, and for this purpose they found it useful to: (1) collect and share careful records on past positions, (2) master sufficient math to precisely describe past patterns, and (3) use those patterns to predict future parameter values.

For a long time astronomy seemed quite exceptional. Most other domains of interest seemed to have too much fuzziness, change, and variety to support a similar approach. What can you usefully measure while walking through a jungle? What useful general patterns can simple math describe there? But slowly and painfully, humans learned to identify a few relatively stable focal parameters of wide interest in other domains as well. First in physics: velocity, weight, density, temperature, pressure, toughness, heat of reaction, etc. Then in dozens of practical domains.

With such standard focal parameters in hand, domain experts also gained status by being able to predict future parameter values. As a result, they also learned that it helped to carefully collect shared systematic data, and to master sufficient math to capture their patterns.

And thus beget the scientific revolution, which helped beget the industrial revolution. A measurement revolution starting in astronomy, moving to physics, and then invading dozens of industrial domains. As domains acquired better stable focal parameters to observe, and better predictions, many such domains acquired industrial power. That is, those who had mastered such things could create devices and plans of greater social value. This raised the status of such domain experts, so that eventually this “scientific” process acquired high status: carefully collecting stable focal parameters, systematically collecting and sharing data on them, and making math models to describe their patterns. “Science” was high status.

One way to think about all this is in terms of the rise of skepticism. If you allow yourself to doubt if you can believe what your sources tell you about the physical world, your main doubt will be “who can I trust?” To overcome such doubt, you’ll want to focus on a small number of focal parameters, and for those seek shared data and explicit math models. That is, data where everyone can check how the data is collected, or collect it themselves, with redundant records to protect against tampering, and explicit shared math models describing their patterns. That is, you will turn to the methods to which those astronomers first turned.

Which is all to say that the skeptics turned out to be right. Not the extreme skeptics who doubted their own eyes, but the more moderate ones, who doubted holy scriptures and inherited traditions. Our distant ancestors were wrong (factually, if not strategically) to too eagerly trust their high status sources, and skeptics were right to focus on the few sources that they could most trust, when inclined toward great doubt. Slow methodical collection and study of the sort of data of which skeptics could most approve turned out to be a big key to enabling humanity’s current levels of wealth and power.

For a while now, I’ve been exploring the following thesis: this same sort of skepticism, if extended to our social relations, can similarly allow a great extension of our “scientific” and “industrial” revolutions, making our social systems far more effective and efficient. Today, we mainly use prestige markers to select and reward the many agents who serve us, instead of more directly paying for results or following track records. If asked, many say we do this because we can’t measure results well. But as with the first scientific revolution, with work we can find ways to coordinate to measure more stable focal parameters, sufficient to let us pay for results. Let me explain.

In civilization, we don’t do everything for ourselves. We instead rely on a great many expert agents to advise us and act for us. Plumbers, cooks, bankers, fund managers, manufacturers, politicians, contractors, reporters, teachers, researchers, police, regulators, priests, doctors, lawyers, therapists, and so on. They all claim to work on our behalf. But if you will allow yourself to doubt such claims, you will find plenty of room for skepticism. Instead of being as useful as they can, why don’t they just do what is easy, or what benefits them?

We don’t pay experts like doctors or lawyers directly for results in improving our cases, and we don’t even know their track records in previous cases. But aside from a few “bad apples”, we are told that we can trust them. They are loyal to us, coming from our nation, city, neighborhood, ethnicity, gender, or political faction. Or they follow proper procedures, required by authorities.

Or, most important, they are prestigious. They went to respected schools, are affiliated with respected institutions, and satisfied demanding licensing criteria. Gossip shows us that others choose and respect them. If they misbehave then we can sue them, or regulators may punish them. (Though such events are rare.) What more could we want?

But of course prestige doesn’t obviously induce a lawyer to win our case or promote justice, nor a doctor to make us well. Or a reporter to tell us the truth. Yes, it is logically possible that selecting them on prestige happens to also max gains for us. But we rarely hear any supporting argument for such common but remarkable claims; we are just supposed to accept them because, well, prestigious people say so.

Just as our distant ancestors were too gullible (factually, if not strategically) about their sources of knowledge on the physical world around them, we today are too gullible on how much we can trust the many experts on which we rely. Oh we are quite capable of skepticism about our rivals, such as rival governments and their laws and officials. Or rival professions and their experts. Or rival suppliers within our profession. But without such rivalry, we revert to gullibility, at least regarding “our” prestigious experts who follow proper procedures.

Yes, it will take work to develop better ways to measure results, and to collect track records. (And supporting math.) But progress here also requires removing many legal obstacles. For example, trial lawyers all win or lose in public proceedings, records of which are public. Yet it is very hard to actually collect such records into a shared database; many sit in filing cabinets in dusty county courthouse basements.

Contingency fees are a way to pay lawyers for results, but they are illegal in many places. Bounty hunters are paid for results in catching fugitives, but are illegal in many places. Bail bonds give results incentives to those who choose jail versus freedom, but they are being made illegal now. And so on. Similarly, medical records are more often stored electronically, but medical ethics rules make it very hard to aggregate them, and also to use creative ways to pay doctors based on results.

I’ve written many posts on how we could work to pay more for results, and choose more based on track records. And I plan to write more. But in this post I wanted to make the key point that what should drive us in this direction is skepticism about how well we can trust our usual experts, chosen mainly for their prestige (and loyalty and procedures) and using weak payment incentives. You might feel embarrassed by such skepticism, thinking it shows you to be low status and anti-social. After all, don’t all the friendly high status popular people trust their experts?

But the ancient skeptics were right about distrusting their sources on the physical world, and following their inclination helped to create science and industry, and our vast wealth today. Continuing to follow skeptical intuitions, this time regarding our expert agents, may allow us to create and maintain far better systems of law, medicine, governance, and much more. Onward, to Science 2.0!

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