Tag Archives: Disagreement

Who Wants Common Sense?

The mass media often says things that should seem unlikely, at least to a well-informed common sense. And in such cases, the usual outcome is that common sense is proved right. This seems so obvious to me that I don’t see the point in arguing it. But to illustrate the point, let me mention the book Expert Political Judgement, and recent claims that AI would take away most jobs, that masks and travel restrictions do not help in pandemics, and that hell on Earth will result if the other side wins the next election.

What I want to point out in this post is a noteworthy lack of clearly-available voices that express such well-informed common-sense-based media-skepticism.

Let us focus on the top 1% of the top 1% of people, in terms of their ability to understand and apply common sense. Such people would be reasonably smart, know the intro-textbook basics of many fields, and the basic history of their industry, region, and world for the last century or two. Oh, and they must be able to write tolerably clearly.

Out of 8 billion people in the world, there should be 800K people in this 1% of 1% class. Each of which could in principle author a newsletter, blog, or podcast, etc. wherein they specialize in pointing out the worst ways that recent media reports conflict with common sense. In its first decade or two, such a newsletter could emphasize cases likely to resolve within a decade or two, in the sense that any reasonable attempt to score them for accuracy will be able to credit a substantial fraction of what they’ve said on this timescale.

For example, if you made one comment per week for ten years, that’s 500 comments, and if just 40% of these could be scored within two decades, that’s 200 scoreable comments. And if you make ten comments per week, that’s 2000 to score. Which should be plenty enough to show that an author can see and apply common sense to correct media errors.

Imagine that the top 1% of media consumers could recognize and appreciate such a track record. So if an author took a decade or two to collect such a track record of cases pointing out media deviations from common sense, this 1% of consumers would be capable of browsing this track record to evaluate it, or trusting intermediaries who scored it for them. And they’d value such a common sense corrections enough that they’d spend some time actually reading them.

So I’m postulating 80M media consumers who would want to read common sense media critiques, and 800K authors capable of writing such critiques, and of validating their track record within a decade or two. This seems a large enough market, in terms of supply and demand, that we should see at least 800 actual entrants, who regularly write commentary on media errors. That’s only one entrant per 100K customer/readers, and one per 1K potential authors.

Surely 80M customers eager to read such commentary could induce at least 800 writers to regularly write such things. Even if such authors did it as a hobby on the side, after their regular job, and got paid nothing directly for it. Maybe most of these 800K folks have better things to do with their time, but not all of them. The wisdom of at least one in a thousand of them may not be recognized by the labor market, or its realization may be blocked by individual personality quirks. Surely we all know this large a fraction of smart and wise but under-used folks.

Consider further that this class of 800K potential authors could each team with associates, to create more effective commentary. Associates could feed these authors summaries of media cases to consider, could polish their prose to become more concise and accessible to readers, and could organized the scoring of their track records. And once an author had validated his or her own track record, they might later specialize in rating other sources, either by endorsing their track records or directly including their commentary. Given all these possibilities, I’m confident that at least 800 writers could actually write such commentary, and have it be validated as accurate, if in fact there were 80M customers willing to read them.

Furthermore, 800 authors would allow a substantial degree of specialization, wherein each author focuses to some extent on particular regions, industries, topics, and media sources. I’d expect a lot of overlap, wherein authors end up commenting on the same media stories. But we don’t need all 80M customers to care mainly about the same world-media stories, ones that most of these 800 authors comment on. We just need these 80M customers to have wide enough interests so that 800 authors suffice to serve them.

The attentive reader has probably already deduced my point: As we don’t actually see 800 authors specializing in using common sense to correct common media errors, and proving their accuracy via track records, there must not actually be even 1% of media consumers interested in reading such corrections. And as I’m confident that at least 1% would be able to find and appreciate such corrections, if they were interested, I must put the main blame on their lack of interest.

I’m not sure we even see eight authors who specialize in this basic writing strategy of using common sense to correct media errors. So I’d say there may not even be 800K customers worldwide, 1% of 1% of readers, interested in reading such media corrections written by the top 1% of 1% common-sense authors, assuming that such writers are willing to write commentary if they can expect 100K readers each.

Now, I expect that many people will say that they’d like to read such commentary. But only as long as that comes with all the usual other things they get from their pundits. Such as wit, political affiliation, name recognition, and arguments they can repeat to associates to sound smart. They aren’t much willing to trade off those other desired pundit qualities for more common sense critical accuracy. Which of course really means that they don’t much care for common sense based media criticism.

Yes, media markets are often regulated. Professional licensing prevents most people from talking on some topics, and media regulation prevents many from getting paid for their commentary. Libel laws and other kinds of liability often punish honesty, as do cancel mobs. But on reflection I just can’t put the main blame on these things. There is in fact usually enough freedom of speech that media error correction could find an audience, if a large enough audience actually existed. (And yes, perhaps also if they stayed away from the most controversial of topics.)

Some hope that future innovations like AI-written commentary, or prediction markets on common media topics, could eventually provide such common-sense based criticism. But can it do so cheaply enough to overcome the low market demand problem? If even simple articulate humans can’t find such a market today, I don’t see why AI or prediction markets should expect to do much better later.

Finally, consider this: if there’s no market for the easiest cheapest way to correct many big errors all at once, why would there be markets for less-effective more-expensive ways to correct media errors?

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More on Experts Vs. Elites

When a boss issues a new order, usually the main thing he or she is fighting with is the effects of his (or a prior boss’) previous orders. It can take time to undo their effects. And subordinates who fear that yet newer orders will come down before they can make enough changes might prefer to drag their feet, to see if these current orders will last.

Some responded to my last post on experts versus elites by saying how good it is that elites often overrule experts, as experts get it so wrong so often. As with early in this pandemic. But the experts are less of an autonomous force here, and more just the repository of previous elite instructions. If pandemic experts had it wrong before about masks or travel bans, that is mostly because elites previously pushed them to adopt such policies. For example, our continued ban on challenge trials is due to how med ethics experts have interpreted prior elite instructions. Experts won’t change their mind on this until elites tell them they are allowed to change their minds. In contexts where elites are typically so pushy, it can be hard to tell what experts would decide in their absence.

In economics, it usually feels pretty obvious what the elites want us to say. Not all economists do what they are told, but the major institutions and their elite leaders seem mostly willing to go along, and so what the public mostly hears is economists saying what elites want us to say. When elites change their minds, our major institutions also quickly change their minds.

Now I had been thinking this is all bad news for the new kinds of institutions I want to introduce, as I had been assuming they would be framed as new expert institutions. And yes all this suggests a distrust of formal expert mechanisms that can’t be easily overruled by elite opinion. But maybe I have been too hasty about how new institutions might be framed.

Consider the widespread hostility to “market manipulation”, such as seen in the recent Gamestop stock price episode. Or consider movies like Boiler Room, Glengarry Glen Ross, Wall Street, and Wolf of Wall Street. Typically, financial markets are chock full of “manipulation”, in the sense that most traders are trying to talk and spin to get others to agree with and follow their trades. Sometimes they succeed, and sometimes they fail, but that mostly doesn’t bother people. What bothers people most is when they see clearly low status low prestige people seeming to greatly influence prices, especially in ways that seem unlikely to last. (Elite manipulations tend to last.)

Consider also that elites only rarely complain about errors in speculative market prices, such as stock prices or currency prices. They mainly complain when they think they can find non-elite folks to blame for such prices. Together, these facts suggest to me that most elites may see speculative market prices as something that elites create. They know that there is a lot of money at stake in such markets, and that many big powerful rich elite players play heavily in such markets. So perhaps elites usually accept the verdict of such prices as a verdict of elites!

If this were true, then the prospects for improving our social consensus via improving speculative markets would be far higher than I’d ever hoped! If we could get thick markets trading on many more topics, then elites might well defer to those price estimates in their elite conversations, and push experts to also accept such estimates.

Of course, even if elites would accept a price estimate when it exists, this doesn’t mean most are eager for such any particular price to exist. Rivalrous elites constantly try to undermine each other, including via undermining the organs that rival elites use to express their opinions. If if the prices existed for a while, I predict elites would cave and defer to them, at least until they could kill them.

To signal to all that they are dominated by elites, I do think it important that a lot of money seem be riding on these market prices. Mere prediction tournaments or polls of experts just will not do. Even real money markets with small stakes may not be taken seriously enough.

My proposal for Fire-the-CEO markets seems like it could work here. Though I’ve been waiting for 25 years now for someone to take up this idea.

Added 8Feb: I see now why my usual answer to “what should I read?”, namely “textbooks”, falls on deaf ears. People are looking for elites to read, not experts.

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Experts Versus Elites

Consider a typical firm or other small organization, run via a typical management hierarchy. At the bottom are specialists, who do very particular tasks. At the top are generalists, who supposedly consider it all in the context of a bigger picture. In the middle are people who specialize to some degree, but who also are supposed to consider somewhat bigger pictures.

On any particular issue, people at the bottom can usually claim the most expertise; they know their job best. And when someone at the top has to make a difficult decision, they usually prefer to justify it via reference to recommendations from below. They are just following the advice of their experts, they say. But of course they lie; people at the top often overrule subordinates. And while leaders often like to pretend that they select people for promotion on the basis of doing lower jobs well, that is also often a lie.

Our larger society has a similar structure. We have elites who are far more influential than most of us about what happens in our society. As we saw early in the pandemic, the elites are always visibly chattering among themselves about the topics of the day, and when they form a new opinion, the experts usually quickly cave to agree with them, and try to pretend they agreed all along.

As a book I recently reviewed explains in great detail, elites are selected primarily for their prestige and status, which has many contributions, including money, looks, fame, charm, wit, positions of power, etc. Elites like to pretend they were selected for being experts at something, and they like to pretend their opinions are just reflecting what experts have said (“we believe the science!”). But they often lie; elite opinion often overrules expert opinion, especially on topics with strong moral colors. And elites are selected far more for prestige than expertise.

When an academic wins a Nobel prize, they have achieved a pinnacle of expertise. At which point they often start to wax philosophic, and writing op-eds. They seem to be making a bid to become an elite. Because we all respect and want to associate with elites far more than with experts. Elites far less often lust after becoming experts, because we are often willing to treat elites as if they are experts. For example, when a journalist writes a popular book on science, they are often willing to field science questions when they give a talk on their book. And the rest of us are far more interested in hearing them talk on the subject than the scientists they write about.

Consider talks versus panels at conferences. A talk tends to be done in expert mode, wherein the speaker sticks to topics on which they have acquired expert knowledge. But then on panels, the same people talk freely on most any topic that comes up, even topics where they have little expertise. You might think that audiences would be less interested in hearing such inexpert speculation, but in fact they seem to eat it up. My interpretation: on panels, people pose as elites, and talk in elite mode. Like they might do at a cocktail party. And audiences eagerly gather around panelists, just like they would gather around prestigious folks arguing at a cocktail party about topics on which they have little expertise.

Consider news articles versus columnists. The news articles are written by news experts, in full expert mode. They are clearly more accurate on average than are columns. But columns writers take on an elite mode, where they pontificate on all issues of the day, regardless of how much they know. And readers love that.

Consider boards of directors versus boards of advisors. Advisors are nominally experts, while directors are nominally elites. Directors are far more powerful, are lobbied far more strongly, and are paid a lot more too. Boards of advisors are usually not asked for advise, they are mainly there to add prestige to an organization. But prestige via their expertise, rather than their general eliteness.

Even inside academic worlds, we usually pretend to pick leaders like journal editors, funding program managers, department chairs, etc. based mainly on their expert credentials. But they also lie; raw prestige counts for a lot more than they like to admit.

Finally, consider that recently I went into clear expert mode to release a formal preprint on grabby aliens, which induced almost no (< 10) comments on this blog or Twitter, in contrast to far more comments when arguable-elites discuss it in panelist/elite mode: Scott Aaronson (205), Scott Alexander (108), and Hacker News (110). People are far more interested in talking with elites in elite mode on most topics, than in talking with the clear relevant experts in expert mode.

All of which suggests that my efforts to replace choice via elite association with prediction markets and paying for results face even larger uphill battles than I’ve anticipated.

Added noon: This also helps explain why artists are said to “contribute to important conversations” by making documentaries, etc. that express “emotional truths.” They present themselves as qualifying elites by virtue of their superior art abilities.

See also: More on Experts Vs. Elites

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Lognormal Priorities

In many polls on continuous variables over the last year, I’ve seen lognormal distributions typically fit poll responses well. And of course lognormals are also one of the most common distributions in nature. So let’s consider the possibility that, regarding problem areas like global warming, falling fertility, or nuclear war, distributions of priority estimate are lognormal.

Here are parameter values (M = median, A = (mean) average, S = sigma) for lognormal fits to polls on how many full-time equivalent workers should be working on each of the following six problems:

Note that priorities as set by medians are quite different from those set by averages.

Imagine that someone is asked to estimate their (median) priority of a topic area. If their estimate results from taking the product of many estimates regarding relevant factors, then not-fully-dependent noise across different factors will tend to produce a lognormal distribution regarding overall (median) estimates. If they were to then act on those estimates, such as for a poll or choosing to devote time or money, we should see a lognormal distribution of opinions and efforts. When variance (and sigma) is high, and effort is on average roughly proportional to perceived priority, then most effort should come from a quite small fraction of the population. And poll answers should look lognormal. We see both these things.

Now let’s make our theory a bit more complex. Assume that people see not only their own estimate, but sometimes also estimates of others. They then naturally put info weight on others’ estimates. This results in a distribution of (median) opinions with the same median, but a lower variance (and sigma). If they were fully rational and fully aware of each others’ opinions, this variance would fall to zero. But it doesn’t; people in general don’t listen to each other as much as they should if they cared only about accuracy. So the poll response variance we see is probably smaller than the variance in initial individual estimates, though we don’t know how much smaller.

What if the topic area in question has many subareas, and each person gives an estimate that applies to a random subarea of the total area? For example, when estimating the priority of depression, each person may draw conclusions by looking at the depressed people around them. In this case, the distribution of estimates reflects not only the variance of noisy clues, but also the real variance of priority within the overall area. Here fully rational people would come to agree on both a median and a variance, a variance reflecting the distribution of priority within this area. This true variance would be less than the variance in poll responses in a population that does not listen to each other as much as they should.

(The same applies to the variance within each person’s estimate distribution. Even if all info is aggregated, if this distribution has a remaining variance, that is “real” variance that should count, just as variance within an area should count. It is the variance resulting from failing to aggregate info that should not count.)

Now let’s consider what this all implies for action biases. If the variance in opinion expressed and acted on were due entirely to people randomly sampling from the actual variance within each area, then efforts toward each area would end up being in proportion to an info-aggregated best estimates of each area’s priority – a social optimum! But the more that variance in opinion and thus effort is also due to variance in individual noisy estimates, then the more that such variance will distort efforts. Efforts will go more as the average of each distribution, rather than its median. The priority areas with higher variance in individual noise will get too much effort, relative to areas with lower variance.

Of course there are other relevant factors that determine efforts, besides these priorities. Some priority areas have organizations that help to coordinate related efforts, thus reducing free riding problems. Some areas become fashionable, giving people extra social reasons to put in visible efforts. And other areas look weird or evil, discouraging visible efforts. Even so, we should worry that too much effort will go to areas with high variance in priority estimate noise. All else equal, you should avoid such areas. Unless estimate variance reflects mostly true variance within an area, prefer high medians over high averages.

Added 3p: I tried 7 more mundane issues, to see how they varied in variance. The following includes all 13, sorted by median.

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Sim Argument Confidence

Nick Bostrom once argued that you must choose between three options re the possibility that you are now actually living in and experiencing a simulation created by future folks to explore their past: (A) its true, you are most likely a sim person living in a sim, either of this sort or another, (B) future folk will never be able to do this, because it just isn’t possible, they die first, or they never get rich and able enough, or (C) future folk can do this, but they do not choose to do it much, so that most people experiencing a world like yours are real humans now, not future sim people.

This argument seems very solid to me: future folks either do it, can’t do it, or choose not to. If you ask folks to pick from these options you get a simple pattern of responses:

Here we see 40% in denial, hoping for another option, and the others about equally divided among the three options. But if you ask people to estimate the chances of each option, a different picture emerges. Lognormal distributions (which ignore the fact that chances can’t exceed 100%) are decent fits to these distributions, and here are their medians:

So when we look at the people who are most confident that each option is wrong, we see a very different picture. Their strongest confidence, by far, is that they can’t possibly be living in a sim, and their weakest confidence, by a large margin, is that the future will be able to create sims. So if we go by confidence, poll respondents’ favored answer is that the future will either die soon or never grow beyond limited abilities, or that sims are just impossible.

My answer is that the future mostly won’t choose to sim us:

I doubt I’m living in a simulation, because I doubt the future is that interested in simulating us; we spend very little time today doing any sort of simulation of typical farming or forager-era folks, for example. (More)

If our descendants become better adapted to their new environment, they are likely to evolve to become rather different from us, so that they spend much less of their income on sim-like stories and games, and what sims they do like should be overwhelmingly of creatures much like them, which we just aren’t. Furthermore, if such creatures have near subsistence income, and if a fully conscious sim creature costs nearly as much to support as future creatures cost, entertainment sims containing fully conscious folks should be rather rare. (More)

If we look at all the ways that we today try to simulate our past, such as in stories and games, our interest in sims of particular historical places and times fades quickly with our cultural distance from them, and especially with declining influence over our culture. We are especially interested in Ancient Greece, Rome, China, and Egypt, because those places were most like us and most influenced us. But even so, we consume very few stories and games about those eras. And regarding all the other ancient cultures even less connected to us, we show far less interest.

As we look back further in time, we can track decline in both world population, and in our interest in stories and games about those eras. During the farming era population declined by about a factor of two every millennium, but it seems to me that our interest in stories and games of those eras declines much faster. There’s far less than half as much interest in 500AD than in 1500AD, and that fact continues for each 1000 year step backward.

So even if future folk make many sims of their ancestors, people like us probably aren’t often included. Unless perhaps we happen to be especially interesting.

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Time to Mitigate, Not Contain

In a few hours, I debate “Covid-19: Contain or Mitigate?” with Tomas “Hammer & Dance” Pueyo, author of many popular pandemic posts (e.g. 1 2 3 4 5 6 7). Let me try to summarize my position.

We have long needed a Plan B for the scenario where a big fraction of everyone gets exposed to Covid19, and for this plan I’ve explored variolation and other forms of deliberate exposure. To be ready, variolation just needs a small (~100) short (1-2mo.) trial to verify the quite likely (>75%) case that it works (cuts harm by 3-30x), but alas, while funding and volunteers can be found, med ethics panels have consistently disapproved. (Months later, they haven’t even allowed the widely praised human challenge trials for vaccines, some of which would include variolation trials.)

One big reason we aren’t preparing enough for Plan B is that many of us are mentally stuck in a Plan A “monkey trap.” Like a monkey who gets caught because it won’t let go of a tasty nut held in its fist within a gourd, we are so focused on containing this pandemic that we won’t explore options premised on failing to contain.

Containment seeks to keep infections permanently low, via keeping most from being exposed, for at least several years until a strong lasting vaccine is widely applied. Mitigation, in contrast, accepts that most will be exposed, and seeks only to limit the rate or exposure to keep medical systems from being overwhelmed, and to maintain enough critical workers in key roles.

Succeeding at containment is of course a much bigger win, which is why containment is the usual focus early in a pandemic. Catch it fast enough, and hit it hard enough with testing, tracing, and isolation, and the world is saved. But eventually, if you fail at that Plan A, and it grows big enough across across a wide enough area, you may need to admit failure and switch to a Plan B.

And, alas, that’s where we seem to be now with Covid-19. Over the last 7 weeks since the cases peak the official worldwide case count has been rising slowly, while official deaths are down ~25%. In the US, deaths are down ~1/2 since he peak 6 weeks ago. You might think, “yay, declines, we are winning!” But no, these declines are just too slow, as well as too uncertain.

Most of the US decline has been in New York, which has just now reached bottom, with no more room to decline. And even if US could maintain the rate of declining by 1/3 every 6 weeks, and repeat that 5 times over 30 weeks (= 7 months), not at all a sure thing, that would only bring daily US cases from 31K at the peak down to 4.2K. Which isn’t clearly low enough for test and trace to keep it under control without lockdown. And, more important, we just can’t afford to lockdown for that long.

You see, lockdown is very expensive. On average, around the world, lockdowns seems to cost about one third of local average income. Yet I estimate the cost of failing to contain, and letting half the population get infected with Covid-19 (at IFR ~0.5%), to be about 3 weeks of income. (Each death loses ~8 QALY.) So 9 weeks of strong lockdown produces about the same total harm as failing to contain! And where I live, we have almost had 10 weeks of lockdown.

If without lockdown the death rate would double due to an overloaded medical system, then paying for less than 9 weeks of added lockdown to prevent that is a good deal. But at that point, paying more than an additional 9 weeks of strong lockdown to prevent all deaths would not be a good deal. So our willingness to pay for lockdowns to cut deaths should really be quite limited. Sure, if we were at a tipping point where spending just a bit more would make all the difference between success and failure, then sure we should spend that bit more. But that’s just not plausibly where we are now.

Yes, sometimes we pay more to prevent harm than we suffer on average from such harms. For example, we pay more for door locks, and security guards, than we lose on average from theft. But those are very effective ways to prevent harm; paying 10% more there cuts harms by much more than 10%. Yet according to my Twitters polls, most see 10% more spent on lockdown as producing much less than 10% fewer deaths. If so, we should spend much less on lockdowns than we suffer from pandemic deaths.

Now if Pueyo is reading this, I suspect he’s screaming “But we’ve been doing it all wrong! Other possible policies exist that are far more effective, and if we use them containment becomes cost-effective. See Taiwan or South Korea.” And yes, other places have achieved better outcomes via better policies. We might not be able to do as well as them now that we’ve lost so much time, but we might well do much better than currently. Pueyo has sketched out plans, and they even seem to be good sketches.

So if we suddenly made Tomas Pueyo into a policy czar tomorrow, with an unlimited budget and able to run roughshod across most related laws or policies, we’d probably get much better Covid-19 outcomes, perhaps even cost-effective containment. But once such a precedent was set, I’d fear for the effectiveness of future czars. Ambitious politicians and rent-seekers would seek to manufacture crises and pseudo-Pueyo-czar candidates, all to get access to those unlimited budgets and legal powers.

Which is a big part of why we have the political systems we do. All around the world, we have created public health agencies tasked with pandemic policy, and political systems that oversee them. These agencies are staffed with experts trained in various schools of thought, who consult with academic experts of varying degrees of prestige. And all are constrained by local legal precedent, and by public perceptions, distrust, and axes of political conflict. These are the people and systems that have produced the varying policies we have now, all around the world.

Yes, those of us in places which have seen worse outcomes should ask ourselves how strong a critique that fact offers of our existing institutions and political cultures, and what we might do to reform them. But there is no easy and fast answer there; good reforms will have to be carefully considered, tested, and debated. We can hope to eventually improve via reforms, but, and this is the key point, we have no good reason to expect much better pandemic policy in the near future than we have seen in the near past. Even when policy makers have access to well-considered policy analyses by folks like Pueyo.

Now it might be possible to get faster political action if Pueyo and many other elites would coordinate and publicly back one specific standard plan, say the “John Hopkins Plan”, that specifies many details on how to do testing, tracing, isolation, etc. Especially if this plan pretty directly copied a particular successful policy package from, say, Taiwan. If enough people yelled in unison “We must do this or millions will die!”, why then politicians might well cave and make it happen.

But that is just not what is happening. Instead, we have dozens of essays and white papers pushing for dozens of related but different proposals. So there’s no clear political threat for politicians to fear defying. Whatever they do, come re-election time politicians can point to some who pushed for some of what they did. So all these divergent essays have mainly limited short term political effects, though they may do much more to raise the status of their authors.

So if political complexity argues against containment now in many places, why doesn’t that same argument apply equally well to mitigation? After all, mitigation can also be done well or badly, and it must be overseen by the same agencies and politicians that would oversee containment. As there is no escaping the fact that many detailed policy choices must be made, why not push for the best detailed packages of choices that we know?

Imagine that you were driving from A to B, and your first instinct was to take a simple route via two interstate freeways, both in big valleys. Your friend instead suggests that you take a backroad mountain short cut, using eight different road segments, many of them with only one lane, and some very wiggly. (Assume no phone or GPS.) That plan might look like it would take less total time, but you should worry about your competence to follow it. If you are very tired, bad at following road directions, or bad at sticking to wiggly roads, you might prefer to instead take the interstates. Especially if it was your tired 16 year old teen son who will do the driving.

Like the wiggly backroad short cut, containment is a more fragile plan, more sensitive to details; it has to be done more exactly right to work. To contain well, we need the right combination of the right rules about who can work and shop, and with what masks, gloves, and distance, tests going to the right people run by the right testing orgs, the right tracing done the right way by the right orgs with the right supporting apps, and the right rules requiring who gets isolated where upon what indications of possible infection. All run by the right sort of people using the right sort of local orgs and legal authority. And coordinated to right degree with neighboring jurisdictions, to avoid “peeing section of the pool” problems.

Yes, we might relax lockdown badly, but we are relaxing toward a known standard policy: no lockdown. So there are fewer ways to go wrong there. In contrast, there are just more ways to go wrong in trying to lockdown even more strictly. And that’s why it can make sense for the public to say to the government, “you guys haven’t been doing so well at containment, so let’s quit that and relax lockdown faster, shooting only for mitigation.” Yes, that might go badly, but it can’t go quite as badly as the worse possible scenario, where we trash the economy with long painful lockdowns, and yet still fail and most everyone gets exposed.

And that’s my argument for mitigation, relative to containment.

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Reply to Cowen On Variolation

In the last nine days I’ve done two online debates on variolation, with Zvi Mowshowitz and Gregory Cochran. In both cases my debate partners seemed to basically agree with me; disagreements were minor. Last night Tyler Cowen posted 1000+ words on “Why I do not favor variolation for Covid-19”. Yet oddly he also doesn’t seem to disagree with my main claims that (1) we are likely to need a Plan B for all-too-likely scenario where most of the world seems likely to get infected soon, and (2) variolation is simple, mechanically feasible, and could cut Covid19 Deaths by a factor of 3-30.

Tyler lists 8 points, but really makes 11. If he had one strong argument, he’d have focused on that, and then so could I in my response. Alas, this way I can’t respond except at a similar length; you are warned. Continue reading "Reply to Cowen On Variolation" »

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Crush Contrarians Time?

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

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

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

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

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

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Common Useless Objections

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

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

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

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

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

So what do useful objections look like? Try these:

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

The book Honeybee Democracy, published in 2010, has been sitting on my shelf for many years. Getting back into the topic of disagreement, I’ve finally read it. And browsing media articles about the book from back then, they just don’t seem to get it right. So let me try to do better.

In late spring and early summer, … colonies [of ordinary honeybees] become overcrowded … and then cast a swarm. … About a third of the worker bees stay at home and rear a new queen … while two-thirds of the workforce – a group of some ten thousand – rushes off with the old queen to create a daughter colony. The migrants travel only 100 feet or so before coalescing into a beardlike cluster, where they literally hang out together for several hours or a few days. .. [They then] field several hundred house [scouts] to explore some 30 square miles … for potential homesites. (p.6)

These 300-500 scouts are the oldest most experienced bees in the swarm. To start, some of them go searching for sites. Initially a scout takes 13-56 minutes to inspect a site, in part via 10-30 walking journeys inside the cavity. After inspecting a site, a scout returns to the main swarm cluster and then usually wanders around its surface doing many brief “waggle dances” which encode the direction and distance of the site. (All scouting activity stops at night, and in the rain.)

Roughly a dozen sites are discovered via scouts searching on their own. Most scouts, however, are recruited to tout a site via watching another scout dance about it, and then heading out to inspect it. Each dance is only seen by a few immediately adjacent bees. These recruited scouts seem to pick a dance at random from among the one’s they’ve seen lately. While initial scouts, those not recruited via a dance, have an 86% chance of touting their site via dances, recruited scouts only have a 55% chance of doing so.

Once recruited to tout a site, each scout alternates between dancing about it at the home cluster and then returning to the site to inspect it again. After the first visit, re-inspections take only 10-20 minutes. The number of dances between site visits declines with the number of visits, and when it gets near zero, after one to six trips, the bee just stops doing any scouting activity.

This decline in touting is accelerated by direct conflict. Bees that tout one site will sometimes head-butt (and beep at) bees touting other sites. After getting hit ten times, a scout usually quits. (From what I’ve read, it isn’t clear to me if any scout, once recruited to tout a site, is ever recruited again later to tout a different site.)

When scouts are inspecting a site, they make sure to touch the other bees inspecting that site. When they see 20-30 scouts inspecting a site at once, that generally implies that a clear majority of the currently active touting scouts are favoring this site. Scouts from this winning site then return to the main cluster and make a special sound which declares the search to be over. Waiting another hour or so gives enough time for scouts to return from other sites, and then the entire cluster heads off together to this new site.

The process I’ve described so far is enough to get all the bees to pick a site together and then go there, but it isn’t enough to make that be a good site. Yet, in fact, bee swarms seem to pick the best site available to them about 95% of the time. Site quality depends on cavity size, entrance size and height, cavity orientation relative to entrance, and wall health. How do they do pick the best site?

Each scout who inspects a site estimates its quality, and encodes that estimate in its dance about that site. These quality estimates are error-prone; there’s only an 80% chance that a scout will rate a much better site as better. The key that enables swarms to pick better sites is this: between their visits to a site, scouts do a lot more dances for sites they estimate to be higher quality. A scout does a total of 30 dances for a lousy site, but 90 dances for great site.

And that’s how bee swarms argue, re picking a new site. The process only includes an elite of the most experienced 3-5% of bees. That elite all starts out with no opinion, and then slowly some of them acquire opinions, at first directly and randomly via inspecting options, and then more indirectly via randomly copying opinions expressed near them. Individual bees may never change their acquired opinions. The key is that when bees have an opinion, they tend to express them more often when those are better opinions. Individual opinions fade with time, and the whole process stops when enough of a random sample of those expresssing opinions all express the same opinion.

Now that I know all this, it isn’t clear how relevant it is for human disagreement. But it does seem a nice simple example to keep in mind. With bees, a community typically goes from wide disagreement to apparent strong agreement, without requiring particular individuals to ever giving up their strongly held opinions.

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