Monthly Archives: January 2022

Life in 1KD

Years ago I read Flatland and Planiverse, stories set in a two-dimensional universe. To me these are the epitome of “hard science fiction”, wherein one makes one (or a few) key contrary assumptions, and then works out their physical and social consequences. I’ve tried to do similarly in my work on the Age of Em and the Hardscrapple Frontier.

Decades ago I thought: why not flip the dimension axis, and consider life in a thousand spatial dimensions? I wrote up some notes then, and last Thursday I was reminded of Flatland, which inspired me to reconsider the issue. Though I couldn’t find much prior work on what life is like in this universe, I feel like I’ve been able to quickly guess many plausible implications in just a few days.

But rather than work on this in secret for more months or years, perhaps with a few collaborators, I’d rather show everyone what I have now, in the hope of inspiring others to contribute. This seems the sort of project on which we can more easily work together, as we less need to judge the individual quality of contributors; we can instead just ask each to “prove” their claims via citations, sims, or math.

Here is what I have so far. I focus on life made out of atoms, but now in a not-curved unlimited space of dimension D=1024 (=2^10), plus one time dimension. I assume that some combination of a big bang and hot stars once created hot dense plasmas with equal numbers of electrons and protons, and with protons clumped into nuclei of varying sizes. As the universe or star regions expanded and cooled, photons bound nuclei and electrons into atoms, and then atoms into molecules, after which those clumped into liquids or solids. Molecules and compounds first accreted atoms, then merged with each other, and finally perhaps added internal bonds.

A cubic array of atoms of length L with as many surface as interior atoms satisfies (L/(L-2))^D = 2, which for D = 1024 gives L = 2956. Such a cube has (2956)^1024 atoms in total. As I hereby define 2^(2^10) to be “crazy huge” and 2^(-2^10) to be “crazy tiny”, this is a more than crazy huge array. (“Crazy huge” is ~100K times a “centillion”. “Astronomical” numbers are tiny by comparison to these.)

We thus conclude that solids or liquids substantially smaller than crazy huge have almost no interiors; they are almost all surface. If they are coupled strongly enough to a surrounding volume of uniform temperature or pressure, then they also have uniform parameters like that. Thus not-crazy-huge objects can’t have separated pipes or cavities. Stars with differing internal temperatures must also be extra crazy huge.

The volume V(r,D) of a sphere of radius r in D dimensions is V = r^D pi^(D/2) / (D/2)!. For dimensions D = (1,2,3,8,24), the densest packing of spheres of unit radius is known to be respectively (0.5,0.28,0.18,0.063,1) spheres per unit volume. The largest D for which this value is known is 24, where the sphere volume fraction (i.e., fraction of volume occupied by spheres) is V(1,24) ~= 1/518. If we assume that for D=1024 the densest packing is also no more than one unit sphere per unit volume, then the sphere volume fraction there is no more than V(1,1024) = 10^-912. So even when atoms are packed as closely as possible, they fill only a crazy tiny fraction of the volume.

If the mean-free path in a gas of atoms of radius r is the gas volume per atom divided by atom collision cross-section V(2r,D-1), and if the maximum packing density for D=1024 is one atom of unit radius per unit volume, then the mean free path is 10^602.94. It seems that high dimensional gases have basically no internal interactions. I worry that this means that the big bang doesn’t actually cause nuclei, atoms, and molecules to form. But I’ll assume they do form as otherwise we have no story to tell.

Higher dimensions allow far more direction and polarization degrees of freedom for photons. The generalized Stefan-Boltzmann law, which says the power is radiated by a black body at temperature T, has product terms T^(D+1), (2pi^0.5)^(D-1), and Gamma(D/2), all of which make atoms couple much more strongly to photons. Thus it seems high D thermal coupling is mainly via photons and phonons, not via gas.

Bonds between atoms result from different ways to cram electrons closer to atomic nuclei. In our world, ionic bonds come from moving electrons from higher energy orbital shells at one atom into lower energy shells at other atoms. This can be worth the cost of giving each atom a net charge, which then pulls the atoms together. Covalent bonds are instead due to electrons finding configurations in the space between two atoms that allow them to simultaneously sit in low shells of both atoms. Metallic bonds are covalent bonds spread across a large regular array of atoms.

Atoms seem to be possible in higher dimensions. Electrons can have more degrees of spin, and there are far more orbitals all at the lowest energy level around nuclei. Thus nuclei would need to have very large numbers of protons to fill up all the lowest energy levels. I assume that nuclei are smaller than this limit. Thus different types of atoms become much more similar to each other than they are in our D=3 universe. There isn’t a higher shell one can empty out to make an ionic bond, and all of the covalent bonds have the same simple spatial form.

The number of covalent bonds possible per atom should be < ~3*D, and B < ~D-10 creates a huge space of possible relative rotations of bonds. Also, in high dimensions the angles between random vectors are nearly right angles. Furthermore, irregularly-shaped mostly-surface materials don’t seem to have much scope for metallic bonds. Thus in high dimensions most atom bonding comes from nearly right angle covalent bonds. Which if they form via random accretion creates molecules in the shape of spatial random walks of bonds in 1024 dimensions.

It is hard to imagine making life and complex machines without making rigid structures. But rigid structures require short loops in the network of bonds, and for high D these seem unlikely to form due to random meetings of atoms in a gas or liquid; other random atoms would bond at a site long before nearby connected atoms got around to trying.

If a network of molecular bonds between N atoms has no loops, then it is a tree, and thus has N-1 bonds, giving less than two bonds per atom on average. But for P>>2, this requires almost all potential bonds to be unrealized. Thus if most atoms in molecules have P>>2 and most potential bonds are realized, those molecules can’t be trees, and so must have many loops. So in this case we can conclude that molecular bond loops are typically quite long. (How long?) Also, the most distinctive types of atoms are those with P =1,2, as enough of these can switch molecules between being small and very large.

Molecules with only long loops allow a lot of wiggling and reshaping along short stretches, and only resist deformations only on relatively large scales. And when many atoms with B < D-2 are close to each other, most neighboring atoms will not be bonded, and can thus slide easily past each other. Thus on the smallest scales natural objects should be liquids, not solids nor metals. And in a uniform density fluid of atoms that randomly forms local bonds as it cools, the connectivity should be global, extending across the entire expanded-and-cooled-together region.

Perhaps short molecular loops might be produced by life-like processes wherein some rare initial loops catalyze the formation of other matching loops. However, as it seems harder to form higher dimensional versions, perhaps life structures are usually low dimensional, and so must struggle to maintain the relative orientation of the “planes” of their different life parts. Life made this way might envy our ease of creating bond loops in low spatial dimensions; did they create our universe as their life utopia?

We have yet to imagine how to construct non-crazy-huge machines and signal processing devices in such a universe. What are simple ways to make wires, gates, levers, joints, rotors, muscles, etc.? Could the very high D space of molecule vibrations be used to good effect? Copying the devices in our universe by extending them in all dimensions is possible but often results in crazy huge objects. Nor do we know what would be the main sources of negentropy. Perhaps gravity clumping, or non-interacting materials that drift out of equilibrium as the universe expands?

The dynamics of a uniformly expanding universe is described by a scale factor a(t), which says how far things have spread apart at each time. For a matter-dominated universe a(t) goes as t^(2/(D-1)), and for a radiation-dominated universe a(t) goes as t^(2D/((D-1)(D+1)). For matter, density goes as a(t)^-D, while for radiation it goes as a(t)^-(D+1). In both cases, we have density falling as t^-2D/(D-1), which is roughly t^-2 for large D. Thus as a high D universe expands, its density falls in time much like it does in low D, but its distances increase far more slowly. There is little expansion-based redshift in high D.

When an expanding region cools enough for molecules to connect across long distances, its further expansion will tend to pull molecular configurations from initially random walks in space more toward long straight lines between key long-loop junctures. This makes it easier for phonons to travel along these molecules, as bond angles are no longer nearly right angles. For the universe, this added tension is not enough to kick it into an exponentially expanding mode; instead the expansion power law changes slightly. Eventually the tension gets large enough to break the atomic bonds, but this takes a long time as widths change only slowly with volumes in high D. (What are typical diameters of the remaining broken molecules?)

As the universe ages, the volume and amount of stuff that one could potentially see from any one vantage point increases very rapidly, like t^(D-1). However, the density or intensity of any emissions that one might intercept also falls very fast as distance d via d^-(D-1), making it hard to see anything very far. In high dimensions it is extremely hard to have a comprehensive view of everything in all directions, and also very hard to see very far in any one direction, even if you focus all of your attention there.

When two powers have a physical fight in this universe, their main problem seems to be figuring out their relative locations and orientation. It might be easy to send a missile to hit any particular location, and nearly impossible for the target to see such a missile coming or to block its arrival. But any extended object probably does not know very well the locations or orientations of its many parts, nor is it even well informed about most of the other objects which it directly touches. It knows far less about objects even a few atom’s width away in all directions. So learning the locations of enemies could be quite hard.

Finding good ways to learn locations and orientations, and to fill and update maps of what is where, would be major civilization achievements. As would accessing new sources of negentropy. Civilizations should also be able to expand in space at a very rapid t^(D-1) speed.

A high D universe of trivial topology and any decent age encompasses crazy huge volumes and numbers of atoms. The origin of life becomes much less puzzling in such a universe, given the crazy huge number of random trials that can occur. It should also be easy to move a short distance and then quickly encounter many huge things about which one had very little information. One has not seen it nor heard about them via one’s network of news and talk. This creates great scope not only for adventure stories, but also for actual personal adventure.

I’ve only scratched the surface here of all the questions one could ask, and some of my answers are probably wrong. Even so, I hope I’ve whetted your appetite for more. If so, please, figure something out about life in 1KD and tell the rest of us, to help this universe come more sharply into view. In principle our standard theories already contain the answers, if only we can think them through.

Thanks to Anders Sandberg and Daniel Martin for comments.

Added 1Feb: One big source of negentropy for life to consume is all of the potential bonds not made into actual bonds on surface atoms. Life could try to carefully assemble atoms into larger dimensional structures with fewer surface atoms.

Added 2Feb: In low D repulsive forces can be used to control things, but in high D it seems that only attractive forces are of much use.

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Me on Tyler on Bryan on Labor

Bryan Caplan has a new book, Labor Econ Versus the World, a collection of his blog posts on the topic. Tyler Cowen says that while “I agree with a great deal of what is in this book, … let’s focus on where we differ”:

Bryan for instance advocates open borders (for all countries?). I think that would be cultural and political suicide, most of all for smaller countries, but for the United States too. You would get fascism first, if anything.

That seems a crazy extreme claim to me. First you’d get lots of immigrants! Then a big economic boom. Any fascism would come much later, and I doubt it would ever come, at least as a result of immigration. (Fascism is pretty rare for US-like places.)

Bryan on education, he believes most of higher education is signaling. In contrast, I see higher education as giving its recipients the proper cultural background to participate in labor markets at higher productivity levels. I once wrote an extensive blog post on this. That is how higher education can be productive, while most of your classes seem like a waste of time.

[From that 471 word “extensive” blog post:] By choosing many years of education, you are telling yourself that you stand on one side of the social divide. The education itself drums that truth into you.

Note how much they agree; both say the usual “material” taught in school isn’t worth much. It is not crazy to think school adds value by pushing modern work culture into students. But it is harder to believe that such a process needs to extend past high school; can the extra years of college and graduate school really be essential to such cultural transmission? Most cultures in human history have finished pushing their culture onto kids well before age 18. Seems more plausible to me that these later years of school are mostly about showing that you embody modern work culture.

[Bryan:] Unless government requires discrimination, market forces make it a marginal issue at most. Large group differences persist because groups differ largely in productivity.

I would instead stress that most of the inequity occurs upstream of labor markets, through the medium of culture. It is simply much harder to be born in the ghetto! … Bryan is not paying enough attention to what is upstream of labor markets, or to how culture shapes human decisions. …

On poverty, Bryan puts forward a formula of a) finish high school, b) get a full time job, and c) get married before you have children. All good advice! But I find that to be nearly tautologous as an explanation of poverty. To me, the deeper and more important is why so many cultures have evolved to make those apparent “no brainer” choices so difficult for so many individuals. … One simple question is why some cultures don’t produce enough men worth marrying, … once you incorporate these messy “cultural upstream” issues, much of labor economics becomes more complicated than Bryan wishes to acknowledge. Much more complicated.

So Tyler doesn’t disagree at all with Bryan on these topics; Tyler instead complains that Bryan’s book on labor econ doesn’t spend enough time on topics outside of labor econ. I think Bryan sees himself correctly has not having much useful advice to offer on how to change cultures, and also sees culture as influencing action largely via the channel of preferences. He thinks it often okay to blame people for choices that result from from their preferences, and to let them suffer consequences from such choices.

If many labor market outcome differences result from differing preferences that result in part from different cultures, then how exactly can outsiders help someone else’s culture change the preferences that it induces? One simple approach is cultural imperialism: actively suppress insider culture and forceable replace it with outsider culture. Such as via school. Another approach is to induce stronger culture competition and selection, such as was once induced by frequent wars.

These approaches are now widely repudiated. But what other plausible options are on the table? I don’t blame Bryan for not offering more concrete advice to solve such a very hard problem in a book on a different topic. I do blame Tyler for complaining that Bryan hasn’t offered a solution to a problem to which Tyler also offers no solution. He just says the topic is “complicated”. Which along with “let’s have a conversation on X” is a usual way to “talk” about a hard subject X without really saying much.

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Discussion Contests

My last post outlined how to make a better “sport” wherein people compete on, and are ranked by, their ability to persuade audiences of claims. Which might be a nice way to find/make sales-folk.

But what I’d really like is to find/make people good at informative discussion. That is, we the audience want to listen to people who are good at taking the floor of our attention and talking so as to more rapidly move our estimates toward higher-confidence values. And we want this more for the case where we are a reasonable rational audience, relative to our being easily swayed by demagoguery. We want to listen to people who will more rapidly change our reasonable minds.

Here’s an idea using betting markets. Imagine a topic for which we will later have some ex post objective measure of truth. We can thus create (possibly subsidized) betting markets over this space of outcomes. Also imagine having some info weights regarding different possible probability distribution over outcomes. Using these weights, we can create a single number saying how informative are any given set of prices. Thus we can say how much info was added (or subtracted) to those prices during any given time period.

So if we have a center of attention “stage” wherein one speaker talks at a time, and if the audience participates in a betting market while they listen, then we can get a measure of the info added by each speaker while they spoke. So we can score each speaker on their info given per second of talking.

Okay, yes, there may be a delay between when a speaker says something and when a listener comes to realize its implications and then makes a resulting market trade. This is a reason to have speakers talk for longer durations, so that their score over this duration can include this delayed realization effect.

Now one way to use this is debate style. Give each speaker the same amount of total time, in the same-length time blocks, and see which one added the most info by the end. Repeat in many pairwise contests. But another approach is to instead just pay to try to get the most info out of any given set of potential speakers.

Imagine an auction for each short period of speaking. If you bid the most per second, you get to the center stage to talk, and then you will be paid in proportion to the info you end up contributing, according to market price changes. Speakers could bid on themselves, or investors might pay for speaker bids. (Let speakers bid for future time periods long enough to include the delayed realization effect.)

Even if there were other sources of info possible, besides this center stage, this auction would still give a credible reason for most of the audience to pay some attention to the center stage. After all, the auction would have selected for the one person expected to be most worth listening to, at least on average.

So now, to induce an informative discussion on a topic, one both subsidizes prediction markets on that topic, and commits to pay each person who wins an auction to speak from a center stage a reward proportional to the info added to those prediction markets while they speak.

What if different time periods are expected to add different amounts of info to the market prices through channels other than the center stage speaker? This could bias the debate structure, but isn’t a problem for the auction structure. Auction bidders would bid more for those extra info time periods, but the winner would still be the speaker expected to add the most info.

This should be pretty easy to test in lab experiments. Who wants to help set them up?

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New Sport of Debate?

Someone recently told me “Hey, you seem good at debate.” Which made me think “Yeah, the world needs more debate. Let’s design a better online debate forum.” Here’s an initial concept sketch.

Audience – These are people allowed to propose and rate debate claims, to propose matches, and to rate performance in them. Each declares their acceptable languages and formats (e.g., text, audio, video). Maybe want to ensure each human can only vote once per issue. To rate a debate, maybe they need to show that they heard the debate live.

Claim – A list of possible claims to debate. Are some topics off limits? Do editors curate the list to edit wordings and cut redundancies?

Debaters – People who have volunteered to debate particular claims. Each one can say which sides (pro or con) of which claims they would defend, in what languages and formats, at what day/times, and who they refuse to debate. (Can “math heavy” or “stat heavy” be languages?)

Debates – Two (or four?) participants publicly debate a given claim online at a given pre-announced time, in a given language and format, with some way to allocate speaking time roughly equally between participants. (Maybe Equatalk?) Some rule decides if debate is cancelled or postponed due to no-shows or health/tech/etc. issues.

Civility – Some process rules, e.g., if debaters can hurl insults, or introduce links for audience to check.

Opinions – Each audience member at a debate gives degree(s?) of support for the claim just before and just after the debate. Maybe state opinions before they know debate participants?

Matching – A process (algorithm?) to pick who debates whom when on what claim in what language, based on the claims that debaters have selected, debater ranks, popularity of claims and matches, and audience participation rates. Maybe do this to max predicted future debate audiences, or info to adjust rankings, or info that changes opinions.

Ranking – A process (algorithm?) to rank value (plus uncertainty?) of each debater, relative to others, based on no-show rates and the opinions expressed at their debates. Maybe opinions of higher ranked debaters count more. Maybe more debates, or being willing to debate more claims, counts more. Ideally the ranking rule is simple, public, and robust to criticism.

Seems the next step here is to propose, critique, and choose more specific rules. Then someone can write or adapt software.

I see big gains from such a forum becoming popular. A good debate forum could become an alternate credentialing framework, to show that some people are good at real debate. (Not like those fake high school debates.) Maybe some new kinds of schools would form to teach people how to do well in such debates.

A related forum might rate participants more in terms of how well they “discuss” claims, and less in terms of persuading an audience toward some pre-defined conclusion. Maybe rate each on how much they moved audience members in any directions, as proxy for being informative? The big question there seems to me: how can we do that rating, and who gets more weight in such ratings.

 

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Medical Doubts OpEd

An editor asked me to write this OpEd, but then he never responded when I gave it to hm. So I submitted it to several other editors, but now I’m out of contacts to try. So I’m giving up and posting this here: Continue reading "Medical Doubts OpEd" »

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Exploring Value Space

If you have enough of a following, Twitter polls are a great resource for exploring how people think. I’ve just finished asking a 8 polls each regarding 12 different questions that make people choose between the following 16 features, either in themself or in others:

attractiveness, confidence, empathy, excitement, general respect, grandchildren, happiness, improve world, income, intelligence, lifespan, pleasure, productive hrs/day, professional success, serenity, wit.

The questions were, in the order they were asked (links give more detail):

  1. UpSelf: Which feature of you would you most like to increase by 1%?
  2. Advice: For which feature do you most want a respected advisor’s advice?
  3. ToMind: Which feature of yourself came to your mind most recently?
  4. WorkedOn: Which feature did you most try to improve in the last year?
  5. UpOthers: Which feature of your associates would you most like to increase by 1%?
  6. City: To which city would you move, options labeled by the feature that people there are on average better on?
  7. KeepSelf: If all your features are to decline a lot, which feature would you save from declining?
  8. Aliens: What feature would you use to decide which civilization survives?
  9. Voucher: On which feature would you spend $10K to improve?
  10. World: Which feature of yours would you most like to improve to become world class?
  11. Obit: Which feature would you feel proudest to have mentioned in your obituary?
  12. KeepOthers: If all of your closest associates’ features will decline a lot, which feature would you save from declining?

Each poll gives four options, and for each poll I fit the response % to to a simple model where each feature has a positive priority, and each feature is chosen in proportion to its priority. The max priority feature is set to have priority 100. And here are the results:

This shows, for each question, the average number who responded to each poll, the RMS error of the model fit, in percentage points, and then the priorities of each feature for each question. Notice how much variation there is in priorities for different questions. Overall, intelligence is the clear top priority, while grandkids is near the bottom. What would Darwin say?

Here are correlations between these priorities, both for features and for questions:

Darker colors show higher correlations. Credit to Daniel Martin for making these diagrams, and to Anders Sandberg for the idea. We have ordered these by hand to try to put the stronger correlations closer to the diagonal.

Notice that both features and questions divide neatly into self-oriented and other-oriented versions. That seems to be the main way our values vary: we want different internal versus external features, and different features in ourselves versus others.

Added 20Jan: Some observations:

There are three packages of features, Impressive, Feelings, and Miscellaneous, plus two pretty disconnected features, intelligence and grandkids. It is striking that grandkids is so weak a priority, and negatively correlated with everything else; grandkids neither make us feel better, nor look impressive.

The Impressive package includes: attractiveness, professional success, income, confidence, and lifespan. The inclusion of lifespan in that package is surprising; do we mainly want to live longer to be impressive, not to enjoy the extra years? Also note that intelligence is only weakly connected with Impressive, and negatively with Feelings.

The Feelings package includes: serenity, pleasure, happiness, and excitement. These all make sense together. The Miscellaneous set is more weakly connected internally, and includes wit, respect, empathy, and improve world, which is the most weakly connected of the set. Empathy and respect are strongly connected, as are wit and excitement. Do we want to be respected because we can imagine how others feel about us, or are we empathetic because that is a “good look”?

There are two main packages of questions: Self and Other. The Other package is UpOthers, City, Aliens, and KeepOther, about what we want in associates. The Self package is Voucher, World, ToMind, WorkedOn, and Advice, about how we choose to improve ourself. UpSelf and KeepSelf are connected but less so, which I interpret as being more influenced by what we’d like others to think we care about.

KeepSelf and KeepOther are an intermediate package, influenced both by what we want in ourselves and what we’d like others to think we care about. Thus what we want in others is close to what we’d like others to think we want in ourselves. It seems that we are more successfully empathetic when we think about the losses of others, rather than their gains. We can more easily feel their pain than their joy.

Obit is more connected to the Other than the Self package, suggesting we more want our Obits to contain the sorts of things we want in others, rather than what we want in ourself. 

Note that while with features the Impressive and Feelings packages are positively correlated, for Questions the Self and Other questions are negatively correlated. Not sure why.

 

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Best Case Contrarians

Consider opinions distributed over a continuous parameter, like the chance of rain tomorrow. Averaging over many topics, accuracy is highest at the median, and falls away for other percentile ranks. This is bad news for contrarians, who sit at extreme percentile ranks. If you want to think you are right as a contrarian, you have to think your case is an exception to this overall pattern, due to some unusual feature of you or your situation. A feature that suggests you know more than them.

Yet I am often tempted to hold contrarian opinions. In this post I want to describe the best case for being a contrarian. I’m not saying that most contrarians are actually in this best case. I’m saying that this is the case you most want to be in as a contrarian, as it can most justify your position.

I recently posted on how innovation is highest for more fragmented species, as species so often go wrong via conformity traps. For example, peacocks are now going wrong together with overly long tails. To win their local competitions, each peacock needs to have and pick the tails that are sexy to other peacocks, even if that makes them all more vulnerable to predators.

Salmon go wrong by having to swim up hard hazard-filled rivers to get to their mating groups. Only a third of them survive to return from that trip. Now imagine a salmon sitting in the ocean at the mouth of the river, saying to the other salmon:

We are suffering from a conformity trap here. I’m gonna stay and mate here, instead of going up river. If you stay here and mate with me, then we can avoid all those river hazards. We’ll survive, with more energy to help our kids, and win out over the others. Who’s with me?

Now salmon listening to his should wonder if genetic losers are especially likely to make such contrarian speeches. After all, they are the least likely to survive the river, and so the most desperate to avoid it. For all its harms, the river does function to sort out the salmon with the best genes. If you make it to the end, you know your mating partner will also be unusually fit.

So yes, those less likely to pass the river test are more likely to become salmon contrarians. But they aren’t the only ones. Also more likely are:
A) those who can better sort good from bad mates in other ways,
B) those who can better see the conformity traps, and see they are especially big,
C) those who can better see which are the best places to start alternatives to the conformity traps, and
D) those who happen to have invested less in, and thus are less tied to, existing traps. Like the young.

Our world suffers from myriad conformity traps. Like investors who must coordinate with other investors (e.g., via the different levels of venture capital), may feel they must do crypto, as that’s what the others are doing. Even if they don’t think that much of crypto. Like academics in fields that use too much math feel they also need to do too much math if they are to be respected there. Like journalists and think tank pundits feel they must write on the topics on which everyone else is talking, even if other topics are more important.

In all of these cases, it can make sense to try to initiate a contrarian alternative. If many others know about the existing conformity traps, they may also be looking for a chance to escape. The questions are then: when is the right time and place to initiate a contrarian move to escape such a trap. Who is best place to initiate, and how? And, what is the ratio of the gains of success to the costs of failure?

In situations like this, the people who actually try contrarian initiatives may not be at all wrong on their estimates about the truth. They will be different in some ways yes, but not necessarily overall on truth accuracy. In fact, they are likely to be more informed on average in the sense of being better able to judge the overall conformity trap situation, and to evaluate partners in unusual ways.

That is, they can better judge how bad is the overall conformity trap, where are promising alternatives, and who are promising partners. Even if, yes, they are also probably worse on average at winning within the usual conformity-trapped system. Compared to others, contrarians are on average better at being contrarians, and worse at being conformists. Duh.

And that’s the best case for being a contrarian. Not so much because you are just better able to see truth in general. But because you are likely better in particular at seeing when it is time to bail on a collective that is all going wrong together. If the gains from success are high relative to the costs of failure, then most such bids should fail, making the contrarian bid “wrong” most of the time. But not making most bids themselves into mistakes.

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Much Talk Is Sales Patter

The world is complex and high dimensional. Even so, it sometimes helps to try to identify key axes of variation and their key correlates. This is harder when one cannot precisely define an axis, but merely gesture toward its correlates. Even so, that’s what I’m going to try to do in this post, regarding a key kind of difference in talk. Here are seven axes of talk:

1. The ability to motivate. Some kinds of talk can more move people to action, and fill people with meaning, in ways that other kinds of talk do not. In other kinds of talk, people are already sufficiently moved to act, and so less seek such added motivation.

2. The importance of subtext and non-literal elements of the talk, relative to the literal surface meanings. Particular words used, rhythms, sentence length, images evoked, tone of voice, background music, etc. Who says it, who listens, who overhears. Things not directly or logically connected to the literal claims being made, but that matter nonetheless for that talk

3. Discussion of, reliance on, or connection to, values. While values are always relevant to any discussion, for some topics and context there are stable and well accepted values at issue, so that value discussions are just not very relevant. For other topics value discussion is more relevant, though we only rarely every discuss them directly. We are quite bad at talking directly about values, and are reluctant to do so. This is a puzzle worth explaining.

4. Subjective versus objective view. Some talk can be seen as making sense from a neutral outside point of view, while other talk mainly makes sense from the view of a particular person with a particular history, feelings, connections, and concerns. They say that much is lost in trying to translate from a subjective view to an objective view, though not in the other direction.

5. Precision of language, and ease of abstraction. On some topics we can speak relatively precisely in ways that make it easy for others to understand us very clearly. Because of this, we can reliably build and share precise abstractions of such concepts. We can learn things, and then teach others by telling them what we’ve learned. Our most celebrated peaks of academic understanding are mostly toward this end of this axis.

6. Some talk is riddled with errors, lies, and self-deceptions. If you go through it sentence by sentence, you find a large fraction of misleading or wrong claims. In other kinds of talk, you’d have to look a long time before you found such errors.

7. Talk in the context of a well accepted system of thought. Like physics, game theory, etc. Where concepts are well defined relative to each other, and with standard methods of analysis. As opposed to talk wherein the concept meanings are still up for grabs and there are few accepted ways to combine and work with them.

It seems to me that these seven axes are all correlated with each other. I want to postulate a single underlying axis as causing a substantial fraction of that shared correlation. And I offer a prototype category to flag one end of this axis: sales patter.

The world is full of people buying and selling, and a big fraction of the cost of many products and services goes to pay for sales patter. Not just documents and analyses that you could read or access to help you figure out which versions are betting quality or better suited to your needs. No, an actual person standing next you being friendly and chatting with you about the product or whatever else you feel like.

You can’t at all trust this person to be giving you neutral advice. Even if you do come to “trust” them. And their sales patter isn’t usually very precise, integrated into systems of analysis, or well documented with supporting evidence. It is chock full of extra padding, subtext, and context that influences without being directly informative. It is even full of lies and invitations to self-deception. Even so, it actually motivates people to buy. And thus it must, and usually does, connect substantially to values. And it is typically oriented to the subjective view of its target.

At the opposite end of the spectrum from sales patter is practical talk in well defined areas where people know well why they are talking about it. And already have accepted systems of analysis. Consider as a prototypical example talk about how to travel from A to B under constraints of cost, time, reliability, and comfort. Or talk about the financial budget of some organization. Or engineering talk about how to make a building, rebuild a car engine, or write software.

In these areas our purposes and meanings are the simplest and clearest, and we can usefully abstract the most. And yet people tend to pick from areas like these when they offer examples of a “meaningless” existence or soul-crushing jobs. Such talk is the most easily painted by non-participants as failing to motivate, and being inhuman, the result of our having been turned into mindless robots by mean capitalists or some other evil force.

The worlds of such talk are said to be “dead”, “empty”, “colorless”, and in need of art. In fact people often justify art as offering a fix for such evils. Art talk, and art itself, is in fact much more like sales patter, being vague, context dependent, value-laden, and yet somehow motivating.

There’s an awful lot of sales talk in the world, and a huge investment goes into creating it. Yet there are very few collected works of the best sales patter ever. Op-eds are a form of sales talk, as is romantic seduction talk, but we don’t try to save the best of those. That’s in part because sales patter tends to be quite context dependent. It also doesn’t generalize very well, and so there are few systems of thought built up around it.

So why does sales patter differ in these ways from practical systematic talk? My best guess is that this is mostly about hidden motives. People don’t just want to buy stuff, they also like to have a relation with a particular human seller. They want sellers to impress them, to connect to them, and to affirm key cherished identities. All from their personal subjective point of view. They also want similar connections to artists.

But these are all hidden motives, not to be explicitly acknowledged. Thus the emphasis on subtext, context, and subjectivity, which make such talk poor candidates for precision and abstraction. And the tolerance for lies and self-deception in the surface text; the subtext matters more. Our being often driven by hidden motives makes it hard for us to talk about values, since we aren’t willing to acknowledge our true motives, even to ourselves. To claim to have some motives while actually acting on others, we can’t allow talk about our decisions to get too precise or clear, especially about key values.

We keep clear precise abstract-able talk limited to areas where we agree enough on, and can be honest enough about, some key relevant values. Such as in traveling plans or financial accounting. But these aren’t usually our main ultimate values. They are instead “values” derived from constraints that our world imposes on us; we can’t spend more money than we have, and we can’t jump from one place to another instantly. Constraints only motivate us when we have other more meaningful goals that they constrain. But goals we can’t acknowledge or look at directly.

If, as I’ve predicted, our descendants will have a simple, conscious, and abstract key value, for reproduction, they will be very different creatures from us.

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My Old Man Rant

As a 62 year old man, I think I’m entitled to rant once in a while. But instead of “you kids get off my lawn!”, this is my rant:

In principle, economics can help advise most any decisions, like when to wake up, or whether to own a second car. But there are fixed costs to doing explicit econ analysis, and also persuasion costs when you try to influence the decisions of some audience. Thus econ analysis seems most valuable for the biggest decisions whose the audience respects economists for those decisions. Or perhaps many similar but smaller decisions which can all be analyzed at once in the same framework. As we economists are most known for our work evaluating institutions, and as our institutional choices are some of the biggest ones we have, this all suggests our biggest wins come there.

I was first exposed to economics and libertarianism at the same time, and what most excited me about both were similarities to science fiction: they let me imagine very different social worlds. One could see how we could have very different institutions from our current versions, ones that would also plausibly be better. Yes, one couldn’t be very sure that those worlds would be better. But they gave us new things to try, to test and see if they might be better.

When I was young, theory was king, and I tried to master theory. But since then data has come to be king (and queen), even in econ and libertarian circles. Yet I hadn’t realized just how far that trend had gone until this pandemic. To me the obvious theory question a pandemic raises is: what are good general institutions for dealing with pandemics? I wrote a bit on that early on, but was told then that we instead needed immediate help in a crisis. Which I also tried to offer, but which many hated.

Yet it is now two years into what is looking more and more like an eternal pandemic, and I still haven’t see economists or libertarians talking about better pandemic institutions. While this pandemic has done great damage to libertarian sympathies, I’ve only seen libertarians argue that in this particular pandemic, doing nothing officially would have been better than doing what we did. And I’ve seen economists argue about particular parameter settings of the usual government-run system: rules, subsidies and direct government management of masks, lockdowns, tests, and vaccines. Mostly via data, not theory, analysis.

But I’ve not seen work on if there are better institutional alternatives to these two categories, if not for this pandemic then for future ones. Which to me feels like a deep betrayal of what I most value in econ: our ability to imagine, test, and argue for big institutional changes. Even my immediate (and beloved) colleagues haven’t been interested.

To me, the obvious other category is: law. We are better off having law to deal with many harms we can each do to each other, such as assault, slander, and reneging on contracts. Better than ignoring them, and better than having government agencies more directly manage such behaviors. Yes, our society runs law centrally, and likely law would be better if offered privately. But even so, for many harms we are better off because we now apply law over the other two main solutions of doing nothing officially or direct government management.

For law to work for assault, slander, theft, or car accidents, we need it to be often feasible to bring sufficient evidence to convince a court that a particular person harmed a particular other person to a particular degree at a particular event. If so, we can then sufficiently discourage such harms merely via the threat of such legal penalties. At least if we can sufficiently punish those we find guilty, and if we make it easy enough for complainants to subpoena the evidence they need to make their case.

Law today often ensures sufficient punishment via jail and criminal law, which works even if not as well as would vouchers. Law usually allows parties to subpoena any info relevant to a live case, and it so happens that evidence needed to prove assaults and car accidents lasts long enough to let them be so subpoenaed. With vouchers and the level of surveillance likely soon, I don’t actually think we’d need most of our traffic laws; the threat of lawsuits would be enough.

The main policy problem with pandemics is that some people hurt other people by infecting them. Just like they do in assault, slander, theft, and auto accidents. So law could deal fine with pandemics if we could meet the same two conditions: (1) sufficiently able to punish those who found guilty, e.g. via jail or vouchers, and (2) often enough able to easily-enough subpoena sufficient info to show who did what to whom. It is on that last point that economists, and lawyers, have traditionally thrown up their hands and concluded that law can’t deal with pandemics.

That is, people have just assumed that it is not possible to tell who infected who in a pandemic. At least not often enough for law to be our main way to deal with severe pandemics. So for something like the flu we subsidize vaccines and little else, while for covid we go crazy with government managing many related details.

But today with smartphone tracking we can actually see who was close enough to whom when to have infected them. And if we have spit samples from two people infected with covid, we can compare the DNA in their viruses to see if they match. By combining these two pieces of information, one could make a sufficiently strong case that a particular person infected another particular person with the virus at a particular time and place.

So the question that remains is: should we actually induce sufficient information collection and subpoena power, and sufficient punishment ability, to let law deal with pandemics? That is, on the one hand we might make infecting others a punishable crime, require everyone to have their phone track their locations, to report their infections, and to save regular spit samples. And then let government police pour over these details. Which does sound like a pretty intrusive police state, though perhaps still better than the actual police state we’ve had during this last pandemic.

Or, only during an officially declared severe pandemic we could tell everyone that they must either strictly isolate, or, they can get a “pandemic passport” by agreeing to get a voucher, have their phone track their locations, and regularly save spit samples, all available only to be subpoenaed in case of lawsuits by people who claim to be harmed, but not for general browsing by a police state.

Yes, once a pandemic becomes nearly endemic, frequent infection events could clog up courts. But at such scale vouchers would streamline their processes and settle almost all cases out of court. I also know of ways to greatly cut court costs. And damages awarded might greatly fall once one could credibly argue that the victim would likely have caught it soon from someone else.

This idea of legally requiring people to save info so that it can be available to be subpoenaed for future lawsuits is not a particularly new idea. It is just the application to the case of pandemics that would be new. But in our new world of greatly increased surveillance and info of various sorts, we should in fact be thinking about how all that new info might help us solve problems. Like pandemics. Via new institutional changes

Come on, don’t any economists or libertarians out there want to think about new pandemic institutions?

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My 11 Bets at 10-1 Odds On 10M Covid deaths by 2022

In February 2020, I made many bets on Covid19, including 11 bets at ten to one odds on if it would cause 10 million deaths worldwide by 2022, as estimated by WHO.

WHO has a Q&A page on Covid excess deaths that includes this section:

Why is excess mortality the preferred measure? … aggregate COVID-19 case and death numbers … being reported to WHO … under-estimate the number of lives lost due to the pandemic … In light of the challenges posed by using reported data on COVID-19 cases and deaths, excess mortality is considered a more objective and comparable measure that accounts for both the direct and indirect impacts of the pandemic.

This WHO page, updated daily, lists reported deaths. This WHO page estimated “The true death toll of COVID-19”, or world covid excess deaths, as of Dec. 31, 2020. I expect them to post a page like it soon with death estimates as of Dec. 31, 2021. But I doubt those estimates will differ much from The Economist, which as of Dec. 30, 2021 said:

The pandemic’s true death toll; Our daily estimate of excess deaths around the world … Although the official number of deaths caused by covid-19 is now 5.4m, our single best estimate is that the actual toll is 18.6m people. We find that there is a 95% chance that the true value lies between 11.6m and 21.6m additional deaths.


For many bets we agreed that if there were two number estimates instead of one, we’d go with a geometric mean of them. The geometric mean of 5.4 and 18.6 is 10.02.

Here is the current status of my 11 bets, with a link to the bets and the amount I’m owed. (I’ll update this as things change.)

These claim to win, say I should pay them:

No response since 31Dec:

  • A Twitter msg bet that I’m keeping private for now, $5000

Paid to me:

Some say that it is rude of me to brag about winning. But I need to make this bet situation public in order to pressure bettors to make good on their promises.

Some say it is immoral to bet on death. But I didn’t cause these deaths, and my public bets helped convince many to take this problem more seriously, for which they’ve thanked me.

Added 12Jan: Many are talking as if the issue is direct vs. indirect deaths, but I’d be very surprised if more than a third of excess deaths are indirect. Most of them were caused directly by covid, but just not caught by official testing and diagnosis systems.

Added 18Jan: Nature article:

Demographers, data scientists and public-health experts are striving to narrow the uncertainties for a global estimate of pandemic deaths. … Among these models, the World Health Organization (WHO) is still working on its first global estimate, but the Institute for Health Metrics and Evaluation in Seattle, Washington, offers daily updates of its own modelled results, as well as projections of how quickly the global toll might rise. And one of the highest-profile attempts to model a global estimate has come from the news media. The Economist magazine in London has used a machine-learning approach to produce an estimate of 12 million to 22 million excess deaths.

That IHME 95% confidence interval is 9 to 18 million deaths.

Added 26Jan: This Sept. 2021 PLOS paper says

[In] the United States … in 2020 … there were 375,235 excess deaths, with 83% attributable to direct, and 17% attributable to indirect effects of COVID-19.

Added 9May: WHO finally speaks on 2021 excess deaths:

 

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