High Dimensional Societes?

I’ve seen many “spatial” models in social science. Such as models where voters and politicians sit at points in a space of policies. Or where customers and firms sit at points in a space of products. But I’ve never seen a discussion of how one should expect such models to change in high dimensions, such as when there are more dimensions than points.

In small dimensional spaces, the distances between points vary greatly; neighboring points are much closer to each other than are distant points. However, in high dimensional spaces, distances between points vary much less; all points are about the same distance from all other points. When points are distributed randomly, however, these distances do vary somewhat, allowing us to define the few points closest to each point as that point’s “neighbors”. “Hubs” are closest neighbors to many more points than average, while “anti-hubs” are closest neighbors to many fewer points than average. It turns out that in higher dimensions a larger fraction of points are hubs and anti-hubs (Zimek et al. 2012).

If we think of people or organizations as such points, is being a hub or anti-hub associated with any distinct social behavior?  Does it contribute substantially to being popular or unpopular? Or does the fact that real people and organizations are in fact distributed in real space overwhelm such things, which only only happen in a truly high dimensional social world?

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“Human” Seems Low Dimensional

Imagine that there is a certain class of “core” mental tasks, where a single “IQ” factor explains most variance in such task ability, and no other factors explained much variance. If one main factor explains most variation, and no other factors do, then variation in this area is basically one dimensional plus local noise. So to estimate performance on any one focus task, usually you’d want to average over abilities on many core tasks to estimate that one dimension of IQ, and then use IQ to estimate ability on that focus task.

Now imagine that you are trying to evaluate someone on a core task A, and you are told that ability on core task B is very diagnostic. That is, even if a person is bad on many other random tasks, if they are good at B you can be pretty sure that they will be good at A. And even if they are good at many other tasks, if they are bad at B, they will be bad at A. In this case, you would know that this claim about B being very diagnostic on A makes the pair A and B unusual among core task pairs. If there were a big clump of tasks strongly diagnostic about each other, that would show up as another factor explaining a noticeable fraction of the total variance. Making this world higher dimensional. So this claim about A and B might be true, but your prior is against it.

Now consider the question of how “human-like” something is. Many indicators may be relevant to judging this, and one may draw many implications from such a judgment. In principle this concept of “human-like” could be high dimensional, so that there are many separate packages of indicators relevant for judging matching packages of implications. But anecdotally, humans seem to have a tendency to “anthropomorphize,” that is, to treat non-humans as if they were somewhat human in a simple low-dimensional way that doesn’t recognize many dimensions of difference. That is, things just seem more or less human. So the more ways in which something is human-like, the more you can reasonably guess that it will be human like in other ways. This tendency appears in a wide range of ordinary environments, and its targets include plants, animals, weather, planets, luck, sculptures, machines, and software.

We feel more morally responsible for how we treat more human-like things. We are more inclined to anthropomorphize things that seem more similar to humans in their actions or appearance, when we more desire to make sense of our environment, and when we more desire social connection. When these conditions are less met, we are more inclined to “dehumanize”, that is to treat human things as less than fully human. We also dehumanize to feel less morally responsible for our treatment of out-groups.

One study published in Science in 2007 asked 2400 people to make 78 pair-wise comparisons between 13 characters (a baby, chimp, dead woman, dog, fetus, frog, girl, God, man, vegetative man, robot, woman, you) on 18 mental capacities and 6 evaluation judgements. An “experience” factor explained 88% of capacity variation, being correlated with capacities for hunger, fear, pain, pleasure, rage, desire, personality, consciousness, pride, embarrassment, and joy. This factor had a strong 0.85 correlation with a desire to avoid harm to the character. A second “agency” factor explained 8% of the variance, being correlated with capacities for self-control, morality, memory, emotion recognition, planning, communication, and thought. This factor had a strong 0.82 correlation with a desire to punish for wrongdoing. Both factors correlated with liking a character, wanting it to be happy, and seeing it as having a soul (Gray et al. 2007).

Though it would be great to get more data, especially on more than 13 characters, this study does confirm the usual anecdotal description that anthropomorphizing is essentially a low dimensional phenomena. And if true, this fact has implications for how biological humans would treat ems.

My colleague Bryan Caplan insists that because ems would not be made out of familiar squishy carbon-based biochemicals, humans would feel confident that ems have no conscious feelings, and thus eagerly enslave and harshly treat ems, as Bryan says that our moral reluctance is the main reason why most humans today are not harshly treated slaves. However, this in essence claims the existence of a big added factor explaining judgements related to “human-like”, a factor beyond those seen in the above survey.

After all, “consciousness” is already one of the items included in the above survey. But it was just one among many contributors to the main experience factor; it wasn’t overwhelming compare to the rest. And I’m pretty sure that if one tried to add being made of biochemicals as a predictor of this main factor, it would help but remain only one weak predictor among many. You might think that these survey participants are wrong, of course, but we are trying to estimate what typical people will think in the future, not what is philosophically correct.

I’m also pretty sure that while the “robot” in the study was rated low on experience, that was because it was rated low on capacities like for pain, pleasure, rage, desire, and personality. Ems, being more articulate and expressive than most humans, could quickly convince most biological humans that they act very much like creatures with such capacities. You might claim that humans will all insist on rating anything not made of biochemicals as all very low on all such capacities, but that is not what we see in the above survey, nor what we see in how people react to fictional robot characters, such as from Westworld or Battlestar Galactica. When such characters act very much like creatures with these key capacities, they are seen as creatures that we should avoid hurting. I offer to bet $10,000 at even odds that this is what we will see in an extended survey like the one above that includes such characters.

Bryan also says that an ability to select most ems from scans of the few best suited humans implies that ems are extremely docile. While today when we select workers we often value docility, we value many other features more, and tradeoffs between available features result in the most desired workers being far from the most docile. Bryan claims that such tradeoffs will disappear once you can select from among a billion or more humans. But today when we select the world’s best paid actors, musicians, athletes, and writers, a few workers can in fact supply the entire world in related product categories, and we can in fact select from everyone in the world to fill those roles. Yet those roles are not filled with extremely docile people. I don’t see why this tradeoff shouldn’t continue in an age of em.

Added July 17: Bryan rejects my bet because:

I don’t put much stock in any one academic paper, especially on a weird topic. .. Robin’s interpretation of the paper .. is unconvincing to me. .. How so? Unfortunately, we have so little common ground here I’d have to go through the post line-by-line just to get started. .. a survey .. is probably a “far” answer that wouldn’t predict much about concrete behavior.

That is, nothing anyone says can be trusted on this topic, except Bryan’s intuition. He instead proposes a bet where I pay him up front, and he might pay me at our life end.

Seems to me Bryan disagrees not just with me, but also with the authors of this Science paper, as well as its editors and referees at Science. About what the survey means. But he seems to accept that a similar survey would show as I claim. And since he’s on record to say there isn’t that much difference between a survey and a vote, it seems he must accept this for predicting vote outcomes.

Added July 19: I offer to bet anyone $10K at even odds that in the next published survey with a similar size and care to the one above, but with at least twice as many characters, over 80% of the variance will be explained by two factors, neither of which is focused on the substance (e.g., carbon, silicon) out of which a character is made.

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Boost For Being Best

The fraction of a normal distribution that is six or more standard deviations above the mean is one in ten billion. But the world has almost eight billion people in it. So in principle we should be able to get six standard deviations in performance gain by selecting the world’s best person at something, compared to using an average person.

I’m revising Age of Em for a paperback edition, expected in April. The rest of this post is from a draft of new text elaborating that point, and its implication for em leisure:

Em workers also earn wage premiums when they are the very best in the world at what they do. Even under the most severe wage competition, a best em can earn an extra wage equal to the difference between their productivity and the productivity of the second best em. When clans coordinate internally on wage negotiations, this is the difference in productivity between clans. (Clans who can’t coordinate internally are selected out of the em world, as they don’t cover their fixed costs, such as for training and marketing.)

Out of 10 billion independently and normally distributed (IID) samples, the maximum is on average about 6.4 standard deviations above the mean. Average spacings between the second, third, fourth highest samples are roughly 0.147, 0.075, and 0.05 standard deviations respectively (Branwen 2017). So when ems are selected out of 10 billion humans, the best em clan may be this much better than other em clans on normally distributed parameters. Using the log-normal wage distribution observed in our world (Provenzano 2015), this predicts that the best human in the world at any particular task is four to five times more productive than the median person, is over three percent more productive than the second most productive person, and is five percent more productive than the third most productive person.

If em clan relative productivity is drawn from this same distribution, if maximum em productivity comes at a 70 hour workweek, and if the best and second best em clans do not coordinate on wages they accept, then even under the strongest wage competition between clans, the best clan could take an extra 20 minutes a day more leisure, or two minutes per work hour, in addition to the six minutes per hour and other work breaks they take to be maximally productive.

This 20 minute figure is an underestimate for four reasons. First, the effective sample size of ems is smaller due to age limits on desirable ems. Second, most parameters are distributed so that the tails are thicker than in the normal distribution (Reed and Jorgensen 2004).

Third, differing wealth effects may add to differing productivity effects. On average over the last 11 years, the five richest people on Earth have each been about 10 percent richer than the next richest person. If future em income ratios were like this current wealth ratio, then the best em worker could afford roughly an extra hour per day of leisure, or an additional six minutes per hour.

Fourth, competition probably does not take the strongest possible form, and the best few ems can probably coordinate to some extent. For example, if the best two em clans coordinate completely on wages, but compete strongly with the third best clan, then instead of the best and second best taking 20 and zero minutes of extra leisure per day, they could take 30 and 10 extra minutes, respectively.

Plausibly then, the best em workers can afford to take an additional two to six minutes of leisure per hour of work in a ten hour work day, in addition to the over six minutes per hour of break needed for maximum productivity.

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A Post-Em-Era Hint

A few months ago I noticed a pattern across the past eras of forager, farmer industry: each era has a major cycle (ice ages, empires rise & fall, business cycle) with a period of about one third of that era’s doubling time. So I tentatively suggested that a em future might also have a major cycle of roughly one third of its doubling time. If that economic doubling time is about a month, the em major cycle period might be about a week.

Now I report another pattern, to be treated similarly. In roughly the middle of each past era, a pair of major innovations in calculating and communicating appeared, and gradually went from barely existing to having big social impacts.

  • Forager: At unknown periods during the roughly two million year forager era, humanoids evolved reasoning and language. That is, we became able to think about and say many complex things to each other, including our reasons for and against claims.
  • Farmer: While the farming era lasted roughly 7 to 10 millennia, the first known writing was 5 millennia ago, and the first known math textbooks 4 millennia ago. About 2.5 millennia ago writing became widespread enough to induce major religious changes worldwide.
  • Industry: While the industry era has lasted roughly 16 to 24 decades, depending on how you count, the telegraph was developed 18 decades ago, and the wholesale switch from mechanical to digital electronic communication happened 4 to 6 decades ago. The idea of the computer was described 20 decades ago, the first digital computer was made 7 decades ago, and computers became widespread roughly 3 decades ago.

Note that innovations in calculation and communication were not independent, but instead intertwined with and enabled each other. Note also that these innovations did not change the growth rate of the world economy at the time; each era continued doubling at the same rate as before. But these innovations still seem essential to enabling the following era. It is hard to imagine farming before language and reasoning, nor industry before math and writing, nor ems before digital computers and communication.

This pattern weakly suggests that another pair of key innovations in calculation and communication may appear and then grow in importance across a wide middle of the em era. This era may only last a year or two in objective time, though typical ems may experience millennia during this time.

This innovation pair would be interdependent, not change the growth rate, and perhaps enable a new era to follow. I can think of two plausible candidates:

  1. Ems might discover a better language for expressing and manipulating something like brain states. This could help ems to share their thoughts and use auxiliary hardware to help calculate useful thoughts.
  2. Ems might develop analogues to combinatorial prediction markets, and thus better share beliefs and aggregate information on a wide range of topics.

(Or maybe the innovation produces some combination of these.) Again, these are crude speculations based on a weak inference from a rough pattern in only three data points. But even so, they give us a vague hint about what an age after ems might look like. And such hints are actually pretty hard to find.

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Beware The Moral Spotlight

Imagine a large theatre with a singer at center stage. A single bright spotlight illuminates this singer, and the rest of the crowded theatre is as dark as can be, given this arrangement. Morality can do the same thing in the theatre of our mind. Once one issue or choice gets a strong moral color, we can focus on it so much that we just don’t see a much larger theatre of action. This is fine when our moral sense works well. If one murder were happening in a stadium of 50,000 people, it could make sense for the Jumbotron to project it onto the big screen, and for the whole stadium to focus on it, to help them do something about it.

But our moral sense often doesn’t work well. We are so obsessed with showing off our moral feelings and inclinations, relative to being useful to a larger world, that we neglect large theatres where we could be useful, to obsess with a small circle highlighted by our moral spotlight, where we can’t actually do much. Let me give three examples.

1. Some friends were recently arguing about the motives of CEOs, relative to politicians and heads of government agencies. One person was arguing that people go into government in order to help others, but go into business to make money. Thus it is better, all else equal, for activities to be run by government. Another person argued that real business people have a wide range of motives, as do real government people. But first person pointed to official statements of purpose, claiming that governments say on paper that they are to help people, while businesses say on paper that they are to make money.

But even if business and government people do differ on average in their motives, you don’t get to elite positions in either area without paying close attention to the great many practical constraints that each area imposes. Business people must attend to customer reactions, employee moral, media coverage, etc. Government people must attend to official procedures, voter sentiment, rival factions maneuvering, etc. Elites must usually navigate such treacherous shoals successfully for decades before they are allowed to make big decisions on behalf of any organization.

Those selection pressures are what determine most behavior in both areas. If business or government is better at running activities, it is mostly because of differences in those pressures. Any remaining behavior differences due to fundamental motives being influenced by official statements of purpose must be small by comparison. While your moral spotlight might want to focus on purpose-statement-induced-motives, most of what matters is elsewhere.

2. I recently watched the documentary The Red Pill, which mostly reviewed Men’s Rights Movement arguments that I had encountered decades before in the book The Myth of Male Power. They point out that many official rules and widely held expectations, as well as many concrete typical outcomes, are unfavorable to men. Their talks and meetings have faced rude and violent interference by those who see this as undermining feminist consciousness-raising regarding areas where official rules and widely held expectations have been and to some extent continue to be unfavorable to women.

The conflict seems to come down emotionally to a perception of which sex is getting the worse deal overall. And there may in fact be some truth of that matter; maybe one sex does have a worse deal. But many seem eager to infer the existence of an entire system, e.g., “patriarchy”, designed in detail to achieve this worse-for-one-sex outcome, entrenched via the direct support of malicious people from the favored sex, and implicit support from most of the rest of that sex.

This seems to me to mostly result from a moral spotlight in overdrive. Yes one sex may have a worse deal overall. But most of the ways in which we’ve had sex-assymetric official rules and widely held expectations did not result from a conspiracy by one sex to repress the other. They were mostly reasonable responses to sex differences relevant in ancient societies. We may have failed to adapt them quickly enough to our new modern context. But many of them are still complex and difficult issues. We’d do better to roll up our sleeves and deal with each one, than to obsess over which sex has the worse overall deal.

3. When people think about changes they’d like in the world one of their first thoughts, and one they return to often, is wanting more democracy. It’s their first knee-jerk agenda for China, North Korea, ISIS, and so on. Surely with more democracy all the other problems would sort themselves out.

But in fact scholars can find few consistent difference in the outcomes of nations that depend much on their degree of democracy. Democracy doesn’t seem to cause differences in wealth, or even in most specific policies. Democracies today war a bit less, but in the past democracies warred more than others. Democracies have less political repression, and our moral spotlight finds that fact to be of endless fascination. But it is in fact a relatively small effect on nations overall.

Nations today have huge differences in outcomes, and we are starting to understand some of them. But most of them have little to do with democracy. Plausibly larger issues include urbanization, immigration, foreign trade, regulation, culture, rule of law, corruption, suppression or encouragement of family clans or religion, etc. If you want to help nations, you’ll have to look outside the moral spotlight on democracy.

Yes, why should you personally sacrifice to help the world? The world will reward you for taking a clear moral stance regarding whatever is in the shared moral spotlight. And it will suspect you of immorality and disloyalty if you pay too little attention to that spotlight. So why should you look elsewhere? I think you know.

Added 3 July: Bryan Caplan points out that democracy can reduce the worst excesses of totalitarian governments. I accept that point; I had in mind less extreme variations, so North Korea was a poor choice on my part.

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Intellectuals as Artists

Consider some related phenomena:

  1. Casual conversation norms say to wander across many topics, with each person staying relevant to each current topic. This functions well to test individual impressiveness. Today, academic and mass media conversations today follow similar norms, though they did this much less in the ancient world.
  2. While ancient artists and musicians tried to perfect common styles, modern artists and musicians seek more distinctive personal styles. For example, while songs were once designed to sound good when ordinary folks sang them, now songs are designed to create a unique impressive performance by one artist.
  3. Politicians often go out of their way to do “position taking” on many issues, even on issues they have little chance of influencing policy while in office. Voters prefer systems like proportional representation where voters can identify more closely with particular representatives, even if this doesn’t give voters better outcomes overall. Knowing many of a politician’s positions helps voters to identify with them.
  4. “Sophomoric” thinkers, typically college sophomores, are eager to take positions on as many common topics as possible, even if this means taking poorly consider positions. They don’t feel they are adult until they have an opinion ready for most common intellectual conversations. This is more feasible when opinions on each topic area are reduced to choices between a small number of standard “isms”, offering integrated packages of answers. Sophomoric thinkers love isms.
  5. We often try to extract “isms” out of individuals, such as my colleagues Tyler Cowen or Bryan Caplan. We might ask “What is the Caplanian position on X?” That is, we wonder how they would answer random questions, presuming that we can infer a coherent style from past positions that would answer all future questions, at least within some wide scope. Intellectuals who desire wider attention often go out of their way to express opinions on many topics, chosen via a distinctive personal style.

We pretend that we search only for truth, picking each specific position only via the strongest specific evidence and arguments. And in many mundane contexts that’s not a bad approximation. But in many other grander contexts we seek more to become and associate with distinctive intellectual artists. Such artists are impressive both via the wide range of topics on which they can be impressive, and via having a distinctive personal style that they can bring to bear on this range of topics.

This all makes complete sense as an impressiveness contest, but far less sense as a way for the world to jointly estimate accurate Bayesian estimates on each topic. I’m sure you can make up reasons why distinctive intellectual styles that imply positions on wide ranges of topics are really great ways to produce accuracy. But they will mostly sound like excuses to me.

Sophomoric thinkers often retain for a lifetime the random opinions they quickly generate without much thought. Yet they don’t want to just inherit their parents positions; they need to generate their own new opinions. I wonder which effect will dominate when young ems choose opinions; will they tend to adopt standard positions of prior clan members, or generate their own new individual opinions?

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Forget The Maine

I often write about situations where we say something is about X, but it is actually more than we admit about Y. In some cases, this is mostly unconscious, and most people are surprised to hear what is going on. In other cases most people kind of know it, even if they don’t tend to talk about it. So if what I’m about to tell you seems obvious, well just stop reading.

I spent the last few days touring monuments near Washington D.C. A great many of them come with the explicit message “Remember.” As if to say “Big things once happened, or nearly happened. If enough of us remember them, we can do better in the future to avoid bad things, and encourage good things.” When your choice is data vs. ignorance, you know what you are supposed to choose.

Except, if that were the goal we might do better to have big pretty places organized around categories of events, each with statistics on that type of event. The monument for wars might show stats on what kinds of wars went better. The monument for floods might show stats relating efforts to prevent floods to later consequences.

But what we actually have are monuments for particular events, and particular people. In reality, these events and people are very complex. Depending on your assumptions and perspectives, you can draw a great many contradictory lessons from them. And usually experts do in fact hold a wide range of conflicting views. Especially if we include experts from other nations, etc.

But monuments usually show little of this wide range of interpretations. Instead, the basic context usually gives visitors a pretty good idea of preferred interpretations. So the monument itself doesn’t have to belabor the point – just a few choice quotes and items selected for presentation in particular contexts are enough. Treating the monument respectfully can then function as a way to signal one’s respect for these usual interpretations.

If monuments gave explicit ideological sermons, visitors who disagreed might try to refute the arguments given. Out loud, on the spot. And many others would have a plausible reason to not want to go there. But if there are only a bunch of artifacts in a beautiful setting, a reminder that people died, and an exhortation to “remember”, what can anyone rebut, and what excuse is there not to go? Even though going there will be on average interpreted as support for the usual interpretation.

For example, Arlington National Cemetery prominently displays the mast of The Maine, a ship sunk in 1898 in Havana harbor. The Spanish were blamed, “Remember the Maine” became a battle cry, and the U.S. had an excuse to start the Spanish-American war. Though today it seems more likely that the explosion was accidental.

A world intent on not forgetting and learning from key data might have a monument to events that start wars, and present stats on the fraction of wars that were started on fake pretexts. And perhaps summarize key arguments on the causes of wars and ways to prevent wars. But in our world there are mainly monuments that, in a pretty, solemn, and patriotic context, remind visitors that people died, and that others uttered the phrase “Remember the Maine.” Damn Spaniards ..

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A Tangled Task Future

Imagine that you want to untangle a pile of cables. It wasn’t tangled on purpose; tangling just resulted naturally from how these cables were used. You’d probably look for the least tangled cable in the least tangled part of the pile, and start to work there. In this post I will argue that, in a nutshell, this is how we are slowly automating our world of work: we are un- and re-tangling it.

This has many implications, including for the long-term future of human-like creatures in a competitive world. But first we have a bit of explaining to do. Continue reading "A Tangled Task Future" »

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A Call To Adventure

I turn 58 soon, and I’m starting to realize that I may not live long enough to finish many of my great life projects. So I want to try to tempt younger folks to continue them. Hence this call to adventure.

One way to create meaning for your life is join a grand project. Or start a new one. A project that is both obviously important, and that might also bring you personal glory, if you were to made a noticeable contribution to it.

Yes, most don’t seek meaning this way. But many of our favorite fictional characters do. If you are one of the few who find grand adventures irresistibly romantic, then this post is for you. I call you to adventure.

Two great adventures actually, in this post. Both seem important, and in the ballpark of doable, at least for the right sort of person.

ADVENTURE ONE: The first adventure is to remake collective decision-making via decision markets (a.k.a. futarchy). Much of the pain and loss in the world results from bad decisions by key organizations, such as firms, clubs, cities, and nations. Some of these bad decisions result because actors with the wrong mix of values hold too much power. But most result from our not aggregating info well; people who could have or did know better were not enticed enough to share what they know. Or others didn’t believe them.

We actually know of a family of simple robust mechanisms that typically do much better at aggregating info. And we have a rough idea of how organizations could use such mechanisms. We even had a large academic literature testing and elaborating these mechanisms, resulting in a big pile of designs, theorems, software, computer simulations, lab tests, and field tests. We don’t need more of these, at least for now.

What we need is concrete evolution within real organizations. Like most good abstract ideas, what this innovation most needs are efforts to work out variations that can fit well in particular existing organization contexts. That is, design and try out variations that can avoid the several practical obstacles that we know about, and help identify more such obstacles to work on.

This adventure less needs intellectuals, and more sharp folks willing to get their hands dirty dealing with the complexities of real organizations, and with enough pull to get real organizations near them to try new and disruptive methods.

Since these mechanisms have great potential in a wide range of organizations, we first need to create versions that are seen to work reliably over a substantial time in concrete contexts where substantial value is at stake. With such a concrete track record, we can then push to get related versions tried in related contexts. Eventually such diffusion could result in better collective decision making worldwide, for many kinds of organizations and decisions.

And you might have been one of the few brave far-sighted heroes who made it happen.

ADVENTURE TWO: The second adventure is to figure out real typical human motives in typical familiar situations. You might think we humans would have figured this out long ago. But as Kevin Simler and I argue in our new book The Elephant in the Brain: Hidden Motives in Everyday Life, we seem to be quite mistaken about our basic motives in many familiar situations.

Kevin and I don’t claim that our usual stated motives aren’t part of the answer, only that they are much less than we like to think. We also don’t claim to have locked down the correct answer in all these situations. We instead offer plausible enough alternatives to suggest that the many puzzles with our usual stories are due to more than random noise. There really are systematic hidden motives behind our behaviors, motives substantially different from the ones we claim.

A good strategy for uncovering real typical human motives is to triangulate the many puzzles in our stated motives across a wide range of areas of human behavior. In each area specialists tend to think that the usual stated motive deserves to be given a strong prior, and they rarely think we’ve acquired enough “extraordinary evidence” to support the “extraordinary claims” that our usual stated motives are wrong. And if you only ever look at evidence in a narrow area, it can be hard to escape this trap.

The solution is expect substantial correlations between our motives in different areas. Look for hidden motive explanations of behaviors that can simultaneously account for puzzles in a wide range of areas, using only a few key assumptions. By insisting on a high ratio of apparently different puzzles explained to new supporting assumptions made, you can keep yourself disciplined enough not to be fooled by randomness.

This strategy is most effective when executed over a lifetime. The more different areas that you understand well enough to see the key puzzles and usual claims, the better you can triangulate their puzzles to find common explanations. And the more areas that you have learned so far, the easier it becomes to learn new areas; areas and methods used to study them tend to have many things in common.

This adventure needs more intellectual heroes. While these heroes may focus for a time on studying particular areas, over the long run their priority is to learn and triangulate many areas. They seek simple coherent accounts that explain diverse areas of human behavior. To figure out what the hell most humans are actually up to most of the time. Which we do not actually know now. And which would enable better policy; today policy reform efforts are often wasted due to mistaken assumptions about actual motives.

Wouldn’t someone who took a lifetime to help work that out be a hero of the highest order?

Come, adventures await. For the few, the brave, the determined, the insightful. Might that be you?

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Conformity Excuses

From a distance it seems hard to explain a lot of human behavior without presuming that we humans have strong desires to conform to the behaviors of others. But when we look at our conscious thoughts and motivations regarding our specific behaviors, we find almost no conformity pressures. We are only rarely aware that we do anything, or avoid doing other things, because we want to conform.

The obvious explanation is that we make many excuses for our conformity – we make up other mostly-false explanations for why we like the same things that others like, and dislike other things. And since we do a lot of conforming, there must be a lot of bias here. So we can uncover and understand a lot of our biases if we can identify and understand these excuses. Here are a few possibilities that come to mind. I expect there are many others.

I picked my likes first, my group second. We like to point out that we are okay with liking many things that many others in the world don’t like. Yes, the people around us tend to like those same things, but that isn’t us conforming to those social neighbors, because we picked the things we like first, and then picked those people around us as a consequence. Or so we say. But we conform far more to our neighbors than can plausibly be explained by our limited selection power.

I just couldn’t be happy elsewhere. We tend to tell ourselves that we couldn’t be happy in a different profession, city, or culture, in part to excuse our reluctance to deviate from the standard practices of such things. We’d actually adjust fine to much larger moves than we are willing to consider.

I actually like small differences. We notice that we don’t like to come to a party in the exact same dress as someone else. We also want different home decorations and garden layouts, and we don’t want to be reading the exact same book as everyone else at the moment. We then extrapolate and think we don’t mind being arbitrarily different.

In future, this will be more popular. We are often okay with doing something different today because we imagine that it will become much more popular later. Then we can be celebrated for being one of the first to like it. If we were sure that few would ever like it, we’d be much less willing to like it now.

Second tier folks aren’t remotely as good. While we personally can tell the difference between someone who is very bad and someone who is very good, we usually just don’t have the discernment to tell the difference in quality between the most popular folks and second tier folks who are much less popular. But we tell ourselves that we can tell the difference, to justify our strong emphasis on those most popular folks.

Unpopular things are objectively defective. We probably make many specific excuses about unpopular things, to justify our neglect of them.

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