Monthly Archives: August 2017

Forager v Farmer, Elaborated

Seven years ago, after a year of reading up on forager lives, I first started to explore a forager vs. farmer axis:

A lot of today’s political disputes come down to a conflict between farmer and forager ways, with forager ways slowly and steadily winning out since the industrial revolution. It seems we acted like farmers when farming required that, but when richer we feel we can afford to revert to more natural-feeling forager ways. The main exceptions, like school and workplace domination and ranking, are required to generate industry-level wealth. (more)

Recently I decided to revisit the idea, to see if I could find a clearer story that accounts better for many related patterns. Here is what I’ve come up with.

Our primate ancestors lived in a complex Machiavellian social world, with many nested levels of allies each coordinating to oppose outside rival groups of allies, often via violence. Humans, however, managed to collapse most of those levels into one: what Boehm has called a “reverse dominance hierarchy.” Human bands were mostly on good terms with neighboring bands, who they met infrequently. Inside each band, the whole group used weapons and language to coordinate to enforce shared social norms, to create a peaceful egalitarian safe space.

Individuals who saw a norm violation could tell others, and then the whole band could discuss what to do about it. Once a consensus formed, the band could use weapons to enforce their collective decision. As needed, punishments could escalate from scolding to shunning to exile to death. Common norms included requirements to share food and protection, and bans on violence, giving orders, bragging, and creating subgroup factions.

This worked often, but not always. People retained general Machiavellian social abilities, and usually used them covertly, just out of view of group norm enforcement. But sometimes the power of the collective waned, and then many would switch to acting more overtly Machiavellian. For example, an individual or a pair of allies might become so powerful that they could openly defy the group’s disapproval. Or such a pair might violate norms semi-privately, and use a threat of strong retaliation to dissuade others from openly decrying their violations. Or a nearby rival group might threaten to attack. Or a famine or flood might threaten mass mortality.

In the absence of such threats, the talky collective was the main arena that mattered. Everyone worked hard to look good by the far-view idealistic and empathy-based norms usually favored in collective views. They behaved well when observed, learned to talk persuasively to the group, and made sure to have friends to watch and talk for them. They expressed their emotions, and acted like they cared about others.

When they felt on good terms with the group, people could relax and feel safe. They then become more playful, and acted like animals generally do when playful. Within a bounded safe space, behavior becomes more varied, stylized, artistic, humorous, teasing, self-indulgent, and emotionally expressive. For example, there is more, and more varied, music and dance. New possibilities are explored.

A feeling of safety includes feeling safe to form more distinct subgroups, without others seeing such subgroups as threatening factions. And that includes feeling safe to form groups that tend to argue together for similar positions within talky collective discussions, and to disagree with the larger group. After all, it is hard for a talky collective to function well unless members are allowed to openly disagree with one another.

But when the group was stressed and threatened by dominators, outsiders, or famine, the collective view mattered less, and people reverted to more general Machiavellian social strategies. Then it mattered more who had what physical resources and strength, and what personal allies. People leaned toward projecting toughness instead of empathy. And they demanded stronger signals of loyalty, such as conformity, and were more willing to suspect people of disloyalty. Subgroups and non-conformity became more suspect, including subgroups that consistently argued together for unpopular positions.

And here is the key idea: individuals vary in the thresholds they use to switch between focusing on dealing with issues via an all-encompassing norm-enforcing talky collective, and or via general Machiavellian social skills, mediated by personal resources and allies. Everyone tends to switch together to a collective focus as the environment becomes richer and safer. (This is one of the many ways that behaviors and values consistently change with wealth.) But some switch sooner: those better at working the collective, such as being better at talking and empathy, and those who gain more from collective choices, such as physically weaker folks who can’t hunt or gather as well. And also people just generally less prone to feeling afraid as a result of ambiguous cues.

People who feel less safe are more afraid of changing whatever has worked in the past, and so hold on more tightly to typical past behaviors and practices. They are more worried about the group damaging the talky collective, via tolerating free riders, allowing more distinct subgroups, and by demanding too much from members who might just up and leave. Also, those who feel less able to influence communal discussions prefer groups norms to be enforced more simply and mechanically, without as many exceptions that will be more influenced by those who are good at talking.

I argue that this key “left vs. right” inclination to focus more vs less on a talky collective is the main parameter that consistently determines who people tend to ally with in large scale political coalitions. Other parameters can matter a lot in different times and places, but this is the one that consistently matters. This parameter doesn’t matter much for how individuals relate to each other personally, and at smaller social scales like clubs or firms, coalitions form more via our general Machiavellian abilities, based on parameters than matter directly in those contexts. But everyone has an intuitive sense for how much we all expect and want big issues to be handled by a talky collective of “everyone” with any power. The first and primary political question is how much to try to resolve issues via a big talky collective, or to let smaller groups decide for themselves.

This account that I’ve just outline does reasonably well at accounting for many known left-right patterns. For example, the right is more conscientious, while the left is more open to experience. The left prefers more varied niche types of sports, movies, and music, while the right prefers fewer standardized types. Artists, musicians, and comedians tend to be on the left. Right sports focus more on physical strength and combat, stronger men have stronger political opinions, and when low status they favor more redistribution. People on the right are less reflective, prefer simpler arguments, are more sensitive to disgust, and startle more easily.

Education elites are more left than business elites. In romance and spirituality, the left tends to favor authentic feelings while the right cares more about standards of behavior. The left is more spiritual while right is more religious. Left jobs focused more on talking and on a high tail of great outcomes, while right jobs focus more on avoiding a low tail of bad outcomes.

The left is more okay with people forming distinct subgroups, even as it thinks more in terms of treating everyone equally, even across very wide scopes, and including wide scopes in more divisive debates. The right wants to make redistribution more conditional, more wants to punish free riders, and wants norm violators to be more consistently punished. The left tends to presume large scale cooperation is feasible, while right tends to presume competition more. The left hopes for big gains from change while the right worries about change damaging things that now work.

Views tend to drift leftward as nations and the world get richer. Left versus right isn’t very useful for predicting individual behavior outside of politics, even as it is the main parameter that robustly determines large scale political inclinations. People tend to think differently about politics on what they see as the largest scales; for example, there are whole separate fields of political science and political philosophy, which don’t overlap much with fields dealing with smaller scale politics, such as in clubs and firms.

I shouldn’t need to say it but I will anyway: it is obvious that a safe playful talky collective is sometimes but not always the best way to deal with things. Its value varies with context. So sometimes those who are more reluctant to invoke it are right to be wary, while at other times those who are eager to apply it are right to push for it. It is not obvious, at least to me, whether on average the instincts of the left or the right are more helpful.

I’ve noted before that if one frames left attitudes as better when the world is safe, while right attitudes as better when world is harsh, the longer is the timescale on which you evaluate outcomes, the harsher is the world.

Added 9Sept: This post didn’t say much directly about farmers. In the much larger farmer social groups, simple one layer talky collectives were much less feasible. Farmer lives had new dangers of war and disease, and neighboring groups were more threatening. The farmer world more supported property in spouses and material goods and had more social hierarchies, farmer law less relied on a general discussion of each accused, and more reliable food meant there was less call for redistribution. Farmers worked more and had less time for play.  Together, these tended to reduce the scope of safe playful talky collectives, moving society in a rightward direction relative to foragers.

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Meaning is Easy to Find, Hard to Justify

One of the strangest questions I get when giving talks on Age of Em is a variation on this:

How can ems find enough meaning in their lives to get up and go to work everyday, instead of committing suicide?

As the vast majority of people in most every society do not commit suicide, and manage to get up for work on most workdays, why would anyone expect this to be a huge problem in a random new society?

Even stranger is that I mostly get this question from smart sincere college students who are doing well at school. And I also hear that such students often complain that they do not know how to motivate themselves to do many things that they “want” to do. I interpret this all as resulting from overly far thinking on meaning. Let me explain.

If we compare happiness to meaning, then happiness tends to be an evaluation of a more local situation, while meaning tends to be an evaluation of a more global situation. You are happy about this moment, but you have meaning regarding your life.

Now you can do either of these evaluations in a near or a far mode. That is, you can just ask yourself for your intuitions on how you feel about your life, within over-thinking it, or you can reason abstractly and idealistically about what sort of meaning you should have or can justify having. In that later more abstract mode, smart sincere people can be stumped. How can they justify having meaning in a world where there is so much randomness and suffering, and that is so far from being a heaven?

Of course in a sense, heaven is an incoherent concept. We have so many random idealistic constraints on what heaven should be like that it isn’t clear that anything can satisfy them all. For example, we may want to be the hero of a dramatic story, even if we know that characters in such stories wish that they could live in more peaceful worlds.

Idealistic young people have such problems in spades, because they haven’t lived long enough to see how unreasonable are their many idealistic demands. And smarter people can think up even more such demands.

But the basic fact is that most everyone in most every society does in fact find meaning in their lives, even if they don’t know how to justify it. Thus I can be pretty confident that ems also find meaning in their lives.

Here are some more random facts about meaning, drawn from my revised Age of Em, out next April.

Today, individuals who earn higher wages tend to have both more happiness and a stronger sense of purpose, and this sense of purpose seems to cause higher wages. People with a stronger sense of purpose also tend to live longer. Nations that are richer tend to have more happiness but less meaning in life, in part because they have less religion. .. Types of meaning that people get from work today include authenticity, agency, self-worth, purpose, belonging, and transcendence.

Happiness and meaning have different implications for behavior, and are sometimes at odds. That is, activities that raise happiness often lower meaning, and vice versa. For example, people with meaning think more about the future, while happy people focus on the here and now. People with meaning tend to be givers who help others, while happy people tend to be takers who are helped by others. Being a parent and spending time with loved ones gives meaning, but spending time with friends makes one happy.

Affirming one’s identity and expressing oneself increase meaning but not happiness. People with more struggles, problems, and stresses have more meaning, but are less happy. Happiness but not meaning predicts a satisfaction of desires, such as for health and money, and more frequent good relative to bad feelings. Older people gain meaning by giving advice to younger people. We gain more meaning when we follow our gut feelings rather than thinking abstractly about our situations.

My weak guess is that productivity tends to predict meaning more strongly than happiness. If this is correct, it suggests that, all else equal, ems will tend to think more about the future, more be givers who help others, spend more time with loved ones and less with friends, more affirm their identity and express themselves, give more advice, and follow gut feelings more. But they will also have more struggles and less often have their desires satisfied.

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Entrenchit Happens

Most artificial systems, made by humans, slowly degrade over time until they become dysfunctional, and are replaced. Such systems rarely change or improve over time, and so are sometimes replaced while still functional, with new improved competitors.

Many systems, such as organisms and some kinds of firms, try to adapt to changing external conditions. But internal damage accumulates and eventually limits their ability to adapt quickly or well enough, and so they lose out to competitors. Empires may also decline due to internal damage.

Some larger systems, like species, nations, languages, and many kinds of firms, face many similar competitors, and rise and fall in ways that seem so random that it is hard to tell if they suffer much from internal damage, including in their ability to adapt to context.

In contrast, other larger systems face no competitors, at least for a long time, even as they are drawn from large spaces of possible systems. Consider, for example, that the community of mathematicians has created a total system of math that hangs together and is stable in many ways, and yet is drawn from a vastly larger space of possibilities. The space of possible math axioms is astronomical, but mathematicians consistently reuse the same tiny set of axioms. One could say that those axioms have become “entrenced” in math practice.

Many other kinds of widely shared systems have few competitors, and yet entrench a set of specific practices drawn from a much larger space of possibilities. Consider, for example, the DNA code, the basic architectures of cells, and standard methods of making multi-cellular organisms. Or consider the shared features of most human languages, legal systems, financial systems, economic systems, and firm organization. Or even of computer languages and computer architectures. In each of these cases most of the world has long shared the same common set of interrelated practices, even though a vastly larger space of possibilities is known to exist and to have been little explored.

Such shared practices plausibly persist because they are just too much trouble to change. As I wrote last year:

When an architecture is well enough matched to a stable problem, systems build on it can last long, and grow large, because it is too much trouble to start a competing system from scratch. But when different approaches or environments need different architectures, then after a system grows large enough, one is mostly forced to start over from scratch to use a different enough approach, or to function in a different enough environment.

In sum, entrenchment (or “entrenchit”) happens. I mention this to suggest that, as per my last post, known styles of software really could continue to dominate for long into the future. Many seem confident that very different styles will arise relatively soon on a civilizational time scale, and then mostly displace familiar styles. But who thinks we will soon see domination by new very different kinds of math axioms, human languages, legal systems, or world economic systems? Why expect more radical change in software than in most other things?

Yes, sometimes new systems really do arise to displace old ones. But you can’t help but notice that while small systems are often replaced, revolutions to replace interlocking sets of common worldwide practices much rarer. And for such systems there are far more proposed and attempted revolutions than successful ones.

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Can Human-Like Software Win?

Many, perhaps most, think it obvious that computer-like systems will eventually be more productive than human-like systems in most all jobs. So they focus on how humans might maintain control, even after this transition. But this eventuality is less obvious than it seems, depending on what exactly one means by “human-like” or “computer-like” systems. Let me explain.

Today the software that sits in human brains is stuck in human brain hardware, while the other kinds of software that we write (or train) sit in the artificial hardware that we make. And this artificial hardware has been improving rapidly far more rapidly than has human brain hardware. Partly as a result of this, systems of artificial software and hardware have been improving rapidly compared to human brain systems.

But eventually we will find a way to transfer the software from human brains into artificial hardware. Ems are one way to do this, as a relatively direct port. But other transfer mechanics may be developed.

Once human brain software is in the same sort of artificial computing hardware as all the other software, then the relative productivity of different software categories comes down to a question of quality: which categories of software tend to be more productive on which tasks?

Of course there will many different variations available within each category, to match to different problems. And the overall productivity of each category will depend both on previous efforts to develop and improve software in that category, and also on previous investments in other systems to match and complement that software. For example, familiar artificial software will gain because we have spent longer working to match it to familiar artificial hardware, while human software will gain from being well matched to complex existing social systems, such as language, firms, law, and government.

People give many arguments for why they expect human-like software to mostly lose this future competition, even when it has access to the same hardware. For example, they say that other software could lack human biases and also scale better, have more reliable memory, communicate better over wider scopes, be easier to understand, have easier meta-control and self-modification, and be based more directly on formal abstract theories of learning, decision, computation, and organization.

Now consider two informal polls I recently gave my twitter followers:

Surprisingly, at least to me, the main reason that people expect human-like software to lose is that they mostly expect whole new categories of software to appear, categories quite different from both the software in the human brain and also all the many kinds of software with which we are now familiar. If it comes down to a contest between human-like and familiar software categories, only a quarter of them expect human-like to lose big.

The reason I find this surprising is that all of the reasons that I’ve seen given for why human-like software could be at a disadvantage seem to apply just as well to familiar categories of software. In addition, a new category must start with the disadvantages of having less previous investment in that category and in matching other systems to it. That is, none of these are reasons to expect imagined new categories of software to beat familiar artificial software, and yet people offer them as reasons to think whole new much more powerful categories will appear and win.

I conclude that people don’t mostly use specific reasons to conclude that human-like software will lose, once it can be moved to artificial hardware. Instead they just have a general belief that the space of possible software is huge and contains many new categories to discover. This just seems to be the generic belief that competition and innovation will eventually produce a lot of change. Its not that human-like software has any overall competitive disadvantage compared to concrete known competitors; it is at least as likely to have winning descendants as any such competitors. Its just that our descendants are likely to change a lot as they evolve over time. Which seems to me a very different story than the humans-are-sure-to-lose story we usually hear.

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There’s Always Subtext

Our new book, The Elephant in the Brain, argues that hidden motives drive much of our behavior. If so, then to make fiction seem realistic, those who create it will need to be aware of such hidden motives. For example, back in 2009 I wrote:

Impro, a classic book on theatre improvisation, convincingly shows that people are better actors when they notice how status moves infuse most human interactions. Apparently we are designed to be very good at status moves, but to be unconscious of them.

The classic screenwriting text Story, by Robert McKee, agrees more generally, and explains it beautifully:

Text means the sensory surface of a work of art. In film, it’s the images onscreen and the soundtrack of dialogue, music, and sound effects. What we see. What we hear. What people say. What people do. Subtext is the life under that surface – thoughts and feelings both known and unknown, hidden by behavior.

Nothing is what it seems. This principle calls for the screen-writer’s constant awareness of the duplicity of life, his recognition that everything exists on at least two levels, and that, therefore, he must write a simultaneous duality: First, he must create a verbal description of the sensory surface of life, sight and sound, activity and talk. Second, he must create the inner world of conscious and unconscious desire, action and reaction, impulse and id, genetic and experiential imperatives. As in reality, so in fiction: He must veil the truth with a living mask, the actual thoughts and feelings of characters behind their saying and doing.

An old Hollywood expression goes “If the scene is about what the scene is about, you’re in deep shit.” It means writing “on the nose,” writing dialogue and activity in which a character’s deepest thoughts and feelings are expressed by what the character says and does – writing the subtext directly into the text.

Writing this, for example: Two attractive people sit opposite each other at a candlelit table, the lighting glinting off the crystal wineglasses and the dewy eyes of the lovers. Soft breezes billow the curtains. A Chopin nocturne plays in in the background. The lovers reach across the table, touch hands, look longingly in each others’ eyes, say, “I love you, I love you” .. and actually mean it. This is an unactable scene and will die like a rat in the road. ..

An actor forced to do the candlelit scene might attack it like this: “Why have these people done out of their way to create this movie scene? What’s with the candlelight, soft music, billowing curtains? Why don’t they just take their pasta to the TV set like normal people? What’s wrong with this relationship? Because isn’t that life? When do the candles come out? When everything’s fine? No. When everything’s fine we take our pasta to the TV set like normal people. So from that insight the actor will create a subtext. Now as we watch, we think: “He says he loves her and maybe he does, but look, he’s scared of losing her. He’s desperate.” Or from another subtext: “He says he loves her, but look, he’s setting her up for bad news. He’s getting ready to walk out.”

The scene is not about what it seems to be about. Its about something else. And it’s that something else – trying to regain her affection or softening her up for the barkeep – that will make the scene work. There’s always a subtext, and inner life that contrasts with or contradicts the text. Given this, the actor will create a multi layered work that allows us to see through the text to the truth that vibrates beyond the eyes, voice and gestures of life. ..

In truth, it’s virtually impossible for anyone, even the insane, to fully express what’s going on inside. No matter how much we wish to manifest our deepest feelings, they elude us. We never fully express the truth, for in fact we rarely know it. .. Nor does this mean that we can’t write powerful dialogue in which desperate people try to them the truth. It simply means that the most passionate moments must conceal an even deeper level. ..

Subtext is present even when a character is alone. For if no one else is watching us, we are. We wear masks to thinner our true selves from ourselves. Not only do individuals wear masks, but institutions do as well and hire public relations experts to keep them in place. (pp.252-257)

Added 17Sep: More on subtext of sound and images:

The power of an organized return of images is immense, as variety and repetition drive the Image System to the seat of the audiences unconscious. Yet, and most important, a film’s poetics must be handled with virtual invisibility and go consciously unrecognized. (p.402) ..

Symbolism is powerful, more powerful than most realize, as long as it bypasses the conscious mind and slips into the unconscious. As it does while we dream. The use of symbolism follows the same principle as scoring a film. Sound doesn’t need cognition, and music can deeply affect us when we’re unconscious of it. In the same way, symbols touch us and move us – as long as we don’t recognize them as symbolic. Awareness of a symbol turns it into a neutral, intellectual curiosity, powerless and virtually meaningless. (p.407)

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Marching Markups

This new paper by De Locker and Eeckhout will likely be classic:

We document the evolution of markups based on firm-level data for the US economy since 1950. Initially, markups are stable, even slightly decreasing. In 1980, average markups start to rise from 18% above marginal cost to 67% now. .. Increase in average market power .. can account for .. slowdown in aggregate output. .. The rise in market power is consistent with seven secular trends in the last three decades.

Yes, US public firms have only 1/3 of US jobs, and an even smaller fraction of the world’s. Even so, this is a remarkably broad result. I’d feel a bit better if I understood why their firm-level simple aggregation of total sales divided by total variable costs (their Figure B.5a) gives only a 26% markup today, but I’ll give them the benefit of the doubt for now. (And that figure was 12% in 1980, so it has also risen a lot.) Though see Tyler’s critique.

The authors are correct that this can easily account for the apparent US productivity slowdown. Holding real productivity constant, if firms move up their demand curves to sell less at a higher prices, then total output, and measured GDP, get smaller. Their numerical estimates suggest that, correcting for this effect, there has been no decline in US productivity growth since 1965. That’s a pretty big deal.

Accepting the main result that markups have been marching upward, the obvious question to ask is: why? But first, let’s review some clues from the paper. First, while industries with smaller firms tend to have higher markups, within each small industry, bigger firms have larger markups, and firms with higher markups pay higher dividends.

There has been little change in output elasticity, i.e., the rate at which variable costs change with the quantity of units produced. (So this isn’t about new scale economies.) There has also been little change in the bottom half of the distribution of markups; the big change has been a big stretching in the upper half. Markups have increased more in larger industries, and the main change has been within industries, rather than a changing mix of industries in the economy. The fractions of income going to labor and to tangible capital have fallen, and firms respond less than they once did to wage changes. Firm accounting profits as a fraction of total income have risen four fold since 1980.

These results seem roughly consistent with a rise in superstar firms:

If .. changes advantage the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms with high profits and a low share of labor in firm value-added and sales. .. aggregate labor share will tend to fall. .. industry sales will increasingly concentrate in a small number of firms.

Okay, now lets get back to explaining these marching markups. In theory, there might have been a change in the strategic situation. Perhaps price collusion got easier, or the game became less like price competition and more like quantity competition. But info tech should have both made it easier for law enforcement to monitor collusion, and also made the game more like price competition. Also, anti-trust just can’t have much effect on these small-firm industries. So I’m quite skeptical that strategy changes account for the main effect here. The authors see little overall change in output elasticity, and so I’m also pretty skeptical that there’s been any big overall change in the typical shape of demand or cost curves.

If, like me, you buy the standard “free entry” argument for zero expected economic profits of early entrants, then the only remaining possible explanation is an increase in fixed costs relative to variable costs. Now as the paper notes, the fall in tangible capital spending and the rise in accounting profits suggests that this isn’t so much about short-term tangible fixed costs, like the cost to buy machines. But that still leaves a lot of other possible fixed costs, including real estate, innovation, advertising, firm culture, brand loyalty and prestige, regulatory compliance, and context specific training. These all require long term investments, and most of them aren’t tracked well by standard accounting systems.

I can’t tell well which of these fixed costs have risen more, though hopefully folks will collect enough data on these to see which ones correlate strongest with the industries and firms where markups have most risen. But I will invoke a simple hypothesis that I’ve discussed many times, which predicts a general rise of fixed costs: increasing wealth leading to stronger tastes for product variety. Simple models of product differentiation say that as customers care more about getting products nearer to their ideal point, more products are created and fixed costs become a larger fraction of total costs.

Note that increasing product variety is consistent with increasing concentration in a smaller number of firms, if each firm offers many more products and services than before.

Added 25Aug: Karl Smith offers a similar, if more specific, explanation.

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My TED/TEDx Talks

My TED video on Age of Em is finally out:

As you can see, the TED folks do great at video editing. I’m hoping this will attract more viewers than the 67K of my first TEDx talk on ems 4 years ago, and the 48K of my TEDx on the Great Filter 3 years ago. As I said back in May:

The TED community seems to come about as as close as I can realistically expect to my ideal religion.

I also have a great TEDx video on Elephant in the Brain: recorded just 3 weeks later:

Added 25 Aug: 280K views of my TED video in the first day!

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How Social Is Reason?

In their book The Enigma of Reason, out last April, Hugo Mercier and Dan Sperber have written an important book on an important but neglected topic. They argue first that humans, and only humans, have a brain module that handles abstract reasoning:

Reason is indeed [a] specialized [module of inference]; it draws interpretive inferences just about reasons.

Second, they argue for a new theory of reason. Previously, scholars have focused on reason in the context of a sincere attempt to infer truth:

Most of the philosophers and psychologists we talked to endorse some version of the dominant intellectualist view: they see reason as a means to improve individual cognition and arrive one one’s own at better beliefs and decisions. Reason, they take for granted, should be objective and demanding.

In this view, observed defects in human reasoning are to be seen as understandable errors, accommodations to complexity, and minor corrections due to other minor selection pressures. Sincerely inferring truth is the main thing. Mercier and Sperber, however, argue that one social correction isn’t at all minor: reason is better understood in the context of a speaker who is trying to a persuade a listener who sincerely seeks to infer truth. Speaker “biases” are just what one should expect from speakers seeking to persuade:

In our interactionist account, reason’s bias and laziness aren’t flaws; they are features that help reason fulfill its function. People are biased to find reasons that support their point of view because this is how they can justify their action and convince others to share their beliefs.

Mercier and Sperber do successfully show that many “defects” in human reasoning can be understand as arising from insincere speaker motives. However, just as we can question speakers motives, we can also question listener motives. Couldn’t listeners also be also concerned about the social consequences of their inferences? Listeners might want to agree to show submission or favor to a speaker, and ignore or disagree to show disfavor or dominance. And listeners may want to agree with what they expect others to agree with, to sound reasonable and to show loyalty.

Mercier and Sperber seem to be aware of many such listener motives:

Luria used problems that were logically trivial but .. unfamiliar:

In the Far North, where there is snow, all bears are white. Novaya Zemlya is in the Far North. What color are bears there?

When unschooled peasants were interviewed, the vast majority seemed at a loss, providing answers such as “There are many sorts of bears.” .. His experiments were successfully replicated with several unschooled populations. .. In small-scale populations, people are very cautious with their assertions, only stating a position when they have a good reason to. .. Only a fool with dare to make such a statement .. she could not appropriately defend. ..

Because of the intense pressure to maintain social harmony, “the Japanese are not trained to argue and reason.” ..

The overlap between the proper and the actual domain of reasoning remains partial. There are false negatives: people in a dominant position or in the vocal majority might pay little attention to the opinion of subordinates or minorities and fail to detect disagreements. There are also false positives; either clashes of ideas that occur between third parties with whom we are not in a position to interact … or clashes of ideas within oneself. ..

Throughout the centuries, smart physicians felt justified in making decisions that cost patients their lives. .. If they were eager to maintain their reputation, they were better off bleeding their patients. ..

You might be ill-judged by people who are not aware of this argument, and you might not have the opportunity to explain the reason for your choice.

Mercier and Sperber treat these various effects as minor corrections that don’t call into question their basic theory, even as they complain that the traditional view of reason doesn’t attend enough to certain effects that their theory explains. But it seems to me that in addition to explaining some effects as due to insincere speaker motives, a better theory of reason could also explain other effects as due to insincere listener motives.

In the modern world, while we usually give lip service to the idea that we are open to letting anyone persuade us on anything with a good argument, by the time folks get to be my age they know that such openings are in fact highly constrained. For example, early on in my relation with my wife she declared that as I was better at arguing, key decisions were just not going to be made on the basis of better arguments.

Even in academia, little value is placed on simple relevant arguments, compared to demonstrating the mastery of difficult tools. And in our larger world, the right to offer what looks like a critical argument is usually limited to the right sort of people who have the right sort of relation in the right sort of contexts. Even then people know to avoid certain kinds of arguments, even if those arguments would in fact persuade if pushed hard enough. And most speakers know they are better off arguing for what listeners want to believe, rather than for unpleasant conclusions.

Mercier and Sperber suggest that arguing used to be different, and better:

When a collective decision has to be made in a modern democracy, people go to the voting booth. Our ancestors sat down and argued – at least if present-day small-scale societies are any guide to the past. In most such societies across the globe, when a grave problem threatens the group, people gather, debate, and work out a solution that most find satisfying. ..

When the overriding concern of people who disagree is to get things right, argumentation should not only make them change their mind, it should make them change their mind for the best.

I’d like to believe that argumentation was all different and better back then, with careful speakers well disciplined by sincere listeners. But I’m skeptical. I expect that the real selection pressures on our abilities to reason have always reflected these complex social considerations, for both speakers and listeners. And we won’t really understand human reasoning until we think through what reasoning behaviors respond well to these incentives.

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Why Ethnicity, Class, & Ideology? 

Individual humans can be described via many individual features that are useful in predicting what they do. Such features include gender, age, personality, intelligence, ethnicity, income, education, profession, height, geographic location, and so on. Different features are more useful for predicting different kinds of behavior.

One kind of human behavior is coalition politics; we join together into coalitions within political and other larger institutions. People in the same coalition tend to have features in common, though which exact features varies by time and place. But while in principle the features that describe coalitions could vary arbitrarily by time and place, we in actual fact see more consistent patterns.

Now when forming groups based on shared features, it make senses to choose features that matter more in individual lives. The more life decisions a feature influences, the more those who share this feature may plausibly share desired policies, policies that their coalition could advocate. So you might expect political coalitions to be mostly based on individual features that are very useful for predicting individual behavior.

You might be right about small scale coalitions, such as cliques, gangs, and clubs. And you might even be right about larger scale political coalitions in the ancient world. But you’d be wrong about our larger scale political coalitions today. While there are often weak correlations with such features, larger scale political coalitions are not mainly based on the main individual features of gender, age, etc. Instead, they are more often based on ethnicity, class, and “political ideology” preferences. While ideology is famously difficult to characterize, and it does vary by time and place, it is also somewhat consistent across time and space.

In this post, I just want to highlight this puzzle, not solve it: why are these the most common individual features on which large scale political coalitions are based? Yes, in some times and places ethnicity and class matter so much that they strongly predict individual behavior. But even when they don’t matter much for policy preferences, they are still often the basis of coalitions. And why is political ideology so attractive a basis for coalitions, when it matters so little in individual lives?

I see two plausible types of theories here. One is a theory of current functionality; somehow these features actually do capture the individual features that best predict member positions on typical issues. Another is a theory of past functionality; perhaps in long-past forager environments, something like these features were the most relevant. I now lean toward this second type of theory.

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Ems in Walkaway

Some science fiction (sf) fans have taken offense at my claim that non-fiction analysis of future tech scenarios can be more accurate than sf scenarios, whose authors have other priorities. So I may periodically critique recent sf stories with ems for accuracy. Note that I’m not implying that such stories should have been more accurate; sf writing is damn hard work and its authors juggle a many difficult tradeoffs. But many seem unaware of just how often accuracy is sacrificed.

The most recent sf I’ve read that includes ems is Walkaway, by “New York Times bestselling author” Cory Doctorow, published back in April:

Now that anyone can design and print the basic necessities of life—food, clothing, shelter—from a computer, there seems to be little reason to toil within the system. It’s still a dangerous world out there, the empty lands wrecked by climate change, dead cities hollowed out by industrial flight, shadows hiding predators animal and human alike. Still, when the initial pioneer walkaways flourish, more people join them.

The emotional center of Walkaway is elaborating this vision of a decentralized post-scarcity society trying to do without property or hierarchy. Though I’m skeptical, I greatly respect attempts to describe such visions in more detail. Doctorow, however, apparently thinks we economists make up bogus math for the sole purpose of justifying billionaire wealth inequality. Continue reading "Ems in Walkaway" »

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