Monthly Archives: September 2015

Max & Miller’s Mate

Geoffrey Miller’s book The Mating Mind was very influential on me, and so I spent several posts on his book Spent. He has a new book out, coauthored with Tucker Max, called Mate: become the man women want. It is a how-to book, on how men can attract women.

The book’s voice is less academic and more like a drill sergeant — stern older men giving harsh but needed instructions to younger men. They don’t mind using some crude language, and they don’t argue much for their claims, expecting readers to accept what they say on authority. Fortunately, most of what they say seems to be pretty well-grounded in the literature.

The world view they present has mating quite thoroughly infused with signaling. Pretty much everything you do with actual or potential mates is used as a reliable signal of your hidden features. Makes me wonder in what other self-help books it would be okay to present as strong a signaling view. Perhaps there are career advice books that infuse signaling as throughly into their view of the work world. But I expect people wouldn’t tolerate advice books on school, religion, arts, and charity that are this signaling heavy. Even if the advice was solid.

Though heavy on signaling, Max & Miller don’t consider self-deception. They talk simply about men just looking inside themselves to see what they want, and tell men to take what women seem to want at face value. But perhaps talking about self-deception to their target audience (young men who feel they are failing at mating) would just confuse more than help.

At several points Max & Miller warn their readers that women never evolved general ways to see and appreciate things like wealth and intelligence; women instead evolved to appreciate more specific signals like nice clothes and wit. So don’t go trying to show off your IQ score or bank balance.

They don’t advise women to fix this oversight, but instead advise men to fix how they show off. I suspect the idea is that humans are just more general and flexible on how to achieve their goals than on what exactly are their goals. And I suspect this is right. While one can imagine a creature that just wants “whatever helps me have many descendants”, humans are just not those creatures.

Two suggestive implication follow from this fact. First, if descendants of humans are ever blocked in their growth or expansion into the universe due to their failing to be sufficiently flexible or general, that failing will more likely come from their preferences, rather than their engineering or science. Second, as human incomes fall toward subsistence, our primary preferences for survival trump others, inducing effectively more general and flexible preferences. So subsistence income descendants have a better chance of avoiding generality failures.

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Beware Intuitive Econ

Economists have two standard very simple models of product competition: firms can compete on price or compete on quantity.

Early in the Dotcom internet revolution, I remember some people, including even Bill Gates I think, forecasting that since it becomes easier to find and compare prices on the internet, products sold on the internet would have stronger price competition. And since price competition tends to be stronger than quantity competition, that would bring down prices and be good for consumers. Yay internet!

This argument makes intuitive sense, but is dead wrong. And that is the warning of this post: beware simple econ arguments based on intuitions that haven’t been verified in concrete models.

Even in price competition, quantity must also be chosen; price competition is where quantities are chosen indirectly, as a result of the prices. Similarly, even in quantity competition price must also be chosen; quantity competition is where prices are chosen indirectly, as a result of the quantities.

Economists have long had simple models where both price and quantity are chosen explicitly and directly by firms. In these models, what matters is which of these parameters becomes expensive to change first, and which parameters can keep changing more cheaply closer to the last minute of sale.

For concrete examples, consider selling TVs and plumbing services. A TV firm must decide at least weeks before a sale how many TVs they are going to make and deliver then, but they might change the price they offer for such TVs in the last few days before a sale. In contrast, a plumber might pick a price to charge to include in ads many days before customers see such ads and call to get help, but at the last minute the plumber can say “sorry, I don’t have any more openings on my schedule today.”

In models where firms must commit to quantities early, but can keep changing their prices easily until near the last minute, outcomes are close to those in simple models of quantity competition. But if firms must commit to prices early, but can keep changing quantity until near the last minute, outcomes are close to those in simple models of price competition.

So whether firms compete on price or quantity depends more on which of these they must commit to earliest, not which is easier to change at the last minute. Knowing this, once you heard that it would be easier to change prices at the last minute for products sold on internet, you should have predicted that the internet would increase quantity competition and reduce price competition. Which it in fact has.

Economics is general and robust enough to predict things like how selling products on the internet changes competition. But you have to use it right.

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Intelligence Futures

For many purposes, such as when choosing if to admit someone to a college, we care about both temporary features, who they are now, and permanent features, who they have the ultimate potential to become. One of those features is intelligence; we care about how smart they are now, and about how smart they have the potential to become.

A standard result in intelligence research is that intelligence as measured late in life, such as at age fifty, is a much better indicator of ultimate potential than is intelligence measured at early ages. That is, environments have a stronger influence over measured intelligence of the young, relative to the old.

So if you want a measure of an ultimate potential, such as to use in college admissions, then instead of using current tests like SAT scores, you’d do better to use a good prediction of future test scores, such as predictions of related tests at age fifty.

Now of course colleges could try to do this prediction themselves. They could collect a dataset of people where they have late life test scores and also many possible early predictors of those future test scores, and then fit a statistical model to all that. But such data is hard to collect, this approach limits you to predictors available in your dataset, and the world changes, so that models that work on old data may not predict new data.

Let me propose a prediction market solution: create prediction markets on late life test scores. To make sure people try hard enough later, collect a fund to pay out to the person later in proportion to their late life test score. Then open (and subsidize) a market today in that future test score, and post any associated info that this person will allow. Speculators could then use that info, and anything else they could figure out, to guess the future test score. Finally, use market prices as estimate of future test scores, and thus of ultimate potential, in college admissions.

This approach could of course also be used by employers and other individuals or organizations that care about potential. A single market on a future test score could inform many audiences at once. And this approach could also be used for any other measures of potential where late life measures are more reliable than early life measures.

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Forsee The Speakularity

James Somers at Nautilus on the “Speakularity“:

We are going to start recording and automatically transcribing most of what we say. Instead of evaporating into memory, words spoken aloud will calcify as text, into a Record that will be referenced, searched, and mined. .. Think of all the reasons that you search through your email. Suddenly your own speech will be available in just the same way. “Show me all conversations with Michael before January of last year … What was the address of that restaurant Mum recommended? …” Robin Hanson, an economist at George Mason University and a co-author of a forthcoming book on evolutionary psychology, has speculated that we might all get in the habit of peppering our speech with keywords, to help us look it up later. …

Much of what is said aloud would be published and made part of the Web. An unfathomable mass of expertise, opinion, wit, and culture—now lost—would be as accessible as any article or comment thread is today. ..

It won’t reshape the basic ways we live and love. It won’t turn our brains to mush, or make us supermen. . .. People talk a lot—on average, about 40 percent of their waking lives. .. anyone who’s ever recorded someone knows that self-conscious monitoring of your own speech is just too mentally expensive to carry on for very long. … After a short while, you go back to normal.

Hanson also thinks “normal” would be the operative word once ubiquitous speech transcription arrives. He’s not convinced that it would change the world as much as some seem to think it would. “As soon as you see just how different our world is from 1,000 years ago, it’s really hard to get very worked up about this,” he says.

There was almost no privacy 1,000 years ago, he explains. Living quarters were dense. Rooms were tiny … Other people could overhear your lovemaking. When you traveled, you hardly ever went by yourself; you roamed around in little groups. Most people lived in small towns, where most everybody knew everybody else and gossiped about them. The differences in how we lived between then and now were huge. And yet we adapted. “I gotta figure the changes we’re looking at are small by comparison,” he says. …

Having a Record will just give us a new dimension on which to map a capacity we’ve always had. People who are constantly being recorded will adapt to that fact by becoming expert at knowing what’s in the transcript and what’s not. They’ll be like parents talking around children. They’ll become masters of plausible deniability. They’ll use sarcasm, or they’ll grimace or grin or lean their head back or smirk, or they’ll direct their gaze, so as to say a thing without saying it.

It sounds exhausting, but of course we already fluidly adapt to the spectrum of private, small-group, and public conversations—just go to a workplace. Or go to a party. We are constantly asking and answering subtle questions about our audience, and tuning our speech based on the answers. (Is Jack in earshot? Is Jack’s wife in earshot?)

“There’s no way this means that everything we say is now in the open,” Hanson argues. “There’s a layer of what we say that’s in the open … but we’re always talking at several levels at once.” … Our brains adapted to writing, to libraries, and to the Web. They will adapt to the Record. And people will, anyway, continue to be less concerned with how they sound than with how they look. (more)

Even if this change is smaller than changes to which we’ve already adapted, still it will be a real change. The biggest open question is what fraction of our speech will go directly into a public record. I find it hard to believe this would be the majority, but then I wouldn’t have predicted how much people are willing to say publicly on Twitter or Facebook.

Even for speech that isn’t directly made public, we would all know that everyone who heard a conversation had a private record, from which they could choose to privately share selected quotes. Of course we already worry about people quoting things we say in private to others, but direct recordings would be more believable and thus more worrisome. Laws prohibiting such recordings could reduce such problems, but would hardly eliminate them.

The big obvious change to predict is that we will be less clear and direct, even in private, when saying things that might make us look bad when quoted later. Already twitter speak tends to be more sarcastic, ironic, and loaded with local references that make it harder for outsiders to clearly understand. Expect most ordinary speech to move in this direction.

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News of What?

Today’s New York Times has a 7000 word article by Amy Harmon on cryonics, brain scanning, and brain emulation. Now these are subjects of great interest to me; my first book comes out in spring on the third topic. And 7000 words is space to say a great deal, even if you add the constraint that what you say must be understandable to the typical NYT reader.

So I’m struck by the fact that I have almost nothing to say in response to anything particular said in this article. Ms. Harmon gives the most space to one particular young cryonics patient who got others to donate to pay for her freezing. This patient hopes to return via brain emulation. Ms. Harmon discusses some history of the Brain Preservation Prize, highlighting Ken Hayworth personally, and quotes a few experts saying we are nowhere close to being able to emulate brains. At one point she says,

The questions the couple faced may ultimately confront more of us with implications that could be preposterously profound.

Yet she discusses no such implications. She discusses no arguments on if emulation would be feasible or desirable or what implications it might have. I’ll give her the benefit of the doubt and presume that her priorities accurately reflect the priorities of New York Times readers. But those priorities are so different from mine as to highlight the question: what exactly do news readers want?

For a topic like this, it seems readers want colorful characters described in detail, and quotes from experts with related prestige. They don’t want to hear about arguments for or against the claims made, or to discuss further implications of those claims. It seems they will enjoy talking to others about the colorful characters described, and perhaps enjoy taking a position on the claims those characters make. But these aren’t the sort of topics where anyone expects to care about the quality or care of the arguments one might. It is enough to just have opinions.

Added 14Sep: Amy posted a related article that is a technical review of brain emulation tech. I’m glad it exists, but I also have nothing particular to say in response.

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Failed Singularity Model

Noted Yale economist William Nordhaus has a new paper “Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth”:

Assume that labor is constant, that all technological change is capital-augmenting at 10% per year, and that the elasticity of substitution between labor and information capital is 1.25. Figure 3 shows a typical simulation of the share of capital and the growth rates of output and wages.

NordhausSingularityIn this model, capital slowly gets a larger share of total income and the economic growth accelerates, even though the rate of innovation never changes. Nordhaus lists six empirical predictions for the sign of observed parameters, and finds that four of the six are rejected by our best estimates having the opposite sign. And this doesn’t include the fact that our best estimates find the elasticity of substitution between labor and capital to be less than one. The two sign predictions that match the data suggest it would take a century or more before growth rates exceed 20% per year. Nordhaus says, “The conclusion is therefore that the growth Singularity is not near.”

Of course this is far from the only possible economic model of a singularity. But it sets a good standard for future efforts. Can anyone find a concrete simple economic model of singularity that better fits the data?

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Help Me Pick Book Title

(No I’m not going to say more about the book now.)

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Monster Pumps

Yesterday’s Science has a long paper on an exciting new scaling law. For a century we’ve known that larger organisms have lower metabolisms, and thus lower growth rates. Metabolism goes as size to the power of 3/4 over at least twenty orders of magnitude:


So our largest organisms have a per-mass metabolism one hundred thousand times lower than our smallest organisms.

The new finding is that local metabolism also goes as local biomass density to the power of roughly 3/4, over at least three orders of magnitude. This implies that life in dense areas like jungles is just slower and lazier on average than is life in sparse areas like deserts. And this implies that the ratio of predator to prey biomass is smaller in jungles compared to deserts.

When I researched how to cool large em cities I found that our best cooling techs scale quite nicely, and so very big cities need only pay a small premium for cooling compared to small cities. However, I’d been puzzled about why biological organisms seem to pay much higher premiums to be large. This new paper inspired me to dig into the issue.

What I found is that human engineers have figured ways to scale large fluid distribution systems that biology has just never figured out. For example, the hearts that pump blood through animals are periodic pumps, and such pumps have the problem that the pulses they send through the blood stream can reflect back from joints where blood vessels split into smaller vessels. There are ways to design joints to eliminate this, but those solutions create a total volume of blood vessels that doesn’t scale well. Another problem is that blood vessels taking blood to and from the heart are often near enough to each other to leak heat, which can also create a bad scaling problem.

The net result is that big organisms on Earth are just noticeably sluggish compared to small ones. But big organisms don’t have to be sluggish, that is just an accident of the engineering failures of Earth biology. If there is a planet out there where biology has figured out how to efficiently scale its blood vessels, such as by using continuous pumps, the organisms on that planet will have fewer barriers to growing large and active. Efficiently designed large animals on Earth could easily have metabolisms that are thousands of times faster than in existing animals. So, if you don’t already have enough reasons to be scared of alien monsters, consider that they might have far faster metabolisms, and also very large.

This seems yet another reason to think that biology will soon be over. Human culture is inventing so many powerful advances that biology never found, innovations that are far easier to integrate into the human economy than into biological designs. Descendants that integrate well into the human economy will just outcompete biology.

I also spend a little time thinking about how one might explain the dependence of metabolism on biomass density. I found I could explain it by assuming that the more biomass there is in some area, the less energy each biomass gets from the sun. Specifically, I assume that the energy collected from the sun by the biomass in some area has a power law dependence on the biomass in that area. If biomass were very efficiently arranged into thin solar collectors then that power would be one. But since we expect some biomass to block the view of other biomass, a problem that gets worse with more biomass, the power is plausibly less than one. Let’s call a this power that relates biomass density B to energy collected per area E. As in E = cBa.

There are two plausible scenarios for converting energy into new biomass. When the main resource need to make new biomass via metabolism is just energy to create molecules that embody more energy in their arrangement, then M = cBa-1, where M is the rate of production of new biomass relative to old biomass. When new biomass doesn’t need much energy, but it does need thermodynamically reversible machinery to rearrange molecules, then M = cB(a-1)/2. These two scenarios reproduce the observed 3/4 power scaling law when a = 3/4 and 1/2 respectively. When making new biomass requires both simple energy and reversible machinery, the required power a is somewhere between 1/2 and 3/4.

Added 14Sep: On reflection and further study, it seems that biologists just do not have a good theory for the observed 3/4 power. In addition, the power deviates substantially from 3/4 within smaller datasets.

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