I'm halfway into my Fulbright year. Like most anthropology graduate students at this stage, I'm dreadfully lonely. My naive fantasies about how much data I can collect and how important I am have been dashed on the rocks of reality. The future feels bleak, for myself and perhaps my family. I read, hear, see, and sense daily reminders of the cost of my absence to my family, especially my wife.  Furthermore, I feel like I'm losing the struggle to save face and prove that I am worth all the taxpayer money invested in me. First world problems wreak havoc upon my addled psychology, which is primarily the product of evolution in a much different social and economic environment.

In short, I'm going batshit crazy and I need a break before I do something foolish.

All this preamble also serves as disclaimer that things are about to get very, very (very) dark. If you're in a good mood today (or a really terrible mood), I recommend you go look at pictures of kittens instead. Jerry Coyne's probably got some up on his blog.

Part of my morning routine is browsing the news. Okay, also reddit. That's where I found this gem of a question on the askreddit board:

Would you buy a lottery ticket with 99% odds of winning $1 million and 1% odds of dying?

Aside from the improper use of the word odds (we're really talking about probability), my initial reaction to this question was, "Of course not!" Seriously, a million measly dollars...or death?" Actually, my initial reaction was a bit more colorful than all that.

Then a challenger appeared with the following argument:

You have a chance to die, by no fault of your own (read: random chance) in any of your daily activities. This includes going to work, where you win significantly less money per iteration of risk.

My first reaction to the argument was negative because the first part of it is bogus. Here's why (from my response):

Based on the 2007 social security area actuarial table, I have an age-specific probability of death of just over ~0.001. By playing the lottery, I would increase my probability of death to ~0.011. The risk ratio is 11. By playing the lottery by my own choice, I increase my probability of death by an order of magnitude. Are you kidding me that it wouldn't be my fault?

Here's that Social Security Administration actuarial table in you want to know your expected probability of death before you reach your next birthday. Convinced that I was also right in my subjective assessment of the relative payoffs to playing and not playing this hypothetical (?) lottery, I went about calculating the expected payoffs from playing and not playing, then estimating the parameters necessary to evaluate them. I repeat that calculation here to (a) see if my math was right and (b) if my math was right, go and take a bitterly cold shower, where I stand repeatedly washing my hands, crying and singing creepily the greatest hits of Simon and Garfunkel, trying desperately to recall the blissful ignorance of my youth, or at least the vicarious experience of it as I play with my daughter (Alice, please don't read this until you are older).

Here are three parameters we know:
  • Probability of winning lottery: 99%
  • Probability of dying lottery: 1%
  • Potential winnings from lottery: US$1,000,000

In my response, I argued that I should account for both my expected lifetime earnings and the expected lifetime earnings of my daughter and a likely second child (which I don't yet have but almost certainly will if I live at least another five years; I promise, Honeysuckle, someday soon!). Then I argued that my daughter's income will probably be similar to mine if I live through her upbringing, but lower if I do not. I also argued that my lifetime earnings from work (therefore my childrens') probably wouldn't change if I played the lottery given the evidence that lottery winnings are a windfall profit that quickly washes away into the ocean of terrible decision-making.

Turning these arguments into assumptions (which I admit are on somewhat shaky empirical foundations since I'm currently concentrating on collecting data for my dissertation), I denoted the following parameters:
  • X: lifetime earnings from work of myself (and my offspring) if I live
  • Y: lifetime earnings from work of my offspring if I die.

I estimated these parameters from a relatively recent study of the effect of educational attainment on lifetime earnings conducted by the Georgetown University Center on Education and the Workforce. I currently have a Master's degree, but if I live I will very likely get a doctoral degree. Therefore:

X = US$3,252,000 (lifetime earnings with a PhD)


That's a little more than $81K per year on average. Reasonable, I think (then again, I am ambitious to the point of hubris).

If I die right now, I think the chances that my daughter will get more than an Associate's degree are slim, because my death would put a lot of financial stress on my family. So:

Y = US$1,727,000 (lifetime earnings with an AA/AS)

My original calculations pretended to draw on inclusive fitness theory, but I realize now that I was doing it wrong. Here, I will attempt to do it right. Maybe Alan Grafen or someone will read this when they're bored (lol, no, nobody's going to read this), and then tell me how to do it right. But I'm feeling rather back-of-the-envelopement-or-on-a-dirty-cocktail-napkin tonight, especially because I desperately need a beer and all of the rumshops are currently closed for the night.

Inclusive fitness is the sum of an individual's traditional Darwinian fitness plus the additional fitness of others resulting from the focal individual's social interactions with those individuals, with all summation arguments weighted by the genetic relatedness between the focal individual and the recipients of aid. At the foundation of inclusive fitness theory is Hamilton's rule:

rb > c

This says that a gene governing the way an individual acts towards its social partners (e.g., helping, hurting others) will propagate if the effect of the behavior on the focal individual's fitness is less than the effect of the behavior on the recipient's fitness, weighted by the genetic relatedness between the focal individual and the recipient. 

In this case, the behavioral strategy in question is my decision to play this lottery, increasing my risk of death by an order of magnitude for the potential windfall of a million dollars. Using X, Y, and the probabilities of winning versus death, we're going to fill in the values for r, b, and c.

Let's just say r is 0.5 or one half from the expected fraction of genes shared between parent and full offspring, because I don't have the time or patience to get into all the complicated ways in which genealogical relatedness is an inadequate definition of relatedness in Hamilton's rule. 

Now let's calculate c, the effect on my fitness if I choose to play the game. If I play the game, my personal fitness (proxied by my income, which is an imperfect proxy for fitness but we can discuss that in Comments below) goes from X to X + $1 million (if I win) or $0 (if I die). My expected income from playing is (ignoring background probability of death, which is on the order of 0.001, quite small):

0.99(US$1,000,000 + US$3,252,000) + 0.01 * US$0

or US$4,209,480. The difference in my income between playing and not playing is therefore:

c =  US$4,209,480 - US$3,252,000 = US$957,480
By playing the game, I expect to gain $950K in personal lifetime income. That's already scary. If I didn't expect to have any children to support, I'd say f*ck it and play the game.

Now let's calculate b, the combined effect of playing the game on my childrens' "fitness". This is more complicated because I have to account for how many children have if I live (likely two) or die (only one, my daughter). The lifetime earnings of my daughter if I die would by Y. The lifetime earnings of both my children if I live would by X. Therefore, my expected indirect fitness from playing would be:

2 * 0.99 * US$3,252,000 + 0.01 * US$1,727,000

or US$6,456,230. If I don't play, the combined income of my children would be:

2 * US$3,252,000

or US$6,504,00. Therefore, the effect on my indirect fitness from playing the game is:
b = US$6,456,230 - US$5,504,000 = -US$47,770

Plugging in the values of r, b, and c into Hamilton's rule yields:

-US$23,885 = rb > c =  US$957,480 

Hrm. It looks like that inequality is untrue. Therefore, playing the lottery doesn't satisfy Hamilton's rule. Therefore, I no longer feel the pressing urge to set up a lottery in one bullet is loaded randomly into one of 100 derringers, one of which is fired at my skull for the chance at a million dollars.

There is hope. But right now what I need is a f*cking hug.

And I think I'll still have that shower.

(Please check my math. As you can tell, I'm not in my right mind this week.)
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This year, Edge.com asks of great minds a good question: What is your favorite deep, elegant, or beautiful explanation? And 192 great minds responded. As I read through the answers, I'll comment on those that catch my eye.

Today, I'm going to address the answer titled "You Think, Therefore I Am", from which I'll quote the first few paragraphs (but be sure to go to the Edge.com link above to see this and the rest of the answers):

"I think, therefore I am." Cogito ergo sum. Remember this elegant and deep idea from René Descartes' Principles of Philosophy? The fact that a person is contemplating whether she exists, Descartes argued, is proof that she, indeed, actually does exist. With this single statement, Descartes knit together two central ideas of Western philosophy: 1) thinking is powerful, and 2) individuals play a big role in creating their own I's—that is, their psyches, minds, souls, or selves.

Most of us learn "the cogito" at some point during our formal education. Yet far fewer of us study an equally deep and elegant idea from social psychology: Other people's thinking likewise powerfully shapes the I's that we are. Indeed, in many situations, other people's thinking has a bigger impact on our own thoughts, feelings, and actions than do the thoughts we conjure while philosophizing alone.

In other words, much of the time, "You think, therefore I am." For better and for worse.

An everyday instance of how your thinking affects other people's being is the Pygmalion effect. Psychologists Robert Rosenthal and Lenore Jacobson captured this effect in a classic 1963 study. After giving an IQ test to elementary school students, the researchers told the teachers which students would be "academic spurters" because of their allegedly high IQs. In reality, these students' IQs were no higher than those of the "normal" students. At the end of the school year, the researchers found that the "spurters'" had attained better grades and higher IQs than the "normals." The reason? Teachers had expected more from the spurters, and thus given them more time, attention, and care. And the conclusion? Expect more from students, and get better results.

The authors stress rightly that the Pygmalion effect demonstrates how we can influence another's being with our thoughts. Many believe that the Pygmalion explains what we colloquially refer to as a self-fulfilling prophecy. Think something will succeed and it will be more likely to succeed. Think something will fail, and it will be more likely to fail. This phenomenon appears counter-intuitive. Why should our opinions about how something will result influence the result? If we had just told those teachers that all of the students were promising, one might ask, wouldn't all of the students have done well?

The answer is: maybe. But the Rosenthal study has a more intuitive interpretation in keeping with the assumption that humans are (at least boundedly) rational thinkers (at least sometimes). This explanation, as we'll see, suggests there's no magical stuff going on here. The Pygmalion effect results from teachers acting rationally given the information they obtain about their students's diverse abilities.

It goes like this. Suppose teachers want students to be successful. Suppose also that teachers have limited energy to spend focusing on any particular students. Suppose also that the laws of thermodynamics apply, so that teachers cannot invest energy in two things at the same time. Suppose also that investing energy in a student produces diminishing marginal returns because eventually the student cannot absorb further information. The relationship between the amount a student learns and the energy expended on that student might look something like this:
See how the benefit of investing in a student gets smaller with each additional unit of energy spent on teaching that student? Eventually, the teacher's investment won't matter much at all to how much the student learns. 

Now suppose that we have two students. One is intelligent and motivated: the model student. The other is not so intelligent or motivated. The model student can absorb more information; so the benefit of investing energy in that student doesn't diminish as quickly as for the other student. Suppose that the model student is three times better than the other student. Comparing the learning functions for these students might look like this: 
The blue line is the learning function for the model student. Note how the red line, representing the learning function of the less talented student, flattens out more quickly than for the  model student. This is very important. Here's why.

Suppose the teacher wants to maximize the average success of these two students (actually, you can suppose the teacher wants to maximize the total success of the students; the results won't change). How much more time should the teacher spend on the model student? It turns out that the the teacher should spend on the model student an amount of time triple that of the other student, which is exactly how many times better the model student is than the other student. That's classic expected utility theory for you (some would call it the expected utility hypothesis, because human behavior appears to deviate in some contexts from that predicted by the expected value logic).

Thanks for everything, von Neumann and Morgenstern!

So what does this result mean for the Pygmalion effect? It means that we should have expected the teachers to invest more in what they believe is a better investment. The Pygmalion effect doesn't prove anything about self-fulfilling prophecies or any other such mysticism. It's just basic thermodynamics and classical economics. It also demonstrates a harsh reality of teaching: If teachers aim to maximize the average success of their students, they're going to focus less on the students who need the most help.

(Interestingly, if there weren't diminishing marginal returns to the teacher's investments, you'd expect the teachers to flip the bird to everyone but the best students.)

When it comes to my own research on migrants' decisions about how much money they spend on their family and friends back home versus themselves, diminishing returns is a crucial assumption. Whether migrants remit because they have altruistic inclinations toward their close kin, or they're maintaining relationships for self-interested reasons, they wouldn't send anything if there were linear or increasing returns to the dollars they kept in their own pockets. Since migrants from developing countries send more money to developing countries than official development aid organizations, we should be thankful for the existence of diminishing marginal returns.

For my own part, whatever the reason I care enough about my own family to send something home every month, I'm also thankful for diminishing returns. In fact, if there were no diminishing returns to anything at all, neither of us would have gotten out of bed this morning. Chew on that.
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If you've ever taken a statistics course, you've probably heard of the birthday problem. If you haven't, the birthday problem asks what the chances are that two people in a room full of, say, n people have the same birthday. It's a fun problem to solve in a class of 23 or more people because it turns out that there's a greater than 50% chance that two out of 23 individuals were born on the same day. More importantly, it turns out that nearly 100% of probability theory newbs think the chances are much lower.

Also, I pulled one of those statistics out my butt. Guess which one, then realize it might not be far off the mark and forgive me.

Today, I was reminded of the birthday problem while I was reconciling church birth records with a genealogy for this village, which I've been improving since 2010 by combining key informant interviews with archival research. One of the last records I checked was a woman whose first initial is J., and who was born on my birthday in 1927, 57 years before I was born. 

"Wow," I said to myself, "I found someone with my birthday, what are the o..."

At that moment, the Debbie Downer of statistical reasoning blew her sad trumpet to remind me of the birthday problem, which I remember from one of my game theory courses in which we reviewed probability theory before getting into the Bayesian equilibrium concept. No, Brash, sighed statistical reasoning, it's not that special.

I felt like most women feel when I them that the whole menstrual synchrony thing is (probably) complete bullshit, as Cecil Adams summarizes with his usual aplomb.

I slapped myself out of my statistical-reality-induced funk and thought, "Actually, it's kind of cool that I'm almost guaranteed to share a birthday with one of these people." Then I hopped over to Wikipedia to review how to calculate the probability to avoid thinking hard at midnight after 12 hours of work. Finally, I calculated the vanishingly small probability that I don't share a birthday with any of the 1,326 records in my genealogical database.

I'll skip the details and tell you that the probability is zero. Here is a good example of how scaling up a problem can help you understand it. We can reduce the birthday problem to the absurd and envision the probability that I don't share a birthday with at least one other person on Earth, which currently nourishes about 7 billion sacks of living, breathing, human meat. But you'd think I was an idiot if I thought about calculating the probability in that case. Of course someone shares a birthday with me. Hell, on my birthday this year, about four people will be born every second.

Thank about that. The second you were born, three other beings were likely screaming their way into this confusing, beautiful world. Each birthday you have you share with enough people to enlist in almost 4,000 Roman legions.

I only wish that the scaling up problem worked in my personal life. Alas, I have an n of only three. The simultaneous trials and triumphs of parenthood, grad school, and living apart from my family must remain seeming paradoxes until I get the time to sort them out the old fashioned way.
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The only popular thing I've ever written is a flawed attempt at snarkily summarizing the results of a peer-reviewed journal article about a voluntary public goods game with a destructive opt-out strategy. The article I covered was published in the Journal of Theoretical Biology. It's trending on reddit.com for the second time (thanks for the heads up, B.T.!).

The piece is terrible looking back on it. I was trying my best to appeal to the website's audience (people who like Cracked.com) but also cover an intellectually stimulating topic within my own field. As many in the reddit thread note, it just made me seem like a pompous a-hole.

Then again, that was the purpose of creating my Brash Equilibrium persona in the first place. Brash is a pompous, pretentious, albeit badass man of mystery who sports a mustache, cowboy hat, and a smug grin, and who crosses his legs Euro-style like a freakin' boss.

Oh, dear. That reminds me that I actually wrote another God-awful thing that went viral on the topic of crossing your legs Euro-style.

Ah. Writing snark for extra cash while also trying to be a legitimate social scientist. That was a fun year.
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(Sweet Jesus this post needed some editing!)


One of the missions of my field work in Dominica is to develop a comprehensive genealogy for this rural community so I can measure the relationship between kinship and sharing between migrants and non-migrants.

It's common knowledge that there are a few very common surnames in this village, but the conventional wisdom doesn't cover the rest of much less common surnames. I tabulated the frequency of different surnames and ordered them by rank to learn more.

Below is the discrete distribution of surname frequency by surname rank (higher rank means higher frequency; there were lots of ties, especially at lower ranks). Because there is good reason to believe that the distribution of surname rank should follow a power law distribution, I also fit a power law and calculated the R-squared value.

A power law, by the way, simply means that the frequency of something that interests you (in this case, surname rank) can be calculated using a formula like this:

(frequency of surname rank) = a(rank)k

where "a" and "k" are parameters that you estimate from real data.
From the R-squared (and the graph), it's clear that the power law model does a good job explaining the variation in surname rank, especially for such a small sample (small relative to, for example, the sample of surnames this guy used in his analysis).

Because we'd expect surname distributions to follow a power law, you might think this analysis isn't particularly surprising. And you'd be right. While the ubiquity of the power law in nature and the reasons thereof are fascinating, it is a pretty well studied phenomenon.

What I don't think is well understood is how universal the parameters ("a" and "k") of the power law distribution are in a given context (i.e., in the distribution of surname rank across different populations), and what mechanisms influence variance in those parameters. Similarly, the fit of the power law distribution to actual surname rank data seems to vary somewhat across populations. Why? The answers to both of these questions may have something to do with the cultural, social, demographic characteristics of a population.


In this community, for example, high rates of out-migration (a net loss of about a fifth of the population between 2001 and 2011) likely affect the current distribution of surnames. Members of the same family tend to migrate together, the psychology underlying the motivations to migrate may run in the family, and some families tend to migrate to destinations where their family members already reside because it eases the transition.

How does this population process influence the power law (and fit thereof) of surname rank relative to, for example, a growing population with rapid IN-migration?

I don't know and I don't have much time right now to investigate. But it's an interesting question.
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Did you know that there is a website called Whale.fm where you can do your part to help marine biologists decipher the eerily beautiful language of whales?

There are all sorts of similar research projects out there tapping into the wisdom of the crowd to deal with difficult problems and large datasets. It's been called crowd science or citizen science. Thanks to the Internet, humans are inventing new ways of thinking that aggregate our grey matter.

Someday I'll have a genealogy or some other dataset large enough so that the time investment in developing a citizen science platform would be worthwhile. For now, I'm thinking about embarking on what I will call open source noodling. And I want you to tell me whether or not this is a really bad idea.

Open source noodling puts together two terms, "open source" and "noodling". Open source, of course, refers to a mode of production that involves free access to and distribution of an end product's design. Many argue that open source bolsters innovation and levels the playing field. Scholars in many disciplines, including my own, are debating a shift to open access peer review from the corporate, for-profit peer-reviewed journal model. Open access is very similar to the open source philosophy.

Noodling is a term I first heard in conversation with Richard McElreath, an anthropologist at UC Davis who studies cultural transmission and lots of other stuff. Basically, it's the use of abstract mathematical models to answer concrete questions like, "Why are humans so reliant on social learning?" or..well..anything really, if you build the model properly.

If you put open source and noodling together you get the development of a mathematical model to answer a question, which is built collaboratively using a system that allows open distribution and free access to information.

What I want to do is take a mathematical model I've been working on, and which is basically finished, write a draft manuscript, and post it in a medium that allows for open discussion and possibly even open editing. This sounds like a terrible idea but I'm playing with it, and want you to come and play with it, too.

The benefits of open source are manifold. I could tap into a huge knowledge base to solve a problem that I think is really interesting. It bypasses the slow process of peer review while also potentially improving a manuscript in preparation for the peer review format. Yet open source also poses new problems about who gets credit for the research, how to filter information, and how to aggregate the wisdom of the crowd into a singular product.

How would you set something like this up? I encourage open (heh) discussion about it. Or contact me personally, whatever. It's an idea I'd like to develop into a systematic method of producing and distribution knowledge.

I'd also appreciate it if you told me if I was reinventing the wheel, and if there are already venues out there where open source noodling can be done in a way that maximizes the benefits but minimizes the costs.

Anyway, here's an example of something I'd like to submit to open source noodling. I have other things, too, but here's one for starters

In between genealogical interviews with key informants and preparation for the rest of my field surveys, I'm working on some side projects to pass the down time.

The one I'm focusing on now is part of a project I'm calling "the tragedy of hawkish altruism". The tragedy of hawkish altruism is this:

If individuals who are aggressive to members of their own group make that group better prepared for competition with other groups, it may drive the evolution of aggressive behavior that leads to more conflict within groups. In this scenario, hawkish bastards are also tragic heroes because they risk their necks training for and maneuvering on the evolutionary battlefield against other groups. 

Think about warriors risking severe injury to realistically train for battle. Think about office intrigue and backstabbing that prepares people for the rigors of negotiation with other firms.

The tragedy of hawkish altruism ties into a broader debate in evolutionary biology about the strength of natural selection among groups versus individuals. Natural selection needs variation in order to happen. When individuals move freely between groups, it causes all groups to be more similar to one another and for most variation to exist among individuals. For this and other reasons, evolutionary biologists have traditionally considered individual selection to be stronger than group selection.

Yet a few evolutionary biologists argue that group selection may still be an important force in some cases. For example, the tendency of humans to conform to the customs most common in their current group may allow variation among groups to increase relative to variation among individuals, causing group selection to overpower individual selection. 

Researchers have used this reasoning to support a hypothesis for why humans are an especially cooperative species, even in large groups of unrelated individuals, and even when cooperation requires altruism, which would be selected against among individuals but favored among groups (that characterization of humanity, by the way, is itself a hypothesis that I don't think has been adequately tested; at least not the part where cooperation is assumed to be altruistic). 

Anyway, this so-called cultural group selection explanation for the cooperativeness of humans is contentious for many but compelling for some. Unfortunately, some people I've spoken to during academic conferences and after-school happy hours have clearly misinterpreted the argument as meaning that group selection leads inexorably to socially desirable outcomes.

Um. No. In fact, some research suggests (though not without substantial controversy) that group selection could potentially explain warfare if warfare entails individually costly contributions toward common defense or offensive raids that benefit a group. Hard to argue that warfare between groups is socially desirable.

Back to the tragedy of hawkish altruism. I've developed a mathematical model showing that group selection cannot only explain conflict BETWEEN groups. It might also explain conflict WITHIN them.

Where the crowd science and open source noodling comes in is that I want to post an early version of my manuscript and hold an open discussion about it before I submit it for publication. Hell, maybe the discussion might result in a collaboration. Scientists share their unpublished manuscripts all the time with their colleagues. And there are many many many scientists out there who could be my virtual colleagues. Why limit it to the people I know personally?

My ultimate goal with this tragedy of hawkish altruism stuff is to develop several quantitative models of increasing complexity while amassing a large, cross-cultural dataset to test the assumptions and qualitative predictions of those models. In fact, one of my post-PhD plans is to apply for a Harry Guggenheim Research Grant (they fund research on dominance and violence) to do this research.

Anyway, I'm looking for suggestions on how and if to do this kind of open source noodling in a way that maximizes the coolness and soundness of the science while minimizing the possibility for unforeseen bad stuff to happen. So how about it?

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Our ancestors once common, as are we,
All slaves to wanton anisogamy.
Theirs was visceral; ours is digital,
With zeal suboptimal 'til I return.
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Like most academics, I overstretch myself with side projects and collaborations. Whatever. Now I am in the field, and there is ample downtime despite all the surveys I've got to do.

So here is a list of side projects--two lone wolf, two collaborative--that I hope to finish during downtime.

1. the tragedy of hawkish altruism

I've developed a mathematical model to analyze the conditions when group selection can increase within-group aggression beyond an individually optimal level toward a higher group level optimum when the byproduct of within-group aggression is better performance in inter-group contests. The model is cool for two reasons. First, it flies in the face of a growing fallacy--fueled by mathematical proofs that group selection can in some cases lead to within-group altruism--that group selection is something that leads to socially desirable outcomes. Jung Kyoo Choi and Sam Bowles and others have already argued that group selection can promote inter-group warfare. I'll argue that group selection can promote people being shitty to their brothers in arms.

2. the joker effect?

A while ago, some people published an article in the Journal of Theoretical Biology showing that the presence of destructive individuals can promote cooperation in a voluntary public goods game. I submitted a short letter outlining some reasons why the published model doesn't present a strong solution to the collective action problem. The reviewers didn't like the letter, to say the least, and I cried into my academic pillow before resolving that I'm still worth a damn. I'm still convinced I am right and am going to resubmit the letter as a full article, addressing all of the reviewers concerns and improving the paper greatly.

3. human-environment interaction

I've been collaborating with a sociologist, a public affairs researcher, and a geographer. We are comparing the explanatory power of different measures of environmental quality when predicting rural-to-urban and urban-to-rural migration in Nang Rong, Thailand. We're using a pretty unique, very large, truly longitudinal migration history dataset, and developing some sophisticated statistical tools to tackle the problem. 

My colleagues will present the first paper associated with this project at the Population Association of America meetings this year. This project has a lot of potential. We're assessing the predictive value of easy-to-obtain, global scale environmental measures (El Niño/La Niña-Southern Oscillation events), versus local scale remote sensing data measuring plant health from light reflectance, which requires several steps of time-expensive processing. 

We're using high-level signal processing methods to find environmental measures that match the rules of thumb people use when making difficult decision about where they should live. Anyway, I want to help my colleagues finish this paper and others we've got in the works, to the best of abilities.

4. Parental Investment Vignette Project

Dr. Geoff Kushnik has designed a clever experiment meant to highlight the factors that influence mothers' decisions about the tradeoffs they face when investing in their children. He wants to make this project cross-cultural. And I want to help him do that. Plus it will be pretty easy to get the n of 40 women necessary to participate.

other stuff...

In addition to the projects above, there are three other things I'd like to get off the ground while I'm here, and they're all related. First, I want to assess the need and desire in these communities for community designed and constructed playgrounds using the KaBOOM! community organization methods, but building with local materials.

Second, I've gotten people here interested in the idea of organizing an annual 12 kilometer run/walk event as a way to raise funds for community projects, tapping local business and athletic fervor, plus the Dominican Diaspora. I want to make sure that this is a feasible thing to do, and that people want to do it, and that people want playgrounds before I go too much further.

Third, if the playground thing flies, I want to take the advice of Rick Kauffmann, student of David Sloan Wilson and Evolution Institute affiliate, who said I should investigate the effects of different playground designs on community level outcomes.
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Grandma. 02/29/2012
 
"So," she begins, her lips jerking in the digital strobe of low-bandwidth Skype, "We are going to get pregnant when you visit in June, right?" She glowers at me in mock seriousness, her eyes flickering down and to the right to glance at the image of herself inset there.

I can hear our two-year-old daughter gabbing with my brother-in-law in the kitchen. He's been sick for the past week or two, unaccustomed to the constant daycare siege on his immune system, which began when he moved in a few days after I left for Dominica. His adventures in sinusitis and my mother-in-law's suicidally busy schedule have left my wife on her own with my daughter for at least a week, which wouldn't be so bad if she wasn't taking a blowtorch to the other end of the proverbial candle at work.

She looks so tired. It paints a pretty glaze on her eyes that I wish the choppy video feed would let me see better.

Meanwhile in Dominica, I don't have to cook. I don't have to drive. I live in a tropical paradise. Truthfully, I haven't anything to worry about on this island but my work, for which I write the schedule and I make the priorities. Nothing troubles me otherwise but writing about how the burden I've placed on my own wife's shoulders is related to the burdens men have placed on women's shoulders since everyone's most recent common ancestor was not in diapers. That and learning to properly wield a cutlass.

Hell, last week I danced for six hours straight during Mas Domnik, this island's version of Carnival, literally moving until I exhausted myself and got extremely ill, all in the name of inter-cultural exchange. And believe it, being a béké (that's Kwéyol for "White guy") who can dance has bolstered my rapport enough that I think it will have a measurable effect on the response rate to my surveys. But as I cheeped away behind a lapo cabwit band, I tried to stomp down the guilt with every upbeat step at the same time my wife would be leaving the office for that second shift at home.

For these reasons, I reply to my wife-computer, "I can't believe that after all I'm putting you through, you still want to have children with me or even hang out with me at all."

"God, these hormones!" my wife exclaims in agreement, cursing the glycoproteins and steroids that have transformed her from a child-hating puppy-lover to a breastfeeding, baby-oogling, no-epidural-using badass in the blink of her hypothalamic pituitary gonadal axis. At the "o" in "hormones", her face freezes on the screen in what, frankly, looks like an O-face, which I savor quietly during a lull in our conversation.

Give a lonely Fulbrighter a break, man.

"Seriously though," she says, suddenly sitting in a different position, "Sometime soon?"

I wish I could tell her how desperately I want to see every beautiful permutation of our two faces despite it all. Despite the two years I have of writing after my return. Despite my lack of certainty that this little adventure will give me an edge in any job market, especially that of the itinerant academic, and never mind the two months after my return before I can take a teaching job.

While I tell her, "Someday. I hope sooner than later," I forget that people in some places manage to have children even though they are malnourished and energetically stressed. In that ignorance, I ask myself, why now? Why when things seem darkest for us, while my mother-in-law is working hard investing her time in mid-life certifications, while I am 4,000 miles away and may not have a job when I get back, do we entertain the idea of a baby?

Later, while clicking on abstracts in my peer-reviewed journal RSS feed until all the links turn grey, I come across this article in Proceedings B and misguidedly relate its finds to my personal life, as many scientists do but don't admit. The paper's abstract reads:

There has been a recent increase in interest among evolutionary researchers in the hypothesis that humans evolved as cooperative breeders, using extended family support to help decrease offspring mortality and increase the number of children that can be successfully reared. In this study, data drawn from the 1970 longitudinal British cohort study were analysed to determine whether extended family support encourages fertility in contemporary Britain. The results showed that at age 30, reported frequency that participants saw their own parents (but not in-laws) and the closeness of the bond between the participant and their own parents were associated with an increased likelihood of having a child between ages 30 and 34. Financial help and reported grandparental childcare were not significantly positively associated with births from age 30 to 34. Men's income was positively associated with likelihood of birth, whereas women's income increased likelihood of birth only for working women with at least one child. While it was predicted that grandparental financial and childcare help would increase the likelihood of reproduction by lowering the cost to the parent of having a child, it appears that the mere physical presence of supportive parents rather than their financial or childcare help encouraged reproduction in the 1970 British birth cohort sample.

That bold statement catches my eye. Is that it? That even though my mother-in-law is a busy lady during the school year, her "mere physical presence" encourages us subconsciously toward another child? If so, what is the mechanism? Surely it isn't just the presence of her that spurs us on, but something more. Those extra dishes we don't have to clean tonight. The matching shirt and skirt that I think is too girly but will help clothe my daughter for the next few months. Someone to talk to after Alice is put to bed and grandma gets home from the night shift and neither of us can sleep.  Time. That is the most valuable currency to a parent. And time is what she gives us in the little things that add up and get taken for granted. That wouldn't end up in the metrics derived from the 1970s British cohort study. 

Calm down, Hanowell, the scientist in me warns. You're getting ahead of yourself. Extrapolating beyond the limits ofone study. Using a logistic regression model to predict a new data point without using cross-validation to avoid overly optimistic model fit. You have an n of 3, Hanowell.

Yes, I say to myself, all of that is right. But at another level, I have a data point for every moment from my daughter's birth to now. My qualitative, post hoc, idiographic analysis of those moments yields the conclusion that we'd be lost without grandma, busy as she is.
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I'll continue with the "Skiing Black Diamond Down Demographic Trails" series next week. This week, I'm changing gears.

For the past 20 years or so, scientists have used sophisticated mathematical models to demonstrate unity among three, previously separate fields: demography, population genetics, and cultural anthropology. Reams and reams of peer-reviewed paper with meaningful Greek symbols and mathematical operators on them show that that the manner in which ideas and social norms are transmitted between minds--that is, culture--is analogous to the way genes are transmitted among organisms.  What's more, the two transmission mechanisms can influence one another. 

For these reasons, you can study the evolutionary fates of ideas using similar mathematical tools as you would to study the fates of genes or species. These tools employ demographic measures at their base. Sweet, right? So I was fresh out of being indoctrinated into this dual-inheritance way of thinking when I took a course in formal demography.

One thing I learned in that class is that, unless all mothers in a population complete their reproductive careers with the same number of children, you will always calculate a smaller average family size when asking post-menopausal mothers about the number of children they've had than if you had asked their children about how many kids their moms had. Here's a good reference by one of the authors of my demography course's textbook.

People use this fact to warn against collecting data from offspring about their parents' completed fertility, or to devise means of correcting it. Others note that the two measures have different social effects. I think this bias might also be significant to the cultural transmission of family size norms and the evolution of some reproductive behaviors.
Picture
"Oh, okay, I see where you're goi--whaaaaaaaaaa?"

I'll explain, starting with the reason for the bias. Say you've got two mothers, a blue one and a green one. The blue mother has three blue babies because she just loves children. The green mother is like, "Ef that," and sticks with one.
Compute the average number of babies per mother. You should get 2 as your answer. So the average family size from the moms' "perspectives" is two babies. 

Okay. Now let's let the babies grow up and ask them how many babies their moms had.
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Look at 'em. All grown up!
Now compute the average number of babies in the thought bubbles above to get the average family size from the kids' "perspectives". The answer is 2.5 kids on average, half a kid more than we calculated from the moms' "perspectives". Might not sound like much, but in developed countries, 2 children per woman would eventually lead to population decline (curious why?) while 2.5 kids per woman is significantly higher than any national total fertility rate recorded in the United States since the Baby Boom ended and The Pill began. So demographically speaking, the difference is huge.

Now let's compare the two images to see what's going on:
Three blue babies got to grow up and say their mom had three babies, but only one green baby grew up to say her mom had one baby. The family size of the blue family is sampled three times, but the family size of the green family is sampled only once.

So the general problem is that when you ask kids about their family size, you oversample the bigger families. There's a formal mathematical relationship between the two calculated family sizes:

(nifty formula 1)
The "C" with a bar over it is the average family size from the kids' "perspectives", the "P" with a bar over it is the same but from the moms' "perspectives", and the squiggly thing with a two over it is  variance in the moms' average family size. For a bonus point, you can check and see if the formula works in my simple example.

It's clear  from nifty formula 1 above that the greater the variance in the number of kids a woman has (that is, the more diverse family size is from the mothers' "perspectives"), the greater the difference between the two "perspectives". Only if there is no variance (i.e., all women have the same number of children) are the two calculations the same.

Okay, now that we've got the basics down, just what the Hell does any of this have to do with cultural transmission, much less the evolution of reproductive behavior? I don't really know yet, but I have some half-cocked ideas that I hope will provoke discussion. Here is one of them. Another will come soon, but it is even less ripe than...

...half-cocked idea #1: cultural transmission

Scholars of cultural transmission have defined types of cultural transmission in relation to the generations to which the senders and recipients of cultural information belong. Vertical transmission is when ideas pass between parents and offspring. We usually assume transmission from parent to offspring, but obviously the opposite can happen, too (but not likely when it comes to completed family size!). Oblique transmission is the passing of ideas between any member of the parent generation and any member of the offspring generation. Horizontal transmission is the passing of ideas within the same generation. There are other types of cultural transmission, and heated debates about whether these categories are meaningful. Let's ignore them for now.

Cultural transmission theory also distinguishes between two types of learning: social learning, which is basically a dependence upon culturally transmitted ideas, and individual learning. In this case, when I talk about individual learning, I mean individual sampling of a local social norm regarding family size by observing the number of kids people have.

Okay. Suppose that people's reproductive decisions (thus their completed family size) depends in part on their perception of the normal (i.e., average) family size. Perhaps knowledge of this average family size is a cue about the quality of the environment. Most people aren't demographers, and certainly nobody was a demographer 200 thousand years ago. Group sizes were small in the distant past, so maybe you could just get a sense of the normal family size by looking around. But perhaps one would benefit more by having knowledge of family sizes in other groups to get a better estimate of the quality of the environment, which makes the count more labor-intensive. Furthermore, evolution did not stop 12 thousand years ago, when groups became larger as people became more sedentary. Who has time to sit down and count babies?

(Answer: human behavioral ecologists...sorry, it's an old joke about my own discipline.)

So, if you want to know about the average family size, you'd best utilize several sources of information: your own observations of yours and others' family sizes (individual learning), and information you get from others (social learning). The degree to which you depend on individual versus social learning depends on the costs of social learning, so the peer-reviewed literature says.

If you depended only on individual learning, and assuming you randomly sampled your observations of people's family sizes, you would have an unbiased estimate of average family size. 

What if you relied solely on social learning? If your social learning relied solely on vertical transmission, you would have have an estimate of average family size biased toward the size of your own family. If you relied solely on oblique transmission, you would again have an unbiased measure because you'd be randomly sampling from the parent generation. Yet if you relied solely on horizontal transmission, you would overestimate average family size because, as in my simple example, you'd over-sample large families.

So if natural selection could independently tune the squelches on each of these types of social learning and modes of cultural transmission, and if an accurate estimate of average family size promotes evolutionary fitness, then you would expect humans to favor a mixture of oblique transmission and individual learning. The degree of individual versus social learning would again depend on the cost of individual learning.

There is, however, reason to believe that there are limits to the fine-tuning that natural selection will allow. Learning is a complex trait, especially in humans. While flexible, it is also general. We use it for many tasks every day, and we have a finite amount of energy to spend on optimizing the way we learn to each and every context. In addition, humans tend to socialize within age groups. Paired with the fact that individual learning is costly, our general reliance on social learning within peer groups might infect our estimate of average family size with the bias I discussed above...another example of how cultural transmission can lead to maladaptive behavior.

I'll talk about my other half-cocked idea soon, which applies to the family size bias problem and the evolution of reproductive behaviors. For now...

DISCUSS!

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