Until further notice, this blog is going into hibernation mode, as I have started blogging over at Wired Science! The new blog is called Social Dimension and is about how to quantify the anthropic part of our world. And lest you be concerned about the frequency of the posts, Social Dimension will have more frequent (and longer) posts than over at arbesman.net.
It turns out that this is not the only Lois Lane. There are others, such as this one, in the suburbs of Detroit.
My wife noted that we now need a Peter Parkway. Anyone aware of that street?
With the recent announcement that Bill Miller will be stepping down from running Legg Mason Value Trust fund, a number of people have used this as an opportunity to re-examine his incredible fifteen year streak of beating the S&P 500. Running from 1991 to 2006, this record has never been matched.
However, in recent years, Miller’s performance fared worse than the market’s, with some years losing up to 50%. Many are therefore now saying that Miller’s streak was nothing more than luck. If there are enough mutual fund managers competing to beat the market, surely at least a handful must have performance streaks like Miller’s.
Happily, we needn’t speculate. We can actually subject this sort of statement to rigorous mathematical analysis. I happen to be partial to using math to understand performance streaks, having examined the math behind 1941 Joe DiMaggio’s hitting streak, as well as streaks in mutual funds.
Unlike what others have done, a more subtle approach than multiplying simple probabilities is required. Specifically, we need to recognize that the probability distribution of beating the market can vary from year to year.
As described here (and in more detail here), I worked with Andrew Mauboussin to compare the performance streaks in the real world to ones in a computationally-generated null model. Looking over many decades (1962-2008), our model used the same numbers of funds each year and the fraction of funds that beat the market as the weights for our Bernoulli trials (weighted coin flips), in order to create as realistic a null model as possible, but one where skill would play no part. Running this model 10,000 times, we then checked to see the distribution of long performance streaks.
Unlike the real world, we found nothing that approached Bill Miller’s streak. In fact, streaks of fifteen years only occurred 30 times out of 10,000 runs, far below a reasonable expectation based on luck. In fact, the next longest streaks in the real world were only eleven years long (these occurred twice). Now this doesn’t sound like much of a difference. But remember, these are streaks. To beat the market year after year isn’t a little bit harder, it’s geometrically harder. This can be seen by looking at how often an eleven-year streak occurred in the null model. While still somewhat unlikely, these occurred in nearly a third of our simulations.
The impressiveness of Bill Miller’s streak is magnified by looking at its timing. Our analysis shows that the streak, begun in the early Nineties, occurred during an unlikely period when compared to the Seventies or early Two Thousands. Surprisingly, despite the market’s good overall performance in the Nineties, it was one of the worst decades for active managers. For example, only about one in ten funds beat the market in 1995 and 1997, demonstrating that a long streak during this time is far from inevitable.
While we can never say with certainty that Miller’s streak was due to skill, such a streak is not possible on the strength of luck alone.
Image from Wikimedia Commons | Katrina.Tuliao
In a fun paper recently published in PNAS, Dynamic social networks promote cooperation in experiments with humans, Dave Rand, Nicholas Christakis, and I explored how a dynamic social network affects cooperation. Scientists have been using the public goods game for a long time to understand people’s tendency to cooperate with others. Recently, research has explored whether it’s important to array people in a network that looks like a real-world social network in order to foster cooperation. However, this doesn’t seem to increase cooperation among people.
We examined whether the secret ingredient in creating cooperation was a dynamic social network: we give people the ability to change the structure of your own social network. Someone screwing you over? No need to simply defect, rather than cooperate. You can now just stop interacting with them. Theory dictates that if we would allow a lot of rewiring, actions have consequences, and you get a lot of cooperation. And that is exactly what we found. To quote our abstract:
Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one’s social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups.
And we did this all on Amazon Mechanical Turk, a great place to run social science experiments. As Dave (my co-first author) notes:
“Lab experiments are incredibly valuable, because they let you very tightly control the experimental conditions, which you need to demonstrate causality,” Rand said. “But the thing about lab experiments is they tend to be very time-consuming and expensive, because it’s difficult to get people to come into the lab. The Internet offers an amazing opportunity for streamlining the process. But the problem has been: Where do you get the people, and how do you set these systems up?”
Developed several years ago, Mechanical Turk is an online labor market where employers can hire workers to perform what they call “human intelligence tasks” — simple, repetitive ones that are easy for humans — such as describing the content of a picture, transcribing audio or translating text from one language to another — but are frustratingly difficult to program computers to perform.
“What we’re doing is crowd-sourcing experimental social science,” Rand said. “We are now an ‘employer’ on Mechanical Turk, but instead of asking people to label images, we’re hiring them to take part in our experiments.
“From a philosophical perspective, I think this is an amazingly important technology for the social sciences, because it’s democratizing,” Rand continued. “You no longer need to be at a university that has a big lab, with a huge research budget and someone maintaining a subject pool. Now anyone who has an idea can spend a day building a survey online, post it on Mechanical Turk, and see what happens.”
I had a piece in the Ideas section of the Boston Globe this weekend about understanding the nature of empires and civilizations, seen through the lens of mathematics, entitled How Long Will America Last? An impossible question, answered with math:
With all the chatter about the rise of China, our possible economic collapse, and climate change, it is little wonder that Americans might be growing preoccupied with our nation’s staying power. Is the rise of the United States a fleeting moment in world history, or simply the beginning of many centuries of American ascendancy?
It might seem like a question for pundits to argue over, pessimists against optimists. But there is another way to answer the question as well: with some data.
History is filled with examples of powers much like America?—?nations whose wealth and influence allowed them outsized effects on the world. In the past, they were empires; America doesn’t usually see itself that way, but its wealth and influence put it in this peer group. And once we place it there, we can look at the lifetimes of lots of empires, see how long they’ve lasted, and use this to gain a bit of insight into our American situation.
This kind of approach, using a quantitative approach to understand history, is part of what has recently begun to be called cliodynamics. The field of cliodynamics?—?a term coined by the mathematician, biologist, and social scientist Peter Turchin from the name Clio, the muse of history?—?uses mathematics to understand the shape of history, and has been around for centuries. With a pedigree dating back to such approaches as that of Francis Galton, a relative of Darwin, who used math to understand the extinction of Victorian aristocratic surnames, a cliodynamic approach can be used to understand the ebb and flow of entire civilizations on a grand scale. Now, with the advent of the digitization of vast amounts of data, we can apply a certain precision to history that wasn’t possible before.
So that’s what I set out to do.
As many of you are probably aware, the estimated world population is set to hit 7 billion at the end of this month, according to the United Nations Population Fund. And of course, this milestone came relatively rapidly:
According to demographers, the world’s population didn’t reach 1 billion until 1804, and it took 123 years to hit the 2 billion mark in 1927. Then the pace accelerated – 3 billion in 1959, 4 billion in 1974, 5 billion in 1987, 6 billion in 1998.
In the spirit of mesofacts, I put the question to readers of this blog: what was the world population that you were taught in school? I learned 5 billion.
Sexual selection, like many evolutionary concepts, was first anticipated by Charles Darwin and has since been elaborated in great detail. It is a powerful concept, explaining everything from the unwieldy nature of the peacock to the changing curves of Playboy centerfolds over the years. But this is all selection at the visual level.
Just as certain appearances are more or less pleasing, there should also presumably be aural sexual selection, selection for or against certain sounds. And this happens too. There are mating calls in the wild and even cases when musical abilities among humans can be fitness advantages (e.g. guitar douchebag). But what about more subtle cases that might be selected against? For example, what about the Nanny, or at least Janice from Friends? Are certain voices or speaking styles, perhaps such as the aforementioned nasal and strongly accented, subject to sexual selection?
I recently asked Coren Apicella, a friend of mine who’s a biological anthropologist, about this. And happily, it turns out that there is a burgeoning body of research in this area. Coren studies the Hadza, a hunter-gatherer group in Tanzania, and works with them to better understand natural selection. She has examined how pitch affects reproductive fitness of men and women, using the lens of sexual dimorphism.
Sexual dimorphism is the difference in appearance or other observable traits between males and females of a single species (for examples, take a look at the many pictures in the Wikipedia article). When it comes to humans, in addition to men and women looking different, we also sound different: men have deeper voices. Coren found that, among other results, Hadza men with deeper voices do indeed have greater reproductive success and men prefer to marry women with more highly pitched voices. Intriguingly though, there is still debate about where this dimorphism arose: is it due to the choice of women of who to mate with, or competition between men? But whatever the mechanism, sexual selection on voice is clearly at work.
Unfortunately, it seems that studies haven’t been done yet on more nuanced voice traits, such as accents. But it is heartening to know that evolution has infiltrated both sight and sound. Of course, sexual selection is far from destiny. There are many people who appreciate the Nanny or other “distinctively” voiced individuals, just as many other sitcom characters are able to be quite picky regarding those traits both audio and visual.
Apicella, C., Feinberg, D., & Marlowe, F. (2007). Voice pitch predicts reproductive success in male hunter-gatherers Biology Letters, 3 (6), 682-684 DOI: 10.1098/rsbl.2007.0410
Apicella, C., & Feinberg, D. (2009). Voice pitch alters mate-choice-relevant perception in hunter–gatherers Proceedings of the Royal Society B: Biological Sciences, 276 (1659), 1077-1082 DOI: 10.1098/rspb.2008.1542
An article on msnbc.com details how rescuers worked to free a drunken moose that was trapped in a tree in Sweden. Weird and slightly disturbing, but not as much as one of the sentences in the article:
Johansson called the police, he told The Local, but while waiting for a response, he and neighbors began to saw off limbs to try to help the entangled, thrashing moose.
If you had to name one American, for instance, who clubbed together with a couple of friends in 1965 and spent more than three weeks building a futuristic seven-foot vertical city out of Lego, you might not immediately think of Norman Mailer. Thirty-three years later, however, the city still stands in Mailer’s living room in Brooklyn Heights, and its creator remains enthusiastic about his project. “It was very much opposed to Le Corbusier. I kept thinking of Mont-Saint-Michel,” he explains. “Each Lego brick represents an apartment. There’d be something like twelve thousand apartments. The philosophers would live at the top. The call girls would live in the white bricks, and the corporate executives would live in the black.” The cloud-level towers, apparently, would be linked by looping wires. “Once it was cabled up, those who were adventurous could slide down. It would be great fun to start the day off. Put Starbucks out of business.”