So a little while ago, I was perusing the latest from PLoS ONE while doing some low-attention-requiring lab work Monday afternoon, and a title caught my eye: “A test of evolutionary policing theory with data from human societies.” Oh, hey. That looked interesting.
The paper’s author, Rolf Kümmerli, claims to have found evidence for a particular kind of evolutionary model of cooperation in recent economic data from Switzerland. The problem Kümmerli addresses is a classic one: from the perspective of natural selection, individuals (apparently) have little evolutionary incentive to cooperate, unless they’re relatives. And yet, we see cooperation in human societies.
Kümmerli compiled data from the Swiss national government, comparing crime rates and police expenditures to the population size percentage of foreign nationals living in every Swiss canton, or administrative region. Rather than just use the raw population size or percentage of foreigners in each canton, he constructed an index that combines the two. And he did, indeed, find that as this index of “dissimilarity” increases, so do crime rates and expenditures on police.
Kümmerli concludes that his data support the “evolutionary policing theory.” But what has he actually shown? Crime happens for lots of reasons, not necessarily because people somehow “know” to behave more cooperatively in small towns. Most glaringly, Kümmerli’s data set includes no data on poverty, which seem like an obvious alternative explanation for the pattern—bigger communities with more immigrants also often have more poor people, and poverty is certainly related to crime rates.
Fortunately, the data Kümmerli uses, and many more variables, are all freely available online through the Swiss Statistical Encyclopedia. So I took a couple hours to play around with the raw numbers. I did all my statistical work in good old R.
For each of the 26 cantons, I compiled the number of reported crimes in 2009, the number of citizens (in thousands) in 2009, the percentage of foreign residents in 2009, the percentage of unemployed residents in 2010, annual expenditures on police in 2008, and—just for the heck of it—the percentage of commuters using public transit in 2000. As in Kümmerli’s data set, each statistic is the most recent value available. I didn’t try to replicate Kümmerli’s “dissimilarity” index because it’s not clearly explained in the paper; but I did log-transform the crime rate, the number of citizens, police expenditures, the unemployment rate, and the transit use rate to make them better conform to a normal distribution.
Here’s what the simple linear relationships among all those variables look like. Apologies for the complicated graphic, but this is a complex data set.
Linear relationships (upper triangle) and correlation coefficients (lower triangle) among variables from the Swiss Statistical Encyclopedia. Grapic by jby.
In the upper triangle of this matrix, you can see scatter plots with linear regression lines estimated from the data. Regression lines are colored according to statistical significance, corrected for multiple testing: red lines are “very” significant, orange just significant; grey lines indicate relationships no stronger than expected by chance. The bottom triangle gives the raw correlation coefficient between the variables, on a scale where 1 means a perfect relationship and 0 means no relationship.
What you should notice first is that top row of scatterplots, which show that crime rates have strong linear relationships with every other variable in the dataset, from population size to mass transit use. But that makes a certain amount of sense—all these variables are interrelated. Larger communities tend to attract more immigrants and tend to have better public transit systems that support more use. Communities with more unemployed people might have higher mass transit use, since cars are expensive. So, lots of correlation—but is there any causation in there?
There are a number of ways to tackle that question. A relatively easy one is to use multiple regression and a “model comparison” approach. This essentially builds a statistical model in which multiple variables—population, foreign residents, unemployment, mass transit use—are used to predict a single variable, crime rates. The procedure then compares the model’s AIC score, an index of the model’s ability to predict crime rates from the other variables, to models with each of the individual variables removed. If removing a variable makes a “significant” reduction in AIC—which is typically understood to be a difference of at least 2 AIC points, then that variable contributes significantly to predicting crime rates.
A Swiss public transit police car. Photo by Kecko.
It turns out that all the variables I considered make a significant difference in a multiple linear regression model trying to predict Swiss crime rates. But they aren’t equally important. Removing unemployment from consideration made a difference of 4.9 AIC points, removing the percentage of foreigners made a difference of 5.9, and removing the percentage of people using mass transit made a difference of 13.6. But removing the number of citizens made a difference of 92.9 points—an order of magnitude bigger difference than the other variables.
So it looks like the strongest pattern in Kümmerli’s data is just the effect of larger communities—they have more crime.
This is not what we scientists call a “surprise.”
Moreover, it’s not particularly informative for the purpose of the question Kümmerli sets out to answer—we don’t really know how population size actually relates to humans’ tendency to be less “cooperative,” or to need police to make them cooperative. Larger population does seem to be related to more crime, but it’s also related to more mass transit use—and mass transit use strikes me as a pretty cooperative behavior.
Admittedly, that’s a pretty off-the-cuff assessment based on a couple hours of fiddling around with simple statistical analysis of an easy-to-access public data set. But I strongly suspect that you could say exactly the same thing of Kümmerli’s paper. ◼
Kümmerli, R. (2011). A test of evolutionary policing theory with data from human societies. PLoS ONE, 6 (9) DOI: 10.1371/journal.pone.0024350
8. Because you’re training for a marathon/ putting in a lab-work marathon/ cleaning house/ doing anything that’s better with someone talking in your earphones, and you’re just not getting enough with NPR, Slate, and that one about the things from the British Museum—and you’ve already used up all the Audible.com freebies offered via those podcasts
7. To hear Dan take a victory lap after Rick Santorum lost his Senate seat (while trying not to dwell on how U.S. politics have changed/not changed/gotten ten times worse since then)
6. As part of creating a drinking game for a sex-ed themed cocktail party (maybe you’re hosting a fundraiser for Planned Parenthood?)
5. For the episode recorded immediately after Thanksgiving dinner
4. To pin down the exact moment of origin of the phrase “tech-savvy at-risk youth”
3. For every time Dan has a special guest/ co-host/ foil
2. Because back in the day Dan and/or the TSARY weren’t so selective about which calls deserved an answer
1. For the old, Monty Pythonesque brass band intro music
It’s … actually kinda plausible. But I think that, if Guillaume manages to overcome his vexation, he might also note that there’s probably some sort of marginal fitness benefit associated with landing a regular gig at Slate. ◼
Nitrogen is one of the elemental building blocks of life as we know it—it’s a basic component of amino acids, which are in turn the building blocks of proteins, which form the building blocks and moving parts of every living cell. The nitrogen interwoven in our tissues originated as part of the atmosphere we breathe, but the path from atmosphere to living flesh is far less direct than drawing a breath. Atmospheric nitrogen becomes useful to us animals only via an intimate relationship between a plant and bacterial growing in its roots.
The bacteria, called rhizobia, have the rare ability to “fix” free-floating nitrogen into biologically useable form. In return for this nitrogen source, the host plant allows the rhizobia to infect a specialized knob of root tissue, a root nodule, which it supplies with sugar for the benefit of its nitrogen-fixing guests. The plant uses the fixed nitrogen to make proteins for its own use, and anything that eats the plant afterwards benefits.
If all this sounds familiar, it’s because the interaction between plants and rhizobia is the focus of my developing postdoctoral research, and I’ve been writing about it as I’ve done more reading about it. Specifically, I’ve been interested in how plants might be able to make sure their root nodules house helpful bacteria rather than freeloaders, who enjoy the sugar supply inside the nodule without fixing nitrogen in return.
I’ve discussed a couple of different mathematical models that suggest some options. However, models are really just formal ways to follow through the implications of a particular idea, not necessarily descriptions of what actually transpires between a plant and the rhizobia inside its roots. So I thought it might make sense to step back and survey what we presently know about what goes on inside those root nodules.
Ten years ago today, I was in organic chemistry lab when the prof walked in and mentioned, somewhat casually, that an airplane had apparently hit one of the World Trade Center towers in New York. We all assumed it was some accident, and I distinctly remember picturing a small private plane of some sort.
By the time I was done synthesizing and purifying and precipitating, I returned to the dorm to find everyone gathered around CNN, watching looped footage of not one but two full-sized commercial airliners striking the towers.
Ten years later, it seems the entire United States is still gathered around 24-hour cable news, still watching the planes strike the towers. If, like me, you find it easiest to contemplate those ten years in numbers, Wired’s Danger Room has compiled an elegant series of infographics illustrating the costs and consequences of the last decade.