The UN keeps data on migrations patterns around the world, tracking origin and destination countries and number of migrants (Trends in International Migrant Stock: Migrants by Destination and Origin). I took some time out and created this network visualization of origin and destination countries from 2010. Other years were available, but this is all I had time for.
The size of each node represents the number of countries from which migrants arrive. By far, the most connected country is the United States, accepting more people from more countries than any other place on the planet. Most areas of the network represent geographic regions. Note that Africa is clustered at the top, and pacific island countries are clustered at the bottom.
An interesting result is that countries tend to send migrants to other countries which are only slightly better off than they are. For example, Malawi sends most of its migrants to Zambia and Mozambique, and Zambians go to South Africa, whereas those countries do not reciprocate to countries poorer than them. Wealthy countries tend to be more cosmopolitan in their acceptance of migrants.
Click on the picture to explore a larger version of the graphic.
I was just checking out an article by Mark Buchanan on Bloomberg about the need to abandon the idea of economic markets as being inherently stable.
For several decades, academics have assumed that the economy is in a stable equilibrium. Distilled into a few elegant lines of mathematics by the economists Kenneth Arrow and Gerard Debreu back in the 1950s, the assumption has driven most thinking about business cycles and financial markets ever since. It informs the idea, still prevalent on Wall Street, that markets are efficient — that the greedy efforts of millions of individuals will inevitably push prices toward some true fundamental value.
Problem is, all efforts to show that a realistic economy might actually reach something like the Arrow-Debreu equilibrium have met with failure. Theorists haven’t been able to prove that even trivial, childlike models of economies with only a few commodities have stable equilibria. There is no reason to think that the equilibrium so prized by economists is anything more than a curiosity.
It’s as if mathematical meteorologists found beautiful equations for a glorious atmospheric state with no clouds or winds, no annoying rain or fog, just peaceful sunshine everywhere. In principle, such an atmospheric state might exist, but it tells us nothing about the reality we care about: our own weather.
This is true. Markets are inherently unstable beasts,as was proven by the crashes of 2000 and 2007/8. Personally, I am an advocate of free markets. The trouble is that no one can agree on what a free market is.
I recently watched a compelling lecture by development economist Ha Joon Chang, where he pointed out (rightly) that “free markets” are truly in the eye of the beholder, pointing out that even the most ardent of free market supporters in 2013 wouldn’t support the free marketers and libertarians who complained of the implementation of child labor laws in the early 20th century.
I should say, then, that I’m an advocate of the “freeest markets within reason” or “the freest markets as will support the moral ideals I hold to be important.” That is, the freeest markets as will support the protection of individual rights to freedom of expression and political thought, the preservation of equal opportunity through education and health, access to capital and social mobility.
Mr. Buchanan points put that where other sciences have accepted that there is no such thing as stability in the rest of the universe, desperate economists and their politically backward fans stick to the idea that, despite evidence of the irrationality of humans in every other space, markets are “self stabilizing.” That humans are rational (they are not) and customers can democratically select optimal prices vs. availability (untrue).
First, I am drawn to the incredible volatility of prices in areas that have the least power to influence them (developing countries).
If there were ever an example of the undemocratic nature of unbridled markets, food in developing countries would be it. Buyers and sellers are legion, yet bodies across the sea set prices with little regard to the demands of the many. In Sub Saharan Africa, stability is a fantastical dream.
Second, I am thinking of the work being done on complex systems in finance, specifically that coming out of Princeton at the moment.
SOME people aren’t waiting around with their heads in the sand, but rather are working to describe the phenomena of finance volatility, noting the increased complexity of financial markets in 2013. It would seem that deeper linkages between financial systems, though necessary, induce the very real problem of volatility. Ignoring it or pretending it doesn’t exist won’t make it go away.
Blaming government regulation and calling for a return to 19th century finance doesn’t work well either.
But that’s enough….
From this, researchers were able to deduce that sexual contact was a likely transmission route, and were able to determine roughly the incubation period of contact to onset of symptoms. This paper provided strong evidence that AIDS was cause by an infectious agent.
It’s truly hard to believe that at one time, the cause of AIDS was largely unknown, even as late as 1983. While the world laughed (I lived in Mississippi), medical professionals and researchers must have felt completely powerless, having no information on what caused the disease, how it was transmitted and no way to treat it. It’s incredible that we come so far, quickly, but a cure will remain elusive for likely decades to come.
The possibility that homosexual men with the acquired immune deficiency syndrome (AIDS) had been sexual partners of each other was studied. Of the first 19 homosexual male AIDS patients reported from southern California, names of sexual partners were obtained for 13. Nine of the 13 patients had sexual contact with one or more AIDS patients within five years of the onset of symptoms. Four of the patients from southern California had contact with a non-Californian AIDS patient, who was also the sexual partner of four AIDS patients from New York City. Ultimately, 40 patients in 10 cities were linked by sexual contact. On the basis of six pairs of patients, a mean latency period of 10.5 months (range seven to 14 months) is estimated between sexual contact and symptom onset. The findlng of a cluster of AIDS patients linked by sexual contact is consistent with the hypothesis that AIDS is caused by an infectious agent.
What they found was nothing short of illuminating. A visualization of the data can be seen at the bottom of this post.
We expect that connected Twitter users will be linked by geographic region and would expect more connections in large urban areas such as New York and LA. Far from being clustered in metropolises, people promoting Kony 2012 were located in smaller cities, such as Pittsburgh, Dayton, OH, Birmingham, AL and Noblesville, IN (wherever that is):
“The large cluster on the top right includes users from Birmingham Alabama who were some of the earliest to publicize the video. The cluster is substantially larger than the others, leading us to believe that Invisible Children had strong roots in Alabama. Additionally, the hashtag#Kony2012 initially trended in Birmingham on March 1st, a few days before the video was even placed online. Other clusters in the graph include Pittsburgh, Oklahoma City and Noblesville Indiana.” But not only were there geographical clusters, but cultural clusters as well, “This movement did not emerge from the big cities, but rather small-medium sized cities across the Unites States. It is heavily supported by Christian youth, many of whom post Biblical psalms as their profile bios.”
Amazing. Kony 2012 billed itself as a happy accident. The evidence indicates that this was a well coordinated, well funded campaign waged by a powerful religious group. “Stealth Evangelism.”
Talk To Action (thanks Jeff) is a secular watchdog organization devoted to exposing the (in my opinion) damaging and self-serving influence that the religious right has on American politics. They have recently done a series of posts on Invisible Children and the Kony 2012 campaign. The more I read about this and the more I find out about this organization, the weirder it appears to me.
Talk To Action has done some digging and found that Invisible Children receives funding from the Family and other right wing Christian sources. The Family are a powerful, though secretive US fundamentalist group and were behind Uganda’s reprehensible Parliamentary bill which called for the execution of anyone suspected of being a homosexual. Though the group denies this is the case, the evidence against them is fairly damning. It appears though, that the Family are behind much of the recent Kony 2012 craze.
Invisible Children’s founder (and creator of the Kony 2012 video) Jason Russell was heard saying the following, where he admits that IC is using the issue of suffering kids in Africa so that his batshit religious group can gain access to kids in public schools:
“Coming in January we are trying to hit as many high schools, churches, and colleges as possible with this movie. We are able to be the Trojan Horse in a sense, going into a secular realm and saying, guess what life is about orphans, and it’s about the widow. It’s about the oppressed. That’s God’s heart. And to sit in a public high school and tell them about that has been life-changing. Because they get so excited. And it’s not driven by guilt, it’s driven be an adventure and the adventure is God’s.”
This is, of course, before he got caught running around town publicly masturbating.
I should be spending more time studying for this stupid test I have to take in May. But screwing around with free network software is much more interesting. In a bid to convince myself that I have a social life, I used NodeXL (http://www.codeplex.com/NodeXL) to extract my emails from the past 4 years and was able to draw this cool picture in UCINET 6 (http://www.analytictech.com/ucinet/). The software extracts all emails sent to and from me and connects them also through persons CC’d to reconstruct a vast communication network of school and personal emails, spam for academic seminars, football ticket postings and people just writing to complain. The red dot in the center left is, of course, myself. Colored dots represent distinct groups based on their level of connectedness, most of which represent particular departments that I happened to communicate in, or open mailing lists (hence my mentioning of football tickets sales). Most everyone has sent mail to me only and not cc’d anyone else. Thus, there’s a lot of isolates (people with only one or fewer connections), but the high level of overall connectedness was surprising. Mostly, though, I should get my ass to work.
Continuing from the previous post, I performed some more analyses of the data to better understand this network analysis business.
First, we need an exploration of basic demographics. I was able to get 63 people to participate in the survey. All but 7 of the people included in the survey were female. Most everyone was from the School of Public Health, which was not surprising since nearly all recruitment occurred through the SPH open student email group. Some recruitment was done through the Rackham Merit Fellowship email group. There were 19 people who were not from the School of Public Health. The largest group represented was Epidemiology with 21 people, 9 from HBHE, 7 from EHS and 7 from Biostat. The other 19 were from other departments. A department/gender breakdown is as follows:
Respondents were overwhelmingly female. The SPH is also overwhelmingly female implying that is participation was a random act, then we would expect to have more women than men by chance alone. However, I am inclined to think that men are less likely to participate at all if for nothing else, men appeared to have fewer social contacts than women over all. The mean number of contacts for women was 20.34, whereas the mean number of contacts for men was a sad 11.7. Women had a max of 86 contacts. Men scored a paltry 26. The following graphic illustrates the sad social state of masculinity at the SPH:
So basically, if you want to spread some information or perhaps a fun infectious disease, you would have better success to target women rather than men. Specifially, you would want to choose the person who had 86 contacts. According to reliable information, this individual was at a professional conference, and had a reportedly inordinate number of contacts on that particular day.
Using UCINET 6, we were able to calculate the density of the network. 86% of possible connections between people are present within the data. There were 2200 possible connections between individuals within this particular group of people, suggesting that this is a tightly knit social group. Again, this is hardly surprising since participants were recruited out of an already established institutional context. All members of the network were reachable by any other member of the network, indicating that there were no isolates in the entire group. Every one of these 63 people were connected with one another through at least one person in the network.
An analysis of geodesic connections between individuals indicated that the mean shortest path for everyone on the list was 1, that is, it only takes (on average) a single step to get from person A to person B. However, even though average distances may be short, there may be a number of paths to get from person A to person B. If person A has a small number of possible connections to person B, then we would assume that person A and person B are somewhat separated from one another in terms of the level of social connectedness. If person A has many shortest possible connections to person B, then we would assume that person A and person B are highly connected within the social space. Here we see a break down of the mean number of possible distances between individuals by department:
Overall, the mean number of possible paths between individuals was 1.58. However, we can see that some departments are more connected than others within the group, namely Epid and Biostat, whereas other groups have low number of possible connections, indicating a level of seperateness from the individuals within the survey group. These results were not surprising in that I maintain strong connections to individuals within Biostat and Epidemiology. While I’d like to make some great conclusions about this measure, I can only conclude that my having done recruiting in groups that I am already connected with biases the results somewhat.
This measure is perhaps the most important of all the measures considered in this project. The number of possible paths between people indicate the number of possible paths that an infection may pass through to get from one person to another. Although this group appears to be fairly tightly knit, infections may only have a limited number of possible paths to get from one infected host to one noninfected host.
more later, back to important stuff….