Social Dimension

Collective Nouns for Facts

With my book about facts and knowledge coming out soon, it might be worth reviewing possible words for collective nouns for facts.

We all know about collective nouns – those words for groups of things of a single kind. From a pride of lions and a gaggle of geese, to a fixie of hipsters, these terms conjure up wonderful imagery and fill gaps in our lexicon. So, let’s say you have a bunch of facts; what do you call this? While it’s unclear what to say for a collection of substantive bits of information (though some use the term miscellany of facts), for small and relatively unimportant facts, the choice is clear: a pursuit of trivia.

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Finding the Sources of Epidemics

Network science started with trying to understand the structure of large-scale complex networks, from social connections to the makeup of protein interactions. It then proceeded to explore the behavior of processes that occur on top of networks. And one of the most important ones is diffusion: how something, such as information or disease, propagates over a network.

But what about the reverse problem? Given that something has diffused across a network, can we infer the source of this epidemic’s spread?

In a new paper in Physics Review Letters (general summary here), a team of scientists led by Pedro Pinto attempted to do this. And unlike other methods, they explore how observing only a small fraction of the nodes of a network can be used to identify the source of the diffusion process.

It all comes down to a process of maximum likelihood estimation, where the most likely values of the parameters of a statistical model can be calculated. In this case, the parameters are related to the source of the diffusion given the possible paths that the disease or bit of information could have taken across the network.

It seems to work. They compare their method to an actual cholera outbreak in South Africa in 2000 and found good results, which could be determined using only 20 percent of the nodes in the network, where the nodes represent different communities in the region. In their supplemental case studies, they explore how the leader of a terrorist organization can be identified, and even show how this methodology could be used in finding contamination sources in a subway system. They found that they could determine the source of a contamination to within a single subway stop by monitoring the behavior fewer than 20 percent of the stations:

Of course, it’s not always easy to implement this kind of detection. There are certain assumptions about how the epidemic diffuses and how the detection works, as well as the assumption that there is even only source, which don’t necessarily hold in every diffusion situation. Nonetheless, exploring the source of diffusion is an exciting problem and one that demands further attention for everything from gossip to disease.

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Metabolic Scaling of Social Insects

There is a clear order to how animals use energy. Specifically, there is a clear relationship between the metabolic rate of organisms and their masses. Known as Kleiber’s Law, this has been known for nearly a hundred years. Specifically, metabolic rates scale sublinearly with the mass of different animals, which means that as species get bigger, they use less energy per pound:



The theory and empirical work on this subject have shown that there are economies of scale related to energy use such that cells in larger, more complex animals require less energy per capita. For eusocial colonies, it has long been posited that these “superorganisms” experience similar relationships with colony size, perhaps owing to shared constraints on the delivery of energy and materials (e.g., branching distribution networks, space-filling surface area to volume constraints). But empirical evidence for these relationships is scarce. This hypothesis deserves further attention because, if unitary organisms and eusocial colonies show the same size-dependent allometries with respect to energy use, this may suggest that selection acts on colonies much as it acts on individuals.

So they tested this, finding somewhat mixed results. Metabolic rate (based on oxygen consumption) seems to scale according to a 3/4 power with mass and hence is sublinear, but it turns out that it is indistinguishable statistically from scaling linearly. However, since the results are fairly consistent across species, it is likely that there is sublinear metabolic scaling in social insect colonies.

And this is bolstered by the fact that colonies grow just like individual organisms. As seen below, just as animals start small and grow larger according to a certain shape, so do insect colonies:

This is certainly gratifying for anyone who has read Gödel, Escher, Bach and remembered its sentient ant colonies. Now just be careful with that magnifying glass.

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The Return to Downtowns versus Jewish Suburban Infrastructure

In recent years, there has been a growth in the idea of the importance and value of downtowns and mixed-use walkable neighborhoods. But at least one population has some difficulty moving back to urban centers: religious Jews. From an article of mine in The Atlantic Cities:



Despite all this renewed interest in downtowns today, many Orthodox communities are stuck in suburbia, and it comes down to one major factor: the “eruv.”

I discuss the importance of walkable Jewish neighborhoods, embodied in the idea of the eruv (check out Wyatt Cenac’s discussion of the eruv as well). But in the end, it comes down to the following factor:


As Yoni Appelbaum noted: “The broader lesson here is that the infrastructure of community is difficult to relocate, making families less mobile.”

Read the rest of my piece here.

David Shankbone/Wikimedia Commons/CC

Urban Populations of the Summer Olympics

While the Winter Olympics has often been located in places not known for their urban density and huge populations—witness Lake Placid’s two times hosting the Winter Olympics—the Summer Olympics has in general gone to large metropolitan areas.

But what have the populations of these cities been when they hosted the Olympics, and how has this varied over time? Using a dataset of urban populations over time, along with some interpolation, I was able to examine how the host city populations has varied over time:

And there actually seems to be a somewhat cyclical nature to these populations. It often seems that the International Olympic Committee seeks to distribute the host city geographically (although hardly completely evenly), which could also entail a cyclical pattern to the population of the host city, as populations are not distributed smoothly. But of course, a small cyclical pattern does not make a long-term trend.

While I have not done a similar analysis for the Winter Olympics (the populations will be far lower), it would be intriguing to see if any similar cyclical patterns occur.

Top image: Paul Wilkinson/Flickr/CC