Guilds Mirror Real Life Street Gangs

[thinus]

Have any of you bothered to read the actual journal article, rather than Slashdot's reaction to it?

The article is in a physics journal. Physicists seek to find general models that fit a wide range of systems. In this case, he chose gangs and online guilds because he viewed them as two very different systems, thus showing that his model is robust.

The mathematical equations that govern a spring are very similar to those for the force exerted between two atoms in a chemical bond. Does this mean that physicists think that atoms are actually held together by little tiny springs? No.

One physical model for electrical current flowing through a metal is the same as a model for a herd of buffalo running through an open plain. Does this mean that physicists think that electrons are the same thing as buffalo? No.

Perhaps you should read the journal article before you start insulting the author.

Yeah, I think people hear the words "guild" and "gang" and they knee jerk in response.

The paper itself seems like it is only available for purchase. It is difficult to gauge exactly what he is modelling from the article only.

Quantifying human group dynamics represents a unique challenge. Unlike animals and other biological systems, humans form groups in both real (offline) and virtual (online) spaces—from potentially dangerous street gangs populated mostly by disaffected male youths to the massive global guilds in online role-playing games for which membership currently exceeds tens of millions of people from all possible backgrounds, age groups, and genders. We have compiled and analyzed data for these two seemingly unrelated offline and online human activities and have uncovered an unexpected quantitative link between them. Although their overall dynamics differ visibly, we find that a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range for each population. By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e., kinship or “homophily”).

So the part I don't really get is:

a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range

What are the quantitative features?
What are the attributes?

The article is too sketchy to really understand anything.

The interesting part is:

By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e., kinship or “homophily”).

So how do race specific gangs fit into their model then?

[Stealthy]

One of the replies to the article had a link to a free download - http://lanl.arxiv.org/abs/0812.2299

This reply neatly sums up the paper:

The authors are interested in the underlying social mechanism that drives group formation.
They compare two competing theories -- homophily or that like attracts like, and a theory that group formation is driven by a search for compliments -- and conclude that the latter drives group formation in *both* gangs and guilds.

In the paper, they then go on to apply a mathematical model to the formation of groups.

I thought this discussion around the physics aspect of the paper quite interesting (note - the authors are physics grad students):

The results are often somewhat unfortunate; but there is an entire genre of papers, across a variety of subjects, generated by physicists' belief that, as long as they can develop a mathematical model, they can write on just about anything. There is a similar behavior in economists, who figure that, if they can assign dollar values to the major variables, they are on safe ground.

Sometimes the results are genuinely interesting, or even downright superior, if the area has been bogged down in excessive qualitative handwaving. Other times, you get breathtaking exercises in over-reduction, ignorant of a variety of messy details that have been common knowledge, among people who actually study the subject, for decades.

And this:

Thomas Nagel [wikipedia.org] famously argued against the reductionist approach of physics and other "hard science" disciplines in his paper "What is it like to be a bat? [clarku.edu]". A rough summary of the paper is that he thinks science may be able to tell us how something works, like the echolocation abilities of a bat, but it's much harder to give an account for how it's like to actually experience something, like what echolocation actually feels like.
This is all by way of saying that you're spot on. Reductionist approaches are problematic and have widely known to be problematic for at least decades if not longer. This is not to say that reductionism is necessarily wrong - it could be the case that if we know everything physical about the world, we will know everything about the world - but it seems less and less likely to those who are not in the "hard sciences". Psychology and Neuroscience remain two distinct disciplines. You can't tell sociological phenomena simply by observing and describing in physical terms physical phenomena. And etc.
This may be an example of the latter. The sociological phenomena of groups have been well-studied by sociologists and psychologists, and we do have quite extensive explanations of group and social dynamics from these disciplines. Yet here, some physics students come in and try to study what has been studied and come to some questionable conclusions that seem to be problematic if examined from a sociological or psychological perspective (as pointed out by GP).

And finally this:

I think of the issue as: reductionism is right, but useless. Unless we can combine the countless calculations that describe the basic physical properties of some system with enough accuracy and detail to model its emergent behavior, then we cannot develop an improved understanding of that system through reductionist means. (Which incidentally is why strong AI is never going to happen.)
Or, a Monet is just a bunch of dried oily goo on some canvas, but it's much more productive to understand it by looking at it than by precisely describing the goo with equations. And one would need to appeal to an entirely different field (cognitive neuroscience) to explain the psychology of the historical context motivating the artist; and thus the art historian's approach manages to synthesize two enormous scientific fields without even needing any math...

And there was another reply with links to these interseting articles, which are kind of related:

You might be interested: Robber's Cave Experiment [wikipedia.org]
Another (not a scientific) study: The Third Wave [wikipedia.org]

All up I found the paper and some of the discussions afterwards made for interesting (and thought provoking) reading.

Cheers,
S.