Social Networks for Charity

August 22, 2012
So this isn’t totally irrelevant:
My brother, Matt Bishop, has started a social enterprise whose goal is to use social media, and social network analysis (aka “social analytics”) to help charitable groups raise money, and help businesses do their part as well.
iGiveMore is launching tomorrow.  They already offer some services, but they need our help to raise money (and publicity) so that every month that passes they have more to offer.
Check out their website, and then I hope you’ll consider donating, and emailing or posting a link to iGiveMore on facebook.

Math Soc Pre-ASA Conference, Thurs. Aug 16

August 14, 2012

As was announced on the website last spring:

The fifth Joint Japan-North America Mathematical Sociology Conference will be held on Thursday, August 16, 2012 in the Colorado Convention Center, Denver, Colorado. The conference focuses on advancement of mathematical sociology worldwide and fosters friendship among those whose work is on mathematical sociology in all countries. Thus this is a wonderful opportunity to know cutting-edge topics in mathematical sociology and to meet people who share great enthusiasm for it.

To register, please download this form and follow the included instructions. If you have questions please e-mail Yoshimichi Sato.

Unfortunately I will miss most of it, but look forward to meeting people there towards the end of the day.

The Formation of the Mathematical Sociology Secion, 1994-1996

July 22, 2012

Young sociologists might assume that the section has been around a long time. I just came across this:

History of the Formation of the Section, 1994-6
by David Heise, Indiana University, Chair of the section 2003-4. This appeared in The Mathematical
Sociologist, Newsletter of the Mathematical Sociology Section of the American Sociological Association ,
Fall 2003
The first formal activity leading to the Mathematical Sociology Section occurred at a Professional
Workshop instigated and chaired by John Angle at the 1994 American Sociological Association annual
meeting in Los Angeles. After this workshop revealed genuine interest in creating a section, Eugene
Johnsen, with the assistance of a Steering Committee, produced a Mission Statement for a Mathematical
Sociology Section and, later, the By-Laws. The Steering Committee consisted of most of those involved in
the 1994 Workshop: John Angle, Stephen Berkowitz, Phillip Bonacich, Scott Feld, Sharlene Hesse-Biber,
James Hollander, Guillermina Jasso, Eugene Johnsen, Joel Levine, Timothy Liao, David McFarland, Alton
Okinaka, John Skvoretz, and Geoffrey Tootell.
A determined effort was made in the early years to bring the group’s interests to the attention of
sociologists in general and to display vital activities to the ASA. Eugene Johnsen organized and chaired a
Professional Workshop on “The Practice of Mathematical Sociology” at the 1995 ASA Meeting in
Washington D.C., with five invited speakers presenting papers. For the 1996 ASA Annual Meeting in New
York the section-in-formation proposed and received ASA approval for a Didactic Seminar by Stanley
Wasserman on social network analysis. At the 1997 ASA Meeting in Toronto, Phillip Bonacich presented
a Didactic Seminar, sponsored by the recently formed Mathematical Sociology Section


Finding Data

July 16, 2012
A friend asked me about where he might find education data to practice/play with.
Here are some links I came up with: has open-access and restricted data.


June 19, 2012

It’s been a while, i know. So something dramatic must have happened. Multiple dramatic things have happened, but one in particular drove me to dust off my WP login. This article on obesity has been rapidly making the rounds (i’ve seen it linked no less than a dozen times since yesterday afternoon). It’s got lots of interesting information in it and easily quotable lines. But in virtually every single cite i’ve seen of it so far (on Facebook, news articles, etc.), the same finding has been quoted as the most striking punchline of the article, but has, in every case misrepresented what that finding actually is. The line from the article that matters is this (from the abstract):

North America has 6% of the world population but 34% of biomass due to obesity.

The quotes of that finding i’ve seen thus far however have inevitably been framed as:

North America has 6% of the world population but 34% of biomass.

A seemingly innocuous edit, but resulting in a complete misrepresentation of what the study actually found. What the analysis shows is that if you limit the analysis only to account for the obesity in the world (i.e., we look only at the “excess” weight) on the planet, N. America accounts for 34% of that amount. Not 34% of all biomass. In order for the citation as it’s making the rounds to be true, the average N. American would have to weigh 371 kg (or roughly 818 pounds; calculations stem from Table 3 in the paper). Now part of the problem is the way things are written as most people aren’t used to having to do a little simple math to digest their research blurbs, but sometimes it’s completely necessary to be sure you aren’t completely lead astray.

Now, i’m not trying to diminish the findings in the article, as i think they have done a good job of demonstrating how weight is unevenly distributed across the globe. Just not in the way people are saying they’ve said it is.

Neal Caren on citation patterns in sociology

June 2, 2012

What articles and books have sociologists been citing a lot recently?  See the list compiled by Neal Caren.  He also made a cool network diagram showing which articles are cited together.

The Promising Future of Mathematical Sociology

May 11, 2012

I strongly believe sociology, especially mathematical sociology, has an extremely promising future. The current trends in information technology clearly indicate a growth in quantitative modeling. Among other things, we are currently witnessing a tsunami of data from a globally-connected world (in fact, big data is the techno-geek buzzword), exponentially faster computing power (Markov Chain Monte Carlo simulations of complex models are now increasingly commonplace), and a rapid uptick in the volume and range of high-quality statistical programs (a great deal of which are open-source).

However, why would I think quantitatively-oriented sociologists are especially well-placed to gain from these structural developments?

The primary reason is that the underlying epistemology of modern quantitative sociology — grounded in complex predictive models, relational and nested data structures, and a folk-Bayesian approach to research design — represents the cutting edge and future direction of modeling in a shockingly vast array of fields. For example, multidimensional scaling, social network analysis, log-linear modeling, and finite mixture models (i.e., latent class analysis) are now at the forefront of disciplines ranging from machine learning to computational genetics (for example, see here, here, here, and here). However, most promising is the growing popularity of Bayesian multilevel models, which sociologists have in effect been using for several decades now. For instance, Bayesian multilevel models are now used by physicists to measure the mysterious properties of dark energy, geneticists to unlock the basic patterns of genomic population differentiation, and neuroscientists to describe the deepest structures of the brain. It is no exaggeration to claim that a human-level form of artificial intelligence, if it is ever developed, will probably be based on multilevel models of the type currently familiar to most quantitatively-oriented sociologists.

A secondary reason why the future looks so promising for mathematical sociology is that a vacuum has been created in the social sciences due to the rise of an alternative approach to quantitative modeling, frequently promoted by mainstream economists. According to this approach, the main goal of quantitative research is to estimate population-averaged causal effects, either by setting up a randomized (controlled) experiment or applying a small suite of techniques to observational data, such as instrumental variables regression, so-called “fixed” effects (rather than “random” effects) regression, difference-in-differences design, and so forth.

This approach is appealing because it promises the extraction of causal estimates with minimal theoretical insight, but it comes at enormous costs. For example, the assumptions of causality are rarely, if ever, satisfied for any particular model fit to observational data (as painfully but clearly outlined by the counterfactual model of causality, and evinced by the growing ranks of not-really-exogenous-but-we’ll-use-it-anyway instrumental variables). Furthermore, although it’s well-known randomized experiments are inferior to controlled experiments, the latter require strong theory that is often absent (and even then experiments in the social sciences often lack generalizability to other populations). Finally, an enormous amount of substantively-rich information is usually discarded when observational data are used primarily  for extracting causal estimates, so if we don’t believe our causal estimates then we’re left with a rather meager description of the data at hand (the worst offender is the so-called “fixed” effects technique, which can be viewed as a special case of a Bayesian multilevel model in which the groups are assumed, rather unreasonably, to have infinite variance between them).

Of course, both economics and sociology are large fields, encompassing a wide range of viewpoints, so I caution that my comments embody ideal types. Yet the dominant trends of a globally-networked world, combined with the rise of a distinctive approach to quantitative modeling popularized by economists, has created the conditions for a promising future for sociology more generally, and mathematical sociology in particular.


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