Quantitative Social Science

I was reading a few reviews of Duncan Watt's Six Degress of Separation, a book that proposes a way to combine models between small-world networks (groups of interconnected individuals within a corporation or other group) and random networks. I may be completely missing the point, so I apologize. All of the reviews were on the Journal of Social Structure's site, and one comment jumped out at me.

In her review, Martina Morris writes:

"This book poses a real challenge to the social sciences, to get their collective technical acts together. How to do this is an interesting question. But it will require, at minimum, either teaching real social science to undergraduates (so that they come to grad school armed with a theoretical background and we can focus on teaching them good methods) or enticing mathematics BAs into the social sciences (so we can focus on teaching them social theory in grad school)."

I just wanted to point this out, as I believe it's a real problem as well. The more I discuss mathematics with students focusing specifically on the social sciences, the more disheartened I become. While I don't expect students to become well-versied in linear algebra or topology, I do believe that having a basic foundation in statistics, a comfort in solving equations will allow you to understand social problems in a better way.

A great deal of important social science tools are mathematically based. Amarty Sen won the Nobel Prize for his work in social indicators and social welfar, much of which includes development of axioms and mathematical tools for measuring such problems. Even more simply, understanding things like the Human Development Index or Purchasing Power Parity requires a basic understanding of mathematics.

The quote comes from the Journal of Social Structure, but may be applied on a larger scale, and I haven't even begun to discuss the importance of institutes like Harvard's Institute for Quantitative Social Science.