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One of the advantages of being retired is the freedom from any kind of obligation.
You do something not because you had to but because you wanted to. During
your working years you may be interested in many things, but you do not have the
time to attend to them since your job duties come first. In retirement , you don’t
have this difficulty and are free to pursue anything you are intellectually curious
about.

Between hard sciences such as physics and mathematics and humanities such as
literature and history lie the disciplines often disparagingly termed as “soft
sciences” such as social science. Last week there was a conference on
Computational Social Science at Harvard. I decide to go and see just what the
field is all about and what people in the field are interested in. Starting from
essentially zero knowledge, below is what I learned.


Traditionally, social sciences have three avenues of research:
1.Design of surveys
2.Analysis of government data (e.g. census)
3.Study of specific problem in depth (e.g., the spread of xxx disease in xxx
province)

Because of privacy concern and expense of collecting data, social sciences were
often data-poor. However, with increasing digitalization and networking of our
lives, the situation is drastically changing. Instead, according to peoples in the
field, data are no longer a problem. In fact the situation has become data-rich
and information-poor. We are being overwhelmed with data from all sources but
don’t know what to do with them. Example: there are over 100 million blog sites
on the Internet. There is a project at Harvard that collects every page ever written
on these sites daily. But no one has the time or budget to analyze the data (yes,
big brother are watching all you bloggers). We have still quite a ways to go along
the data-information-knowledge-wisdom path.

Examples of data that are currently available:

1.Cell phone traffic data all over the world
2.Detail election data
3.Geophysical information
4.Biological and genetic information on the population
5.Purchase records of individuals
6.RFID data on everything


These data are multidimensional and inter-related. Making sense of them via
visualization, statistical analysis, and providing security at the same time are the
hard challenges of current social sciences. In time social scientists feel they will
be able to understand and predict spread of social trends such as obesity, happiness.,
etc. The discipline is on the cusp of a revolution.


Note added 4/2/2017 Readers might be interested to read http://blog.sciencenet.cn/blog-1565-866637.html about "differential privacy" and the danger of too much data