Fortunately or accidentally, I only have two classes this term. Meanwhile, they separate them into four days, so I only have two-hours class every day from Monday to Thursday. Compared to my previous schedule, it is too relaxing.
An advantage now is that I have enough time to read and think. Today I found Becker's book by chance, when I was browsing the literature on "social economics", or socio-economics. It is quite exciting, and I have realized how deep the water might be- before I was only using my naive intuition that there is something I can contribute soon.
The book I'm talking about now is
Before I was paying more attention solely to network economics, and it turned out to be that they were quite similar to each other in most sense; however, socio-economics is for sure more broad.
Moreover, I took a few hours finishing reading another book,
Salsburg, D. (2002) The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
From the name, you can see that this book is about basic statistics. To save time, I read its Chinese translation. Not very long, but very exciting - maybe I have been, and will always be attracted by mathematics and statistics. Especially the later one, perhaps due to the fact that I have so many friends in this area, is one field beyond economics that has influenced me the most, and more on the level of conception and methodology than techniques or actual methods.
While reading this book, it reminds me another book I read before, which is about the famous economist Keynes,
Robert Skidelsky, 2005, John Maynard Keynes: 1883-1946: Economist, Philosopher, Statesman
What impressed me most at that time was not Keynes' contribution to economics - although nobody can neglect that, but his ideas on probability. Until now, I still have the wish that one day I want to read Keynes' original book on probability somewhere.
I want to read Becker's book only for the reason that I need an idea for my history paper. One question I have been seeking for the answer for a while: why do we need to care about the network structure? Before, I was only arguing that the "summation is a naive way to draw the group's characteristics"; now it seems that I need to really re-think about this argument. In addition to sum or mean, people have developed distribution to help understand the world; furthermore, from central limit theorem, normal distribution can be utilized in most scenarios. Therefore, under what particular case will summation cause a severe problem?
Another thing I'm thinking about now is after reading the "Lady tasting tea", a term still remains to be explained more clearly: frequency school and Bayesian school's debate on the definition of probability. On one side we are lucky today that following Baye's idea will not be regarded as heterodox any more; on the other side, although his idea itself is very simple, how to make a perfect use of it is still a very tricky and should be dealed with carefully.
I'll stop here for now, and see whether I can gain some new senses soon. This year is too short- I need a longer time to make all things clear.
It is a little funny that while I was trying to build the psychological-economic model in partner matching, two papers appeared in front of me, and they are all about link prediction:
- Linyuan Lu y Tao Zhou, “Link Prediction in Complex Networks: A Survey,” 1010.0725 (Octubre 4, 2010), http://arxiv.org/abs/1010.0725.
- D. Liben-Nowell y J. Kleinberg, “The link-prediction problem for social networks,” Journal of the American Society for Information Science and Technology 58, no. 7 (2007): 1019-1031.
Nowadays as an economics student, one important thing you can never overlook is math. That is why sometimes I feel that I am paying more attention to math instead of those economic thoughts. Yes we always stress economic intuitions. However, when it comes to the time problem, apparently math training acquires more time. More important, you may not able to build the entire economic intuition system only through lectures. Outside of the classroom is the place where you can really recognize the sense of how economics is related to people’s everyday life. Newspapers, street talks, and even TV shows may be more helpful.
Well… a little off topic. Today when I was listening to Thijs, suddenly a question not relative to any materials from that course came to my mind. What do we really care? Explanation, or prediction power? Of course they cannot be separated with each other. However, when it comes to prediction, it seems that people care more about the accuracy instead of why this or that method is working well. The best example may be the variety of methods of stock prices predicting. Once when I attended a statistics conference, there was someone who talked about his beautiful “triangle” prediction model of stock prices. For me, it does not make any sense. First, I’m not in the stock market, so I do not care; second, this kind of prediction methods are built above the cloud – there is no foundations which can even partly support the logic beyond the seemly accurate results. It may be unfair to attribute those meaningless methods to physicians – however, more of them studied physics before and they are somehow trying to transplant those solid physical theorems to economics, regardless the fact that economics is a branch of social science, which studies the (economic) functions of the society, the interactions among people and the fundamental reasons behind human behaviors. That is why whenever I see any model directly related to physics, I’m more careful about the economic implications behind the model: at least it should not be contra-intuitive.
Now myself is trying to borrow something from physics…. Obviously the first rule for me it to be as cautious as I can. In particular, I am trying to apply the link prediction idea to economic and social network analysis. Somewhat like the debate about reduced form and structural form in econometrics, I do not want to lose the micro foundations and economic intuitions behind the fancy model itself. It is easier if I just want to predict the result, but with consideration of internal and external validity, the model should be granted with more explanation power, which can conquer the difficulties originated from the complexity of the real society. Actually, I am fond of the name of this branch of science – complex network. How can we abstract simplicity from the complicated world? That may be the common question confronted by every single branch of science, no matter social science or hard/natural science. Without idea experiments in social science, how far can we go? It is really a difficult question to answer….
Anyway, hopefully I can find out a way to introduce link prediction ideas to my naive economic model. It is always interesting to work on the boundary of two different fileds, and you are tasting something new. However, the exams are coming so fast... need to focus on reviewing first.... Will update more later.