[中文版]

Wenhui Lyu

Feb. 1, 2022

Professor Li brought up an anecdote in the class. When he presented our course Computational Practices for Social Science Research at a conference in China, many professors were surprised, “Could liberal arts students like us handle such a course with intensive coding demands?” Professor Li answered, with pride, “Yes, they have done very well.”

Over the years, Professor Li has devoted himself to the “new liberal arts” reform in China. Disciplinary intersection is one of his major efforts. The bias towards LA students has always been a formidable obstacle to deeper disciplinary integration. It is widely believed in China that LA students are incapable of logical thinking but prone to emotional outbursts. We are seen as unsuitable for intellectually intensive disciplines that involve logic and numbers. In inviting me to compile my assignments for his course, Professor Li hoped to break such stereotypes. Study on the history of western philosophy had dominated the three and a half years of my undergraduate studies, which made me a “typical” LA student. Before Professor Li’s course, I had only learned Python for one semester, yet not in depth. However, a LA student like me and other LA students in our class have perfectly lived up to the demands of this course.

Of course, when we were working on the assignments at the beginning of the semester, we encountered considerable difficulty and were once even frustrated. In my past academic training in humanities, I had only worked with “natural languages.” The object-orientedness of Python and the algorithms which center on variables baffled me. I spent eight good hours on our first assignment (7 hours on debugging), although it was only 100 lines of code. All of this started to change since the fourth assignment. I wrote 200 lines of code that time, and my algorithm used recursion three times, all of which I finished within 2 hours. Since then, coding has started to feel a bit effortless for me: although I still have to look up documentation now and then, I have the feeling that everything is in my grasp. I have the feeling that, no matter what, I could always shoot and kill the trouble.

That is to say, I indeed struggled with programming at first, as did the rest of the class, but isn’t that what happens to anybody who undertakes something unfamiliar? I believe that a “nerdy” science and engineering student would find it just as difficult to understand Kant‘s Critiques. There is no need for a LA student to attribute the initial frustration with programming to his or her lack of intelligence and abstraction ability, nor should the Chinese society imply and reinforce such stereotypes. In fact, with practice, it should be no more difficult for us to excel at computing than is for a CS student to excel at Kantian philosophy.

Not only should we dispel the stereotype of humanities students as incapable of coding in a negative sense, but, in an affirmative sense, humanities students also have their unique strengths. Computer science and information technology are more than algorithmic design. Their theoretical implications extend to various disciplines, and their practical efficacy reaches all corners of society. That is why and where humanities and social sciences could make distinct contributions.

Professor Li’s course planning took full account of the characteristics and strengths of LA Students. It took societal phenomena and key studies in social sciences as a starting point. He then guided us to build a computational model on them congruently. I believe this is crystal clear to those who have already read this book. Another example is our final assignment. We were asked to select a classical problem in social sciences and algorithmicize it. We studied various topics, including democratization, K12 education in Haidian, anti-Semitism, and macroeconomic fluctuations. While the programming part comprises no hardship for a student with a CS background, to combine algorithms and political theories as congruently as we did requires years of accumulation in social sciences and humanities.

Interdisciplinary efforts are an antidote against stereotypes. They make it possible for us to break through previous barriers and think bigger. On the one hand, liberal arts students could code well and think extraordinarily computationally. On the other hand, disciplinary intersection breathes vitality into traditional disciplines. I admire Professor Li’s efforts and am honored and delighted to share my homework. Hopefully, our pamphlet would come to the aid of the LA students who want to improve their computational ability but were scared off by societal bias. Hopefully, the spirit of the course and the pamphlet will pass on to farther and farther places.

Table of Contents

1. Foundation of Social Network
   Assignments:
   codes calculate clustering coefficient and embeddedness
   codes corroborate friendship paradox
2. Homophily and the Evolution of Social Network
   codes simulate the network evolution under the effect of homophily
3. Polarized Relations and the Stability and Balance of the Network Structure
   codes determine whether a network is balanced
4. Foundation of the Game Theory
   codes simulate the iterated prisoner's dilemma (IPD)
5. Matched Market
   codes simulate how a market comes to perfect matches
6. Network Effect in the Market
   codes compare different pricing strategies in a market with the network effect
7. Cascade in a Network
   codes simulate the network cascade
8. Popularity and Power-Law Distribution
   codes simulate the Matthew effect
9. Influence and Consensus in Social Network
   codes investigate the relationship between PageRank and DeGrook consensus
10. Voting: Manners and Challenges
    codes calculate the voting results according to Condorcet model

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