|
課程名稱 |
計算社會科學
Computational Social Science |
|
開課學期 |
110-2 |
|
授課對象 |
社會科學院 政治學系 |
|
授課教師 |
李宣緯 |
|
課號 |
PS5697 |
|
課程識別碼 |
322 U2330 |
|
班次 |
|
|
學分 |
2.0 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期四8,9(15:30~17:20) |
|
上課地點 |
社科102 |
|
備註 |
政治思想，國際關係，公共行政，本國政治，比較政治。
限學士班三年級以上
總人數上限：40人
外系人數限制：10人 |
|
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1102PS5697_ |
|
課程簡介影片 |
|
|
核心能力關聯 |
本課程尚未建立核心能力關連 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
The ever-increasing use of the Internet and different ways of communication has led to a rapid growing flood of data. Computational social science is an emerging field which applies computational methods to analyze large, complex datasets related to human social behavior, and to arrive at theoretically and empirically grounded explanations of social processes and outcomes such as change of demographic, gender and income inequality, ethnic segregation, cultural and political change, diffusion on social networks and many others.
On the other hand, social scientists used increasingly available computing technology to perform macro-simulations of control and feedback processes in organizations, industries, cities, and global populations. Social complexity concepts such as complex systems, non-linear interconnection among macro and micro process, and emergence introduced to the social science world in these decades. Researchers uses tools like computer simulations and mathematical modeling to analyze the structure of social systems. Often these tools could help predicting and explaining the outcome distributions as holistic functions of other systematic factors such as migration, opinion formation, traffic, disease propagation, and the spread of fake news. Different approaches, evaluations, and methodologies in or related to computational social science are explored in this course.
三、每週進度及教學內容簡述Course outline (Course Schedule of 18 weeks)
Week 1: Course overview
Week 2: Introduction of computational social science
Week 3: Computation and social science
Week 4: Automated information extraction
Week 5: Social networks
Week 6: Social complexity (I): Origin and measurements
Week 7: No class
Week 8: Social complexity (II): Laws
Week 9: Social complexity (III): Theories
Week 10: Simulations (I): methodology
Week 11: Simulations (II): variable-oriented models
Week 12: Simulations (III): object-oriented models
Week 13: Introduction to complex adaptive systems
Week 14: Computational modeling
Week 15: Models of complex adaptive systems
Week 16: Review and/or class presentations
Week 17: Class presentations
Week 18: Class presentations |
|
課程目標 |
This class will provide and introduction of key tools and techniques of computational social science, consistently framing these techniques in terms of intriguing research questions.
In this course, students are lead into multidisciplinary domains of research in the social sciences fields such as political science, sociology, economics, management science, and related disciplines with technical innovations in applied mathematics, statistics, and computer science and other data science fields. Students will also learn to apply advanced computational methods–including mathematical modeling, social network analysis, the theory of social complexity and agent-based simulation. We hope this course would be helpful for social scientists who want to do more data science and data scientists who want to do more social science.
The course provides an overview of models and techniques for computational social science. The course is meant for undergraduate and graduate students in College of Social Sciences with a good mastery of math/statistics who are interested both in the theoretical study of computational social science and in their application to political, social and economic phenomena. |
|
課程要求 |
Midterm project 50%
Final Presentation and report 50%
Grades in the C range represent performance that is below expectations;
Grades in the B range represent performance that meets expectations;
Grades in the A range represent work that is excellent.
1. An important component of this course is active engagement with the material in classes. Regular attendance is essential and expected.
2. Quizzes are closed book, closed notes. Students are expected to study after classes.
3. No makeup exams will be given.
4. No foods in class. |
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
Cioffi-Revilla, Claudio. ``Introduction to computational social science." London and Heidelberg: Springer (2014). |
|
參考書目 |
Salganik, M. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
Holland, J. H. (2012). Signals and boundaries: Building blocks for complex adaptive systems. Mit Press.
Page, S. E. (2018). The model thinker: what you need to know to make data work for you. Hachette UK.
For the suggestion reading, please see the course outline (online version of the syllabus). |
|
評量方式
(僅供參考) |
|
No. |
項目 |
百分比 |
說明 |
|
1. |
Midterm project |
50% |
|
2. |
Final Presentation and Report |
50% |
|
|
|
週次 |
日期 |
單元主題 |
|
第1週 |
2/17 |
Course Overview |
|
第2週 |
2/24 |
Introduction of computational social science |
|
第3週 |
3/03 |
Computation and social science |
|
第4週 |
3/10 |
Automated information extraction |
|
第5週 |
3/17 |
Social networks (I) |
|
第6週 |
3/24 |
Social networks (II) |
|
第7週 |
3/31 |
Preliminaries and computational modeling |
|
第8週 |
4/07 |
Social complexity (I): Origin and measurements |
|
第9週 |
4/14 |
Social complexity (II): Laws |
|
第10週 |
4/21 |
Social complexity (III): Theories |
|
第11週 |
4/28 |
Simulations (I): methodology |
|
第12週 |
5/05 |
Simulations (II): variable-oriented models |
|
第13週 |
5/12 |
Simulations (III): object-oriented models |
|
第14週 |
5/19 |
Final presentation (I) |
|
第15週 |
5/26 |
Final presentation (II) |
|
第16週 |
6/02 |
Final presentation (III) |
|