1、课程名称：Understanding Complexity 《对复杂系统的认识》
课时：36学时 招生人数：70人 工作语言：英语
主讲教师：Scott E. Page教授和Andrea Jones-Rooy教授
Scott E Page serves as Leonid Hurwicz Collegiate Professor of Complex Systems, Political Science, and Economics at the University of Michigan and as an external faculty member of the Santa Fe Institute. His research focuses on complex systems and diversity in social systems. He is the author of three books and more than seventy-five research papers in economics, political science, sociology, psychology, philosophy, physics, public health, geography, computer science, and management. He has filmed two video series for The Great Courses, and his online course Model Thinking has attracted over three quarters of a million participants. A frequent public speaker to corporations and government agencies including NASA, Bloomberg, Google, Boeing, the IMF, Genentech, Gilead, the United States Federal Reserve, and Pimco, Scott has also been a featured speaker at The World Economic Forum – Davos and The Aspen Ideas Festival. In addition to his teaching, Scott has consulted with Yahoo! Ford, DARPA, Procter and Gamble, and AB InBev. He has been the recipient of a Guggenheim Fellowship as well as fellowship at the Center for Advanced Studies in the Behavioral Sciences at Stanford. In 2011, he was elected a fellow of the American Academy of Arts and Sciences.
Andrea Jones-Rooy, Ph.D., is a social scientist specializing in complexity. She is the author of one book and several research articles (along with Scott E. Page) on complex systems, and she also contributes articles to media outlets on international relations, foreign affairs, and uncertainty. She is a research consultant for Fortune 500 firms, helping them integrate social scientific principles into understanding, researching, and developing policies for institutional and cultural change on some of their toughest issues, including diversity, team dynamics, and adaptability. Andrea is developing an online course on Complexity with Lemma, a mathematics education startup, and formerly designed and taught NYU’s first global course – also on Complexity – which was taught across four continents simultaneously. She was previously a professor of political science and global China studies at NYU Shanghai, where she founded and directed China’s first undergraduate major in integrated social science. Andrea earned her Ph.D. in Political Science from the University of Michigan, Ann Arbor, and was a postdoctoral fellow in Social and Decision Sciences at Carnegie Mellon University. In addition to being a sought-after researcher and celebrated teacher, Andrea is a standup comedian and circus artist, both of which she has performed for audiences (including royalty) all over the world.
In this course, we study foundational papers and topics in complex systems. The course assumes a general knowledge of complex adaptive systems. We focus on models, tools, concepts, and ideas from complexity theory including formal measures, non linear dynamics power laws, percolation, rugged landscapes, simulated annealing, genetic algorithms, self-organized criticality, networks, learning, and collective wisdom. The course requires calculus, some basic familiarity with di_erence and di_erential equations, but does not require any experience computer programming.
报告人：Scott E. Page教授；主持人：谢宇教授；时间：暂定2017年7月12日下午
主题：Many Model Thinking in a Complex World
介绍：To predict, explain, analyze, understand, and explore the complex world, social scientists apply models. Models, by definition, simplify. Therefore, no single model can cope with the complexity of the world. In his lecture, Professor Page describes an emerging new approach to scientific understanding based on many model thinking. This approach applies multiple, diverse models and leads to more accurate predictions, deeper understandings, better policy choices, and more practical designs.
Professor Page's MOOC Model Thinking has attracted over a half a million students who've learned how to use models to explain everything from why we cannot predict stock prices to why elephants don't explode.
2、课程名称：Advanced Quantitative Methodology 《高级量化分析方法》
课时：25学时 招生人数：80人 工作语言：英语
主讲教师：Gary King教授和Christopher Lucas博士
Gary King is the Albert J. Weatherhead III University Professor at Harvard University -- one of 24 with Harvard's most distinguished faculty title -- and Director of the Institute for Quantitative Social Science. King develops and applies empirical methods in many areas of social science research, focusing on innovations that span the range from statistical theory to practical application.
King is an elected Fellow in 8 honorary societies (National Academy of Sciences, National Academy of Social Insurance, American Statistical Association, American Association for the Advancement of Science, American Academy of Arts and Sciences, Society for Political Methodology, American Academy of Political and Social Science, and the Guggenheim Foundation) and has won more than 40 "best of" awards for his work. King was elected President of the Society for Political Methodology (1997-1999) and Vice President of the American Political Science Association (2003-2004). He has been a member of the Senior Editorial Board at Science (2015-2016), Visiting Fellow at Oxford (1994), and Senior Science Adviser to the World Health Organization (1998-2003). His more than 150 journal articles, 20 open source software packages, and 8 books span most aspects of political methodology, many fields of political science, and several other scholarly disciplines.
Christopher Lucas is a graduate student in the Government Department at Harvard University and an affiliate of the Institute for Quantitative Social Science. He studies political methodology and the political economy of information and media. His current projects include the development of models and tools to analyze text, audio, image, and video data. He applies these developments to the study of police-involved shootings in the United States, electoral accountability in Mexico, and American elections.
His research has been published in Political Analysis, Comparative Political Studies, twice in the American Journal of Political Science and received an award from the Society of Political Methodology. He has also published four open-source software packages. Ongoing projects are generously supported by J-PAL, the Weiss Family Program Fund, and various internal funding sources at Harvard.
He is a co-founder of the Harvard Experiments Working Group, a forum providing graduate students with funding for and feedback on experimental research. He also co-organized the inaugural Harvard Experimental Political Science Graduate Student Conference.
Although social scientists now have access to more data than ever before, making inferences about cause and effect remains difficult. In this course, students will learn how to leverage massive amounts of data via methods from statistics, computer science, and political methodology to improve causal inferences. We start by introducing the fundamentals of causal inference, and then introduce students to model-based inference and new developments in matching that scale to large data. We then show how machine learning methods can augment these approaches, particularly in cases where the functional form is unknown and where there exists large heterogeneity in treatment effects.
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