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Вернуться к Социальные и экономические системы: модели и анализ

Социальные и экономические системы: модели и анализ, Стэнфордский университет

4.8
Оценки: 407
Рецензии: 86

Об этом курсе

Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4...

Лучшие рецензии

автор: SW

Aug 09, 2016

Very good course on Social Networks, and also a hard one even for graduate level. Generally assignments are not too tough but fully understanding all the concepts take lots of extra readings.

автор: MG

Apr 17, 2018

Great survey course for social network analysis. Dr. Jackson's lectures motivated me to buy the book, and I hope to come back to this course later to work more on the optional parts.

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Рецензии: 82

автор: Llewellyn Preece

Apr 17, 2019

Great presentation of a variety of materials. There could have been some more details in terms of fully understanding some of the details, calculations, etc. You see this in the comments where folks struggle to be sure how the calculations are made. So that takes time and maybe the book as some of that. But all in all, just a great way to get introduced to some exciting work being done leveraging graphs.

автор: HEF

Apr 15, 2019

Challenging but worthwhile. So amazing that it took me to analyse things from a completely new perspective. I felt much more sophisticated in modeling things like economics, sociology, politics and epidemics, just to name a few. The course is well organized from simple basics in the first few weeks to the more advanced models in the later half. The quiz style is also very friendly to help me review the important concepts, and also try out softwares like Gephi and Pajek.

автор: Michael Samet

Jan 24, 2019

I loved everything so far, the quiz questions are well selected, but, I believe there are some notions which should be explained further mathematically.

автор: kazuyuki higashi

Dec 27, 2018

This lecture is a Great Introduction to Economic Networks.

Good point 1, many applications to economics research.

Good point 2, nice intuitive explanation to the notion of networks.

Note that MIT open course about Network can be complementary to this lecture.

автор: Sebastián Foti

Dec 22, 2018

Very nice and useful course.

автор: Justin Keswick

Dec 10, 2018

Excellent course. The labs are the best. Pajek and Gephi will be handy for network graphing and analyzing data. Thank you Professor Matthew Jackson. Your work is very good for reference.

автор: Rijul Kumar

Dec 03, 2018

greaaaat course

автор: Taras Zhylenko

Dec 02, 2018

Good introduction to the topic! Assignments were helpful as well as optional lecture videos.

автор: Noah J Wescombe

Nov 17, 2018

A very comprehensive course, taught in a very engaging manner by a top-caliber researcher and professor. An improvement would be adding a separate problem set for each lecture topic, to more thoroughly test specific understanding immediately after the teaching. Also, some of the Gephi instructions were not quite clear enough.

Getting Prof. Jackson's book as a companion to this course is very useful.

автор: Yadnesh Sawant

Nov 04, 2018

Provides an in depth knowlledge about topics.