CS-E577004 - Special Course in Complex Systems: Computational Social Science, Lectures, 20.4.2022-25.5.2022

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  Lectures on Wednesdays at 14.15 in Zoom. See the link in the Lectures menu.

  Exercises available at A+: https://plus.cs.aalto.fi/cs-e5770/2022spring/

  Zulip chat for questions and the Monday exercise help session (at 12-14): https://cs-e577004.zulip.aalto.fi/

  Computational techniques have revolutionised social sciences leading to the fast-growing field of computational social science. While until recently it was only possible to use small-scale questionnaires studies and aggregated statistics to probe peoples lives, we can now have access to detailed logs of behavior of millions of people via the digital traces they leave in social media, mobile phones and other electronic means. Similarly, instead of theoretising how the different micro-scale behavioral patterns we observe in the data affects the macro-scale society using thought experiments and simplified models, we can now perform massive computer simulations to search for such emergent phenomena. In recent years, such techniques have been both used to test long-standing theories in social science but also to come up with completely new kinds of understanding of societies and individual behavior patterns. These analyses require a combination of techniques and ideas from computer science, applied mathematics and social sciences.

  
  

  In this special course we will explore basic techniques and ideas of computational social science with the emphasis on computation. You will build simulations of artificial societies, and see how various societal phenomena, such as segregation, inequality and polarisation, can emerge from individual behavior patterns that might seem relatively insignificant at first sight. We will also explore the pros and cons of using large-scale behavioral data.

  This special course consists of 4 weeks of lectures and exercises (multiple choice, Python programming). The course includes a project which will be introduced in week 4. Grading will be 60% exercises and 40% project.

  Exercises will be made available in A+

  
  

  Schedule

  Wed 20.4. Lecture 1

  
  

  Mon 25.4.  Exercise session 1

  Wed 27.4. Lecture 2

  Fri 29.4. Ex 1 deadline

  
  

  Mon 2.5. Exercise session 2

  Wed 4.5 Lecture 3

  Fri 6.5. Ex 2 deadline

  
  

  Mon 9.5. Exercise session 3

  Wed 11.5 Project intro

  Fri 13.5. Ex 3 deadline

  
  

  Mon 16.5. Project help

  Wed 18.5 Lecture 4

  
  

  Mon 23.5. Exercise session 4

  Fri 27.5. Ex 4 deadline

  
  

  Mon 30.5. Project help

  Tue 31.5. Project submission deadline

  Fri 3.6. Project feedback deadline

  
  

  
  
  
  

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