LEARNING OUTCOMES
After this course the student is able to...
... design (and run) agent-based simulations to test hypotheses on how various microscopic social mechanisms lead to emergent macroscopic phenomena.
... value the importance of thinking collective behavior and societies as complex systems with possibly emergent behaviour and non-linear phenomena.
... recall the pros and cons of large-scale social data from digital traces, and to analyse such data.
... appreciate social networks as a tool for analysing social systems both empirically and theoretically and produce basic social network analysis results.
... list ways of experimentation in CSS and explain why they are needed over observational data. In addition, they can construct simple large-scale CSS experiments given a research question.
... identify various challenges related to ethics, privacy and reproducibility is CSS and assess privacy risks related to social data, and appreciate the importance of these aspects of CSS.
Credits: 5
Schedule: 27.02.2023 - 29.05.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Mikko Kivelä, Juhi Kulshrestha
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
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. In addition to scientific research, social media platforms, game companies, online retailers and many other types of companies are using these techniques more and more to gain insight on their users behavior and competitive advantage. These analyses require a combination of techniques and ideas from computer science, applied mathematics and social sciences.
In this course you will learn basic techniques and ideas of computational social science with the emphasis on computation. You will learn how to analyse data on detailed behavior of millions of people and draw conclusions on the system level behavior that emerges from it. 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. Finally, as the ideas and techniques you learn are extremely powerful, we will discuss issues related to ethics and privacy in relation to computational social science research and practices.
Topics covered in this course include 1. agent-based simulations & artificial societes, 2. digital footprints and social data, 3. structure and dynamics of social networks, 4. experiments, and 5. ethics & reproducibility & privacy.
Assessment Methods and Criteria
valid for whole curriculum period:
Grades are given on the basis of weekly exercises and project work, there is no exam. Exercises and project work require Python programming, but you do not need to be familiar with Python beforehand, as long as you have some programming skills.
Workload
valid for whole curriculum period:
9 lectures + 8 sets of weekly exercises + project work. Attending lectures or exercises sessions is not mandatory, but returning completed weekly exercise sets is.
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
10 Reduced Inequality
16 Peace and Justice Strong Institutions
17 Partnerships for the Goals
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring IV - V
2023-2024 Spring IV - V