GRA 2271 Understanding Organizations and Leadership Through Advances in Computational Social Science
GRA 2271 Understanding Organizations and Leadership Through Advances in Computational Social Science
The world produced 120 zettabytes of data in 2023. On WhatsApp, around 100 billion messages are sent daily. It would take 100s of millions of years to download all the data available on the internet. This digital revolution provides social scientists and organizations with an enormous opportunity. The availability of large-scale human data, combined with computational advances, such as social networks, large language models, and artificial intelligence can greatly inform our understanding of social behavior and organizational systems.
Welcome to the young field of Computational Social Science (CSS). CSS harnesses large datasets and novel data sources, computational advances, and social scientific theories to reveal novel insights into individual and group behavior. With rapid advancements in data availability and computation, we are no longer beholden to small samples, basic study designs, and simple analyses. New methods and techniques allow us to study and understand topics like organizations, leadership, and psychological phenomena from a brand-new light.
You will learn about the tools researchers and organizations can use to access and collect large-scale data, such as APIs, data scrapping, and human computation, as well as the recent computational methods to understand this data, such as social networks, natural language processing, and machine learning. A series of guided in-class tutorials will also teach you the basics underlying these skills and tools so that you can apply these methods outside of the course. Throughout the course, we will consider the ethical concerns accompanying the digital revolution, as well as appreciate the existing limits of human and machine computation.
Taken together, the course will set you apart from other graduates by placing you at the forefront of the digital revolution, giving you the cutting-edge skills needed to understand human behavior, transform organizations, and solve institutional problems.
The course is open to students at any level—it is not a data science or computer science course.
I look forward to diving into the exciting world of CSS with you!
Prerequisite: None. Coding or data analysis experience is not required. Though you will learn how to use existing tools, the course is not a data science or computer science course.
- Overview of how organizational topics and human psychology can be informed by CSS.
- Basic knowledge of the advancements that have been made in social and organizational sciences by applying computational techniques of CSS.
- Creative methodological ways to solve organizational problems.
- Skills entailing ways to examine organizational and leadership questions via new methods and approaches.
- Skills to interpret findings in the field of CSS and how these findings inform psychological topics.
- Skills to solve organizational problems with modern computational methods.
- Appreciation for and understanding of how organizations and other topics, such as human resources, can be informed by the relatively young field of CSS.
- Critical reflection on the benefits as well as limitations of varying methods in terms of informing organizational and psychological research.
The course covers the following topics
- The structure and dynamics of social systems.
- Social cognitive processes, including phenomena like social contagion and wisdom of crowds.
- Understanding organizations and groups in terms of modern computational methods, including social networks, natural language processing, machine learning, and artificial intelligence.
- Novel sources of data (e.g., geo-location, sensors) in organizations.
- The ethics of data privacy in organizations and beyond.
The course is structured as a combination of lectures, discussions, in-class tutorials/activities, and asynchronous/synchronous group work. The course will be held either in person or on Zoom (digital lectures) with active participation. The course will rely on research and popular articles, as well as guided method walkthroughs. The course assessment is a straightforward final exam—multiple choice and short answer questions.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
This is a course with a structured exam assessment (a single final exam). Students who fail to participate in the exam will fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
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| Assessments |
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Exam category: School Exam Form of assessment: Structured Test Exam/hand-in semester: First Semester Weight: 100 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: . Exam code: GRA 22711 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 24 Hour(s) | |
Prepare for teaching | 60 Hour(s) | |
Student's own work with learning resources | 60 Hour(s) | Work with asynchronous materials |
Examination | 16 Hour(s) | Incl. review and preparation |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.