3 Credit Hours

MS

Munindar Singh

Professor

919-515-5677 [email protected]

This course surveys the field of social computing and decentralized artificial intelligence, introducing its key concepts, paradigms, and techniques. Specific topics are selected from the following list: social media and social network analytics, sociological underpinnings, mobility and social context, crowdsourcing and human computation, social mobilization, human decision making, voting theory, judgment aggregation, prediction markets, economic mechanisms, incentives, organizational modeling, argumentation, contracts, norms, sociotechnical systems, and software engineering with and for social computing. This course incorporates ideas from diverse disciplines [including sociology, psychology, law, economics, political science, logic, statistics, philosophy, business] to provide essential background for future computer science careers in industry and research.

Prerequisite

The course is self-contained. The main informal prerequisite is maturity in thinking about subtle concepts, such as might be gained through experience with conceptual modeling in databases or software.

From long experience, I have discovered that the material in CSC 226 is essential for my courses. Here is a (partial) list of topics that will be assumed: elementary set theory, relations, partial orders, functions, concept of a theorem, propositional logic, and predicate logic.

I recommend you brush up on these topics if you aren’t comfortable with them. These topics are covered in CSC 226: Applied Discrete Mathematics. You may review Chapters 1 to 6 from the following book, which is sometimes used as the CSC 226 textbook: Kenneth H. Rosen, Discrete Mathematics and its Applications, McGraw-Hill, 7th edition, 2012. ISBN 0-07-289905-0.

Learning Outcomes

Upon completion of this course, students will be able to do the following.

• Understand the range of social computing and decentralized artificial intelligence applications and concepts.
• Understand and apply concepts of computational models underlying social computing and decentralized artificial intelligence
• Carry out simple forms of social analytics, involving network and language models, applying existing analytic tools on social information.
• Design and launch decentralized artificial intelligence applications.
• Understand the broad aspects of, and implement, richer social computing models and decentralized artificial intelligence .
• Evaluate emerging social computing and decentralized artificial intelligence applications, concepts, and techniques in terms of key principles.

Course Requirements

Textbook

None.

The following materials will be used in this course.

All necessary software will be available for use on NCSU laboratory computers or for free download for academic purposes.

Required: Transparencies and notes. Available on the course website. I will announce the availability of the final versions throughout the semester.

Required: Readings. Available openly, through the university library, or from the course website.

Updated: 10/05/2026.