CSC 455 - Social Computing and Decentralized Artificial Intelligence

Catalog Description:
This course surveys the field of social computing, introducing its key concepts, paradigms, and techniques. Specific topics are selected from the following list: social media and social network analytics, sociological underpinnings, crowdsourcing and surveys, human computation, social mobilization, human decision making, voting theory, judgment aggregation, prediction markets, economic mechanisms, incentives, organizational modeling, argumentation, contracts, norms, mobility and social context, 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.
Contact Hours: Prerequisites: CSC226 and CSC316
Co-requisites: None
Restrictions: None
Coordinator: Dr. Munindar P. Singh
Textbook: None

Course Outcomes:
This course is about developing systems of humans and artificial intelligence agents. Specifically, it seeks to develop the understanding for building agents that effectively interact with each other and with humans. To this end, this course conceives of social computing as the interplay between computing on the one hand and social relationships among social entities on the other hand. Specifically, this course considers how (1) social relationships and individual preferences can be modeled, represented, and reasoned about by artificial intelligence agents and (2) how interactions among social entities can be incorporated into such agents. This course surveys the key paradigms exhibited by applications of social computing. It identifies concepts for modeling and realizing social computing applications.

  1. Apply concepts of computational models underlying social computing and decentralized artificial intelligence
  2. Carry out simple forms of social analytics, involving network and language models, applying existing analytic tools on social information
  3. Design and launch social computing and multiagent systems applications
  4. Implement rich social intelligence models in social computing applications
  5. Evaluate emerging social computing applications, concepts, and techniques in terms of key principles of decentralized artificial intelligence


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