Mathematical Foundations for Social Computing
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, and, Jennifer Wortman Vaughan

TL;DR
This paper discusses the need for a rigorous mathematical foundation for social computing, highlighting its interdisciplinary nature and the potential for mathematical research to advance understanding and development in this emerging field.
Contribution
It provides an overview of social computing, emphasizes the importance of establishing mathematical foundations, and summarizes expert discussions on future research directions.
Findings
Social computing involves human-machine joint computation.
A broad mathematical framework for social computing is lacking.
Expert consensus on the importance of mathematical foundations for progress.
Abstract
Social computing encompasses the mechanisms through which people interact with computational systems: crowdsourcing systems, ranking and recommendation systems, online prediction markets, citizen science projects, and collaboratively edited wikis, to name a few. These systems share the common feature that humans are active participants, making choices that determine the input to, and therefore the output of, the system. The output of these systems can be viewed as a joint computation between machine and human, and can be richer than what either could produce alone. The term social computing is often used as a synonym for several related areas, such as "human computation" and subsets of "collective intelligence"; we use it in its broadest sense to encompass all of these things. Social computing is blossoming into a rich research area of its own, with contributions from diverse…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Complex Network Analysis Techniques · Privacy-Preserving Technologies in Data
