Contributions to this post were made by Jenn Wortman Vaughan, a senior researcher at Microsoft Research and a member of the workshop’s organizing committee.  

The organizing committee for the Computing Community Consortium (CCC) sponsored Theoretical Foundations for Social Computing Workshop have released their workshop report.

Social computing encompasses the mechanisms through which people interact with computational systems. It has blossomed into a rich research area of its own, with contributions from diverse disciplines including computer science, economics, and other social sciences. Yet a broad mathematical foundation for social computing is yet to be established, with a plethora of under-explored opportunities for mathematical research to impact social computing.

This workshop, held in June 2015, brought together roughly 25 experts in related fields to discuss the promise and challenges of establishing mathematical foundations for social computing.

The report recognizes some of the success stories in which mathematical research has led to innovations in social computing.

• Crowdsourced Democracy
  • Mathematical research has led to the design of new systems in which crowds of hundreds, or even millions, of individuals can collaborate to reach consensus on difficult societal issues.
• Automated Market Makers for Prediction Markets
  • Algorithmic research has been applied to the design of principled and efficient pricing mechanisms for prediction markets, financial markets specifically designed to elicit and aggregate information from traders.
• Fair Division for the Masses
  • Social computing systems can be used to help groups of people make decisions about their day-to-day lives such as how to fairly divide rent payments among roommates or assign credit in group projects.

The report also proposes an ambitious challenge problem called the Crowdsourcing Compiler, and recognizes several challenges that must be addressed in order for mathematical research to make great contributions to social computing.

• Blending Mathematical and Experimental Research
  • Mathematical and experimental research are complementary and both are needed to develop relevant foundations for social computing.
• Learning from the Social Sciences
  • In order for mathematical foundations to provide useful practical results, it is necessary to base it on models that better reflect human behavior.
• Generalization
  • Models will have the most potential for impact if they incorporate reusable components, allowing results to generalize to many systems.
• Transparency, Interpretability, and Ethical Implications
  • As users of social computing systems become increasingly sophisticated and aware of the impact of algorithms on their day-to-day lives, it is important to make social computing algorithms and models transparent, interpretable, and fair.

Please read the full report for more information.