Computational Social Choice (ComSoc) lies at the intersection of computer science, economics, social choice, and political science. Many, often disjoint, groups of researchers both outside and within computer science study group decision making and preference aggregation. The computer science view of social choice focuses, broadly, on computational aspects of social choice and importing ideas from social choice into computer science. While the surge of research in this area has created dramatic benefits in the areas of market matchings, recommendation systems, and preference aggregation, much of the ComSoc community is  focused on worst case assumptions.

As ComSoc evolves there is a growing need to relax or revise some of the more common assumptions in the field: worst case complexity, complete information, and overly-restricted domains, among others. This means going beyond traditional algorithmic and complexity results and providing a more nuanced look, using real data, parameterized algorithms, and human and agent experimentation to provide a fresh and impactful view of group decision making. This goes hand in hand with highlighting the practical applications of much of the theoretical research — as much of the most impactful work in ComSoc does. It also involves looking at more complex preference aggregation settings that help model real world requirements.