EXPLORE 2016 - 3rd Workshop on Exploring Beyond the Worst Case in Computational Social Choice (EXPLORE 2016)

Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science. Many, often disjoint, groups of researchers both outside and inside computer science study group decision making and preference aggregation. The computer science view of social choice focuses on computational aspects of classical social choice and importing ideas from further afield (i.e. classical social choice) into computer science, broadly. While the surge of research in this area has created dramatic benefits in the areas of matching markets, recommendation systems, and preference aggregation, much of the ComSoc community remains focused on worst case assumptions.
As ComSoc evolves in the coming years there will be an increased 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, advanced 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.
We encourage research related to:
Algorithms
Empirical Studies
Average case analysis
Identification of tractable sub-cases
Fixed parameter complexity analysis
Benchmarking and analysis from the preference handling and recommendation systems
Studies of matching and auction mechanisms in practice
Crowd-sourcing and other real-world data aggregation domains.