DBCOMSOC - M1 Internship

Summary (from dbcomsoc.org)

Social choice underlies the equitable and efficient operation of a society. How does one aggregate preferences of individuals, arriving at a society-wide consensus? This question has been the subject of intense debate throughout history, dating as far back as ancient Greece, and, in the past two decades, has led to the development of computational social choice - an interdisciplinary area of research and practice that combines insights from mathematics, logic, economics, and computer science. One of the main foci of computational social choice are the algorithmic aspects of determining actual or potential winners in a poll or in an election. Moreover, dealing with incompleteness and uncertainty (an inherent characteristic of polling) is an important challenge confronted by computational social choice. In recent years, the data management community embarked on an investigation of preference databases, which extend traditional databases by treating preferences on a par with relational data. The main aim of this project is to develop a unifying framework that brings together preferences, rules, outcomes, contextual information, and database query languages.

My work on this project:

check_circleImplement and optimize Necessary Winner algorithm for several scoring rules (Xia and Conitzer, 2010) with Kunal Relia.
check_circleImplement an approximate algorithm for Possible Winner algorithm for positional scoring rule.
check_circleBuild a data generation method for partial orders and test it with real data.
check_circleImplement an algorithm for possible and necessary queries.
Check the github repistory here

Be a part of it!

If you're on a computer and have 5 minutes, you can answer our experiment by clicking here. It will really help us to gather data in order to test our algorithms.