Computational Social Choice Meets Databases
This work addresses the need for more expressive querying in computational social choice, potentially benefiting researchers and practitioners in both fields, though it appears incremental as it builds on existing concepts.
The paper tackles the problem of integrating computational social choice with database management by developing a framework that supports sophisticated queries about voting rules and related entities, establishing rigorous semantics and investigating computational complexity, with results showing sharp contrasts in complexity for necessary answers compared to prior work on necessary winners.
We develop a novel framework that aims to create bridges between the computational social choice and the database management communities. This framework enriches the tasks currently supported in computational social choice with relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions. At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries. At the technical level, we embark on an investigation of the computational complexity of the necessary answers. We establish a number of results about the complexity of the necessary answers of conjunctive queries involving positional scoring rules that contrast sharply with earlier results about the complexity of the necessary winners.