Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges

Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context

Computational Social Choice: Prospects and Challenges

AbstractHow should we aggregate the individual views of the members of a group so as to arrive at an adequate representation of the collective view of that group? This is a fundamental question of deep philosophical, economic, and political significance that, around the middle of 20th century, has given rise to the field of Social Choice Theory. More recently, a research trend known as Computational Social Choice has emerged, which studies this question from the perspective of Computer Science. This “computational turn” is fuelled both by the fact that questions of social choice have turned out to be central to a range of application areas, notably in the domain of Information and Communication Technologies, and by the insight that many concepts and techniques originating in Computer Science can be used to solve (or provide a new angle on) problems in Social Choice Theory. In this paper, I give a brief introduction to Computational Social Choice and discuss some of the prospects and challenges for this fast growing area of research

Collective Schedules: Scheduling Meets Computational Social Choice

International audienceWhen scheduling public works or events in a shared facility one needs to accommodate preferences of a population. We formalize this problem by introducing the notion of a collective schedule. We show how to extend fundamental tools from social choice theory—positional scoring rules, the Kemeny rule and the Con-dorcet principle—to collective scheduling. We study the computational complexity of finding collective schedules. We also experimentally demonstrate that optimal collective schedules can be found for instances with realistic sizes