Binary Aggregation with Integrity Constraints
Institute for Logic, Language and Computation, University of Amsterdam
DEI - Seminar Room
June 27th, 2012
Collective decision making problems arise when a set of individual agents need to make a choice over a set of common alternatives. A central problem in the study of these situations is that of aggregating individual expressions, such as preferences, judgments and beliefs, into a collective view, to obtain a summary of the individual views provided. A classical example is that of aggregating the result of several search engines into a common ranking.
In this talk I will focus on situations in which a set of agents each need to make a yes/no choice regarding a number of issues, and these choices then need to be aggregated into a collective choice. Inspired by potential applications in Artificial Intelligence, I will put forward a systematic and flexible framework that aims to account for the wide variety of situations that can be encountered when dealing with the problem of collective choice.
In the first part of the talk I will give an overview of recent developments in Computational Social Choice, a research agenda that stems from the growing collaboration between researchers in Artificial Intelligence and Social Choice Theory. In the second part of the talk I will present the framework of binary aggregation with integrity constraints, and show how classical frameworks such as preference and judgment aggregation can be viewed as instances of our general setting.
Umberto Grandi is a PhD student at the Institute for Logic, Language and Computation at the University of Amsterdam, working with Ulle Endriss in the Computational Social Choice group.
Artificial intelligence, robotics and computer vision