Thursday 17 December 2015 h. 14:30, Room 2BC30
Andrea Loreggia (Padova, Dip. Mat.)
"Computational social choice: between AI and Economics"
During the last decades, the trend has been for disciplines to converge on common techniques to be used in similar problems, besides focusing on specific techniques to be used in narrow domains. AI is one of the best examples: the cross-fertilisation process leads to a very fascinating solutions. Consider for example genetic algorithms, which mimic evolutionary mechanisms to solve search and optimization problems. The individualistic approach of problem solving becomes insufficient: concepts, techniques and experts need to collaborate to get a better understanding of the problems they would like to solve. The techniques that AI makes available are being used by many other disciplines. AI nowadays inundates our everyday life with tools and methods that are hidden in our household electrical devices, smartphones and much more. Starting from the field of multi-agent systems, researchers in AI recently considered the use of models and problems from economics. Notable examples are voting systems used to aggregate the results of several search engines, game theoretic methods that analyse the complex interaction of autonomous agents, and matching procedures implemented on large-scale problems such as the coordination of kidneys transplants and the assignment of students to schools. In this scenario, a number of research lines federated under the name of computational social choice. The need for a computational study of collective decision procedures is clear. On the one hand, from crowdsourcing to university admission ranking, many real-life applications apply existing social choice methods to large scale problems. On the other hand, collective decision-making is not a prerogative of human societies, and multi-agent systems can use these methods to coordinate their actions when facing complex situations. In this talk, we would like to focus on two examples that highlight the impact of a computational approach to classical problems of collective choice. First, by studying repeated decisions (think of opinion polls that precede an election) to evaluate the quality of the result, and, second, by devising innovative procedures to predict the preferences of a collection of individuals.