Computational social choice: towards realistic models of group decision-making
Computational social choice is a rapidly growing research area that studies algorithms for preference aggegation and collective
decision making. Early papers in this field typically assumed that all agents have complete information about each others'
preferences, and are either honest, i.e., can be assumed to report their true rankings of the candidates, or manipulative, i.e., aim to get a particular candidate elected. However, in real life, these assumptions do not always hold. In this talk, we will describe some recent work on developing richer and more nuanced models of group decision-making, which take into account uncertainty about the voters' behavior and/or game-theoretic considerations, and discuss algorithms and complexity results for these models.