Preferences are ubiquitous in everyday decision making. Therefore, they are essential ingredients in every reasoning tool. Preferences are mainly studied in artificial intelligence in the context of multi-agent decision making, where each agent expresses its preferences over a set of possible decisions and the goal is to find the best collective decision. Preferences have been classically studied in a social choice context and in particular in voting theory, where several voters express their preferences over the candidates and a voting rule is used to elect the winning candidate. Since this scenario is similar to multi-agent decision making, many interesting papers in the multi-agent area have tried to adapt social choice results to multi-agent setting by taking into account issues that do not occur in a social choice context: a large set of candidates with a combinatorial structure, formalisms to model preferences compactly, preference orderings including indifference and incomparability, uncertainty, as well as computational concerns. This is the basis of a new research area called computational social choice, which studies how social choice and artificial intelligence can fruitfully cooperate to give innovative and improved solutions to aggregate preferences given by multiple agents. This talk will present this interdisciplinary area of research and some of my recent results regarding the issues mentioned above.