Computational Preference Assessment and Optimization for Group Decision Support

信息技术进步带来的大量数据和各种选择，深刻地改变了个人、专业人员和决策者所面临的决定的性质。 智能决策支持系统（DSS）对于帮助管理这种复杂性至关重要。不幸的是，DSS需要（个人或组织）偏好的大量知识，导致“偏好瓶颈”：我们如何以自然，不引人注目的方式引出偏好？ 在群体决策（或社会选择）问题中，偏好瓶颈加剧了，因为DSS必须聚集单个群体成员的偏好。这不仅增加了所需的偏好信息量，而且还鼓励个人歪曲其偏好，从而损害社会目标。

The abundance of data and range of options afforded by advances in information technology have profoundly changed the nature of the decisions that face individuals, professionals and policy makers. Intelligent decision support systems (DSSs) are vital to help manage this complexity. Unfortunately, DSSs require considerable knowledge of (individual or organizational) preferences, leading to the "preference bottleneck": how do we elicit preferences in a natural, unobtrusive manner? In group decision making (or social choice) problems, the preference bottleneck is exacerbated, since a DSS must aggregate the preferences of individual group members. This not only increases the volume of preference information needed, but it provides incentives for individuals to misrepresent their preferences to the detriment of the social objective.