Computational Preference Assessment and Optimization for Group Decision Support

信息技术的进步提供了丰富的数据和广泛的选择，深刻地改变了个人、专业人士和政策制定者面临的决策的性质。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需要对(个人或组织)偏好有相当多的了解，这导致了“偏好瓶颈”：我们如何以自然、不显眼的方式获得偏好？在群体决策(或社会选择)问题中，偏好瓶颈加剧，因为决策支持系统必须聚合个体群体成员的偏好。这不仅增加了所需的偏好信息量，而且还激励个人歪曲自己的偏好，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.