Computational Preference Assessment and Optimization for Group Decision Support

群体决策支持的计算偏好评估和优化

• 批准号: 121843-2013
• 负责人: Boutilier, Craig
• 金额: $ 4.81万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=593334
• 关键词: Computational; Preference; Assessment; Optimization; Group

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.

信息技术的进步所提供的丰富的数据和选择范围深刻地改变了个人、专业人士和政策制定者所面临的决策的性质。智能决策支持系统（DSSs）对于帮助管理这种复杂性至关重要。不幸的是，DSSs需要相当多的（个人或组织）偏好知识，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，偏好瓶颈被加剧，因为决策支持系统必须汇总个体群体成员的偏好。这不仅增加了所需偏好信息的数量，而且还为个人提供了歪曲其偏好的动机，从而损害了社会目标。