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，而且还为个人提供了误导其偏好的动机，从而损害了社会目标。本研究计划将开发新的计算模型和算法，用于社会选择（群体）设置中的决策支持，重点是有效评估和偏好的诱导，以帮助打破偏好瓶颈。具体目标包括开发：（a）决策算法与不完整的偏好，有效的，最小的启发的偏好;（B）新技术的理论和实证分析的激励措施误报的偏好下现实的信息假设;和（c）新的算法学习统计模型的用户偏好，可以利用我们的优化和启发的方法。这些技术将被应用到一系列的社会选择问题，包括投票，匹配和分割问题。其中一个研究重点将涉及在社交网络中发现和利用偏好相关性的方法，以改善部分偏好的启发和群体决策。该研究计划的结果将对大多数形式的群体决策/社会选择产生广泛的影响，包括信息有效的投票方案，市场细分工具，匹配机制，政策设计和群体推荐系统。

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.This research program will develop new computational models and algorithms for decision support in social choice (group) settings, with a focus on the effective assessment and elicitation of preferences to help break the preference bottleneck. Specific objectives include development of: (a) algorithms for decision making with incomplete preferences, and effective, minimal elicitation of preferences; (b) new techniques for the theoretical and empirical analysis of the incentives for misreporting of preferences under realistic informational assumptions; and (c) new algorithms for learning statistical models of user preferences that can be exploited by our optimization and elicitation methods. These techniques will be applied to a range of social choice problems, including voting, matching, and segmentation problems. One research thrust will involve methods to discover and exploit preference correlations in social networks to improve elicitation and group decision making with partial preferences. The results of this research program will have broad consequences for most forms of group decision making/social choice, including informationally efficient voting schemes, market segmentation tools, matching mechanisms, policy design, and group recommender systems.