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。 这项研究计划将开发新的计算模型和算法，用于社会选择(群体)环境中的决策支持，重点是有效地评估和引出偏好，以帮助打破偏好瓶颈。具体目标包括：(A)不完全偏好的决策算法，以及有效的、最小限度的偏好诱导；(B)在现实信息假设下，对错误报告偏好的诱因进行理论和经验分析的新技术；以及(C)学习用户偏好统计模型的新算法，这些算法可以被我们的优化和启发方法所利用。这些技术将被应用于一系列社会选择问题，包括投票、匹配和分割问题。其中一个研究重点将涉及发现和利用社交网络中偏好相关性的方法，以改善启发式和具有部分偏好的群体决策。这一研究项目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，包括信息高效的投票方案、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.