ICES: Small: Collaborative Proposal: Robust Preference Aggregation

ICES：小型：协作提案：稳健的偏好聚合

• 批准号: 1216016
• 负责人: Michel Regenwetter
• 金额: $ 5.27万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216016&HistoricalAwards=false
• 关键词: ICES; Small; Collaborative; Proposal; Robust

An individual's preferences can be complicated. The project investigates whether people's preferences do, in fact, conform to the CP-net (conditional preference network) formalism, how such preferences can be combined according to different voting methods, and how vulnerable those methods are to manipulation or "strategic voting." Finally, the very large corpus of preference data from the Netflix Challenge is used to build statistical models of the efficiency of manipulation algorithms and to compare the performance of different consensus methods.Laboratory experiments will check whether people exhibit preferences that can be modeled by CP-nets. The project extends ideas from aggregation in Bayesian networks (models of conditional probabilities) to aggregation in CP-nets (models of conditional preferences).The team investigates how statistical inference from noisy data relates to or interacts with strategic manipulability and bribery. The project extends behavioral social choice and computational social choice on voting systems, and the manipulation thereof, from preferences expressed as ratings, rankings, or subsets to preferences expressed as CP-nets.The team investigates the performance of voting methods and the efficiency of manipulation schemes on real preference data from the Netflix challenge data set. These data, namely hundreds of thousands of (slightly perturbed) personal rankings of movies, were released several years ago for data-mining purposes. One can extract individual "elections" based on the rankings of a small set of movies, evaluate and compare various aggregation methods and empirically characterize voting scenarios that are especially susceptible or especially resilient to strategic manipulation.Among the broader impacts of the research, beyond the integration and cross-fertilization of several distinct research areas spanning several scientific disciplines, are the development of more adequate individual and collective decision making tools, that will help individuals, groups, organizations, and society to improve decision making.

个人的偏好可能很复杂。 该项目调查人们的偏好实际上是否符合 CP-net（条件偏好网络）形式，如何根据不同的投票方法组合这些偏好，以及这些方法如何容易受到操纵或“策略投票”的影响。 最后，来自 Netflix 挑战赛的大量偏好数据被用来构建操纵算法效率的统计模型，并比较不同共识方法的性能。实验室实验将检查人们是否表现出可以通过 CP 建模的偏好网。该项目将想法从贝叶斯网络（条件概率模型）中的聚合扩展到 CP 网络（条件偏好模型）中的聚合。该团队研究了噪声数据的统计推断如何与战略操纵性和贿赂相关或相互作用。该项目扩展了投票系统上的行为社会选择和计算社会选择及其操纵，从以评级、排名或子集表示的偏好到以 CP 网络表示的偏好。该团队研究了投票方法的性能和操纵效率基于 Netflix 挑战数据集的真实偏好数据的方案。 这些数据，即数十万个（稍微受到干扰的）电影个人排名，是几年前出于数据挖掘目的而发布的。 人们可以根据一小组电影的排名提取个人“选举”，评估和比较各种聚合方法，并凭经验描述特别容易或特别容易受到战略操纵影响的投票场景。该研究的更广泛影响超出了跨越多个科学学科的几个不同研究领域的整合和交叉发展，是开发更充分的个人和集体决策工具，这将帮助个人、团体、组织和社会改进决策。