Combinatorial Statistics and Quantitative Social Choice

组合统计和定量社会选择

• 批准号: 1106999
• 负责人: Elchanan Mossel
• 金额: $ 32.98万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1106999&HistoricalAwards=false
• 关键词: Combinatorial; Statistics; Quantitative; Social; Choice

The investigator studies combinatorial, probabilistic and analytic concepts, theorems and algorithms for analyzing stochastic models coming from social choice theory, from the theory of biological networks and from theoretical computer science.The proposal aims to provide rigorous models answering questions from molecular biology, from the theory of voting and from theoretical computer science. Some of the biologically motivated problems we study include: Which biological networks can be reconstructed from genetic data? How can they be efficiently reconstructed? What genealogical relations between individuals can be recovered from their genomes in an efficient manner? In the theory of voting the questions we address focus on minimizing the ability to manipulate voting and ranking methods and to increase their robustness against voting errors.

该研究员研究组合，概率和分析的概念，定理和算法，用于分析来自社会选择理论，生物网络理论和理论计算机科学的随机模型。该提案旨在提供严格的模型，回答来自分子生物学，投票理论和理论计算机科学的问题。我们研究的一些生物学动机的问题包括：哪些生物网络可以从遗传数据重建？如何有效重建？个体之间的什么样的系谱关系可以有效地从他们的基因组中恢复出来？ 在投票理论中，我们解决的问题集中在最大限度地减少操纵投票和排名方法的能力，并增加其对投票错误的鲁棒性。