Combinatorial Statistics and Quantitative Social Choice

研究人员研究了来自社会选择理论、生物网络理论和理论计算机科学的组合、概率和分析概念、定理和算法，旨在为回答来自分子生物学、投票理论和理论计算机科学的问题提供严格的模型。我们研究的一些生物动机问题包括：哪些生物网络可以从基因数据中重建？如何才能有效地重建它们？个体之间的什么谱系关系可以有效地从他们的基因组中恢复？在投票理论中，我们讨论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，并增加它们对投票错误的稳健性。

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.