Combinatorial Statistics and Quantitative Social Choice

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

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.