Network models and biproportional rounding for fair seat allocations in the UK elections

Abstract

Systems for allocating seats in an election offer a number of socially and mathematically interesting problems. We discuss how to model the allocation process as a network flow problem, and propose a wide choice of objective functions and allocation schemes. Biproportional rounding, which is an instance of the network flow problem, is used in some European countries with multi-seat constituencies. We discuss its application to single seat constituencies and the inevitable consequence that seats are allocated to candidates with little local support. However, we show that variants can be selected, such as regional apportionment, to mitigate this problem. In particular, we introduce a parameter based family of methods, which we call balanced majority voting, that can be tuned to meet the public's demand for local and global "fairness". Using data from the 2010 to 2015 UK general elections, we study a variety of network models and implementations of biproportional rounding, and address conditions of existence and uniqueness.

Tensor Balancing on Statistical Manifold

“…Matrix balancing is the problem of rescaling a given square nonnegative matrix A ∈ R n×n ≥0 to a doubly stochastic matrix RAS, where every row and column sums to one, by multiplying two diagonal matrices R and S. This is a fundamental process for analyzing and comparing matrices in a wide range of applications, including input-output analysis in economics, called the RAS approach (Parikh, 1979;Miller and Blair, 2009;Lahr and de Mesnard, 2004), seat assignments in elections (Balinski, 2008;Akartunalı and Knight, 2016), Hi-C data analysis (Rao et al, 2014;Wu and Michor, 2016), the Sudoku puzzle (Moon et al, 2009), and the optimal transportation problem (Cuturi, 2013;Frogner et al, 2015;Solomon et al, 2015). An excellent review of this theory and its applications is given by Idel (2016).…”