Reconstructing weighted voting schemes from partial information about their power indices

TL;DR

This paper develops algorithms to reconstruct weighted voting schemes from partial information about voters' power indices, advancing understanding of inverse problems in voting theory and computational social choice.

Contribution

It introduces polynomial and quasi-polynomial algorithms for reconstructing linear threshold functions from partial power index data.

Findings

Polynomial-time algorithm for approximate Chow parameters reconstruction.

Quasi-polynomial algorithm for approximate Shapley indices reconstruction.

Addresses the partial information setting in inverse power index problems.

Abstract

A number of recent works [Goldberg 2006; O'Donnell and Servedio 2011; De, Diakonikolas, and Servedio 2017; De, Diakonikolas, Feldman, and Servedio 2014] have considered the problem of approximately reconstructing an unknown weighted voting scheme given information about various sorts of ``power indices'' that characterize the level of control that individual voters have over the final outcome. In the language of theoretical computer science, this is the problem of approximating an unknown linear threshold function (LTF) over $\{-1, 1\}^n$ given some numerical measure (such as the function's $n$ ``Chow parameters,'' a.k.a. its degree-1 Fourier coefficients, or the vector of its $n$ Shapley indices) of how much each of the $n$ individual input variables affects the outcome of the function. In this paper we consider the problem of reconstructing an LTF given only partial information about its Chow parameters or Shapley indices; i.e. we are given only the Chow parameters or the Shapley indices corresponding to a subset $S \subseteq [n]$ of the $n$ input variables. A natural goal in this partial information setting is to find an LTF whose Chow parameters or Shapley indices corresponding to indices in $S$ accurately match the given Chow parameters or Shapley indices of the unknown LTF. We refer to this as the Partial Inverse Power Index Problem. Our main results are a polynomial time algorithm for the ($\varepsilon$-approximate) Chow Parameters Partial Inverse Power Index Problem and a quasi-polynomial time algorithm for the ($\varepsilon$-approximate) Shapley Indices Partial Inverse Power Index Problem.