The Inverse Shapley Value Problem

For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley and Martin Shubik in 1954 \cite{SS54} and are widely studied in social choice theory as a measure of the "influence" of voters. The \emph{Inverse Shapley Value Problem} is the problem of designing a weighted voting scheme which (approximately) achieves a desired input vector of values for the Shapley-Shubik indices. Despite much interest in this problem no provably correct and efficient algorithm was known prior to our work. We give the first efficient algorithm with provable performance guarantees for the Inverse Shapley Value Problem. For any constant \eps > 0 our algorithm runs in fixed poly$(n)$ time (the degree of the polynomial is independent of \eps) and has the following performance guarantee: given as input a vector of desired Shapley values, if any "reasonable" weighted voting scheme (roughly, one in which the threshold is not too skewed) approximately matches the desired vector of values to within some small error, then our algorithm explicitly outputs a weighted voting scheme that achieves this vector of Shapley values to within error \eps. If there is a "reasonable" voting scheme in which all voting weights are integers at most \poly(n) that approximately achieves the desired Shapley values, then our algorithm runs in time \poly(n) and outputs a weighted voting scheme that achieves the target vector of Shapley values to within error $\eps=n^{-1/8}.

Supersymmetry and the LHC Inverse Problem

Given experimental evidence at the LHC for physics beyond the standard model, how can we determine the nature of the underlying theory? We initiate an approach to studying the "inverse map" from the space of LHC signatures to the parameter space of theoretical models within the context of low-energy supersymmetry, using 1808 LHC observables including essentially all those suggested in the literature and a 15 dimensional parametrization of the supersymmetric standard model. We show that the inverse map of a point in signature space consists of a number of isolated islands in parameter space, indicating the existence of "degeneracies"--qualitatively different models with the same LHC signatures. The degeneracies have simple physical characterizations, largely reflecting discrete ambiguities in electroweak-ino spectrum, accompanied by small adjustments for the remaining soft parameters. The number of degeneracies falls in the range 1<d<100, depending on whether or not sleptons are copiously produced in cascade decays. This number is large enough to represent a clear challenge but small enough to encourage looking for new observables that can further break the degeneracies and determine at the LHC most of the SUSY physics we care about. Degeneracies occur because signatures are not independent, and our approach allows testing of any new signature for its independence. Our methods can also be applied to any other theory of physics beyond the standard model, allowing one to study how model footprints differ in signature space and to test ways of distinguishing qualitatively different possibilities for new physics at the LHC.Comment: 55 pages, 30 figure

On the inverse power index problem

Weighted voting games are frequently used in decision making. Each voter has a weight and a proposal is accepted if the weight sum of the supporting voters exceeds a quota. One line of research is the efficient computation of so-called power indices measuring the influence of a voter. We treat the inverse problem: Given an influence vector and a power index, determine a weighted voting game such that the distribution of influence among the voters is as close as possible to the given target value. We present exact algorithms and computational results for the Shapley-Shubik and the (normalized) Banzhaf power index.Comment: 17 pages, 2 figures, 12 table