Search arXivSearch

arXiv · 2004.09417

Load-sharing dependence models and construction of voting situations for any arbitrary ranking schemes

Abstract

In this paper we present a study about minima among random variables, about the context of voting theory, and about paradoxes related with such topics. In the field of reliability theory, the term load-sharing model is commonly used to designate a special type of multivariate survival models. We demonstrate the effectiveness that such dependence models can have also in some other fields, such as those of interest here. Several important, and by now classic, papers have been devoted to single out and to prove general conclusions in the field of voting theory. We reformulate and achieve such conclusions by developing a method of proof, alternative to the existing ones, and completely probabilistic in nature. As main features of this method, we focus attention on ranking schemes associated to m-tuples of non-negative random variables and suitably single out a special subclass of load-sharing models. Then we show that all possible ranking schemes can be conveniently obtained by only considering such a special family of survival models. This result leads to some new insight about the construction of voting situations which give rise to all possible types of voting paradoxes. Our method and related implications will be also illustrated by means of some examples and informative remarks.

Explore related subjects

Keep this discovery

BibTeXRIS

Emilio De Santis, Fabio Spizzichino. 2020-04-20. Load-sharing dependence models and construction of voting situations for any arbitrary ranking schemes. https://arxiv.org/abs/2004.09417

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR