An Effective Personnel Selection Model
For a personnel selection problem we define a new mathematical approach and make a computer tool that finds effective stable matching between the set of employers and candidates. A characterization, main components and application with its advantages are given.
In this article, we consider the impact of personal contacts on the labor market outcome. Unlike previous studies, we do not assume any particular network structure or vacancies communication protocol. Instead, we state three general properties of matching functions that allow us to establish the existence and uniqueness of equilibrium and characterize the impact of social ties on the labor market. In particular, we show that a monotonically increasing matching function in socialization level is a necessary and sufficient condition for having monotonically decreasing unemployment and increasing wage and market tightness. However, the same does not apply to vacancy rate. We establish a condition under which a monotonically increasing matching function produces a vacancy rate that first increases in socialization level, but then decreases.
The main purpose of the workshop-to allow people studying the discipline "Labor law", to apply theoretical knowledge in the performance of practical tasks based on specific situations, the preparation of control tasks of an analytical nature and conducting business games. For students on educational programs of academic undergraduate, graduate students and teachers of law schools and faculties. It can be used by students of additional educational programs, employees of state and municipal bodies, personnel services and legal services of various organizations, employers - individuals, as well as anyone interested in labor law.
The problem of axiomatic and algorithmic constructions of the threshold decision making is studied in the case when individual opinions are given as m-graded strict preferences (with m ≥ 3). It is shown that the only rule satisfying the introduced axioms is the threshold rule. Two explicit algorithms are presented: the ordering algorithm, under which the vector-grades of alternatives are successively written out, and an enumerating social decision function corresponding to the natural order of the weak order equivalence classes.
The research is devoted to the legal regulation of employment contract in Eastern Europe in the aspect of general and special features in this legal institute. The employment legislation in the countries of Eastern Europe is constantly developing, and its main aim is the unification of the legal regulation of the employment contract.
Classical change-point detection procedures assume a change-point model to be known and a change consisting in establishing a new observations regime, i.e. the change lasts infinitely long. These modeling assumptions contradicts applied problems statements. Therefore, even theoretically optimal statistics in practice very often fail when detecting transient changes online. In this work in order to overcome limitations of classical change-point detection procedures we consider approaches to constructing ensembles of change-point detectors, i.e. algorithms that use many detectors to reliably identify a change-point. We propose a learning paradigm and specific implementations of ensembles for change detection of short-term (transient) changes in observed time series. We demonstrate by means of numerical experiments that the performance of an ensemble is superior to that of the conventional change-point detection procedures.
As the object of empirical research conducted deep interviews, made 68 employers-owners and salaried managers, heads of enterprises of small and medium-sized business of Nizhny Novgorod. The research results obtained by using content analysis of transcripts of interviews, testify to the presence of a number of specific features characterizing the position of employer small and medium-sized businesses in the modern system of labour relations. Unfortunately, it should be stated that this position today is extremely vulnerable.