Les 10 concurrents dialnet.unirioja.es les plus visités

redalyc.org

La producción científica de México en revistas iberoamericanas, Biología

scielo.br

RESUMO CONTEXTO: Diferenças de sexo e gênero são muitas vezes negligenciadas no desenho da...

elibrary.ru

рефераты и полные тексты более 39 млн научных публикаций и патентов. электронные версии научно-технических журналов в свободном доступе и по подписке

jstage.jst.go.jp

J-STAGEのメンテナンス予定に関する最新情報をお知らせしています。

revistas.ucm.es

RIBES es una revista científica que publica artículos sobre bibliotecas públicas, escolares y universitarias, relacionados con la educación y la sociedad

arxiv.org

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.

cyberleninka.ru

The banking system is a key sector of the economy and often faces various types of risks. This article focuses on credit risks, which are the main source of financial losses for credit institutions, including banks. Based on real data, a scoring model was developed in this study to assess and optimally manage credit risk, using the XGBoost machine learning algorithm. The model returns score values in the range of 300–850. Quality metrics such as ROC AUC, PR AUC, Precision, Recall, and F1 score were evaluated for the developed model. Within the scope of the research, a new approach was proposed to assess the sensitivity of a machine learning model to changes in variable values. The approach is based on the concept of entropy. The results show that the use of machine learning models makes it possible to assess and optimally manage credit risk, thereby reducing potential losses. Moreover, the proposed new approach, when applied together with the well-known SHAP method, allows for a deeper evaluation of the model and its sensitivity to changes in variable values.