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Seminario 20/25: Dolores Romero Morales (Copenhagen Business School, Denmark): Explaining Machine Learning Outcomes by Means of Mathematical Optimization

There is a growing literature on enhancing the interpretability of Machine Learning methods involved in Data Driven Decision Making. Interpretability is desirable for non-experts; it is required by regulators for models aiding, for instance, credit scoring; and since 2018 the European Union has extended this requirement by imposing the so-calledright-to-explanation in algorithmic decision making. Mathematical…

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V Jornadas Anuales de Doctorado y Seminarios Novel. 19-20 de noviembre

Los días 19 y 20 de noviembre de 2020 se celebrará la V edición de las Jornadas Anuales de Doctorado y Seminarios Novel del programa de doctorado DEcIDE, en modalidad íntegramente online. Las Jornadas anuales y los Seminarios novel constituyen dos actividades formativas obligatorias para los estudiantes del programa, en las que los/as directores/as de tesis están…

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Sesión de bienvenida curso 2020/21 – jueves, 19, 15:30 h.

El jueves, día 19 de noviembre, a las 15:30 h., tendrá lugar la sesión de bienvenida del Programa de Doctorado en Economía Interuniversitario DEcIDE. La sesión será online y podrá seguirse a través del Aula Virtual de la UMU – Sitio Actividades Formativas Doctorado DEcIDE (herramienta Videoconferencia), o bien directamente a través del enlace de Zoom que…

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Seminario 20/24: Francesco Ciardiello (Sheffield University: On Pure-Strategy Nash Equilibria in a Duopolistic Market Share Model

This paper develops a duopolistic discounted marketing model with linear advertising costs and advertised prices for mature markets still in expansion. Generic and predatory advertising effects are combined together in the model. We characterize a class of advertising models with some lowered production costs. For such a class of models, advertising investments have a no-free-riding…

Ecuaciones diferenciales

Seminario 20/23: Laureano F. Escudero (Universidad Rey Juan Carlos), On the Stochastic Dominance functional-basedrisk averse versions in mathematical optimization under uncertainty

Very frequently, mainly in dynamic problems,some data is uncertain at the decision-making time, although some information is already available. The mathematical optimization models under uncertainty, so-named stochastic optimization ones structure the uncertainty in a set of representative scenarios. The stochastic Risk Neutral (RN) models aim to obtaining a feasible solution for the scenario-based constraint system…

Machine_learning

Seminario 20/22: David Ríos (Real Academia de Ciencias), Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Adversarial Machine Learning (AML) is emerging as a major field aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conflict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benefits that…