Voluntades anticipadas y su estatus bioético

Las voluntades anticipadas se deben distinguir del consentimiento informado ya que resultan más amplias que éste: la manifestación de la persona no se limita a la aceptación de lo que el facultativo le ha propuesto en un momento determinado. Parece lógico pensar que las decisiones deberían tomarse basándose en el conocimiento y en la comprensión de los datos médicos, los pronósticos de la enfermedad y los objetivos conversados entre paciente y profesional, y no bajo la presión de distintas circunstancias. En este contexto las decisiones sobre “voluntades anticipadas” presentan un estatus bioético particular, que es necesario evaluar para lograr tomar la decisión más correcta.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.

Derecho natural y ciencia jurídica : consideraciones sobre la ciencia del derecho como ciencia práctica

Resumen: En el trabajo se aborda la vexata quaestio de la cientificidad de la ciencia jurídica, a la que se le impugna la pretensión de ser científica y, a la vez, práctica. Se estudian las impugnaciones realizadas en ese sentido por Claude Lévi-Strauss y Hans Albert, así como los intentos de algunos filósofos del derecho analíticos de superarla a través del análisis lógico del lenguaje jurídico. Se analizan después los cuestionamientos efectuados por algunos representantes de la filosofía hermenéutica, que critican los ensayos analíticos y proponen una concepción interpretativa de la ciencia jurídica. Luego de esta exposición, el A. evalúa las aportaciones analíticas y hermenéuticas, poniendo de relieve sus fortalezas y sus falencias a la hora de establecer el estatuto científico de la ciencia jurídica, para pasar finalmente a proponer una concepción de la ciencia del derecho heredera de la tradición de la filosofía práctica de cuño aristotélico. En este punto, el A. desarrolla —con base en el pensamiento de Tomás de Aquino— una concepción analógica del conocimiento científico y explica de qué modo es posible aplicarla a un saber acerca del derecho que resulte, a la vez, científico y práctico. Finalmente, desenvuelve la necesidad epistémica de una apertura del conocimiento de la ciencia jurídica a los primeros principios prácticos, que la vincula constitutivamente a la doctrina de la ley natural, así como al nivel máximamente determinado de la prudencia jurídica.

On the Informational Role of Term Structure in the U.S. Monetary Policy Rule

This paper uses a structural approach based on the indirect inference principle to estimate a standard version of the new Keynesian monetary (NKM) model augmented with term structure using both revised and real-time data. The estimation results show that the term spread and policy inertia are both important determinants of the U.S. estimated monetary policy rule whereas the persistence of shocks plays a small but significant role when revised and real-time data of output and inflation are both considered. More importantly, the relative importance of term spread and persistent shocks in the policy rule and the shock transmission mechanism drastically change when it is taken into account that real-time data are not well behaved.

The Importance of Stock Market Returns in Estimated Monetary Policy Rules: a Structural Approach

This paper estimates a standard version of the New Keynesian Monetary (NKM) model augmented with financial variables in order to analyze the relative importance of stock market returns and term spread in the estimated U.S. monetary policy rule. The estimation procedure implemented is a classical structural method based on the indirect inference principle. The empirical results show that the Fed seems to respond to the macroeconomic outlook and to the stock market return but does not seem to respond to the term spread. Moreover, policy inertia and persistent policy shocks are also significant features of the estimated policy rule.

Term Structure and the Estimated Monetary Policy Rule in the Eurozone

Published as an article in: Spanish Economic Review, 2008, vol. 10, issue 4, pages 251-277.

A factor model of seasonality in stock returns

Published as an article in: The Quarterly Review of Economics and Finance, 2004, vol. 44, issue 2, pages 224-236.

Regional Entrepreneurial Scorecard

[ES] Este trabajo analiza la relación entre el desarrollo regional y la creación de empresas desde una perspectiva micro del enfoque institucional, a partir de los stakeholders más relevantes que intervienen en el proceso. La contribución de los emprendedores al crecimiento económico regional viene siendo objeto de especial atención por los poderes públicos, para lo que se necesita un sistema de referencias que permita evaluar la adecuación de los programas públicos de fomento de la actividad emprendedora.

Ontologies for representation of folk song metadata

The digital management of collections in museums, archives, libraries and galleries is an increasingly important part of cultural heritage studies. This paper describes a representation for folk song metadata, based on the Web Ontology Language (OWL) implementation of the CIDOC Conceptual Reference Model. The OWL representation facilitates encoding and reasoning over a genre ontology, while the CIDOC model enables a representation of complex spatial containment and proximity relations among geographic regions. It is shown how complex queries of folk song metadata, relying on inference and not only retrieval, can be expressed in OWL and solved using a description logic reasoner.

Facilitated advocacy: supporting farmers to have a voice in policy making and the development of service provision

Listening to people, especially those who are poor, and involving them in policy making and decisions about service delivery processes are logical steps in building better services and improving policies aimed at poverty alleviation. This case describes a facilitated advocacy that helped to negotiate and support a role for poor people who farm and fish, to contribute recommendations for changes in services and policies that impact on their lives. The national Government of India’s Department of Animal Husbandry and Dairying and the Indian Council for Agricultural Research, both in the capital Delhi, have been linking with farmers and fishers and state government officials in the eastern states of Jharkhand, Orissa and West Bengal, in partnership with the STREAM Initiative of the intergovernmental Network of Aquaculture Centers in Asia Pacific and with the support of the UK Government Department for International Development, Natural resources Systems Program supporting farmers to have a voice(13 p.)

The Effect of Data Revisions on the Basic New Keynesian Model

This paper proposes an extended version of the basic New Keynesian monetary (NKM) model which contemplates revision processes of output and inflation data in order to assess the importance of data revisions on the estimated monetary policy rule parameters and the transmission of policy shocks. Our empirical evidence based on a structural econometric approach suggests that although the initial announcements of output and inflation are not rational forecasts of revised output and inflation data, ignoring the presence of non well-behaved revision processes may not be a serious drawback in the analysis of monetary policy in this framework. However, the transmission of inflation-push shocks is largely affected by considering data revisions. The latter being especially true when the nominal stickiness parameter is estimated taking into account data revision processes.