Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

Understanding the development of the ESDP: Perspectives and insights from historical institutionalism and theories of learning

How can we best understand the emergence of the European Security and Defence Policy (ESDP)? This paper applies the theories of historical institutionalism and experiential learning to offer a dynamic conceptualisation of moves towards an ESDP which highlights some of the causal factors that a more temporally-restricted analysis would miss. It firstly shows how the institutional and functional expansion of European Political Cooperation (EPC) over the course of the 1970s and 80s gave rise to a context in which the development of a security and defence dimension came to be viewed as more logical and even necessary. It then goes on to analyse some of the external factors (in the form of actors, events and institutions) that further pushed in this direction and proved to influence the policy’s subsequent evolution. The paper is therefore intended to act as a first-step to understanding the ESDP’s development from this perspective.

A Formal Framework for Specifying, Reusing and Implementing Learning Processes. First level

This file contains the ontology of patterns of educational settings, as part of the formal framework for specifying, reusing and implementing educational settings. Furthermore, it includes the set of rules that extend the ontology of educational scenarios as well as a brief description of the level of patters of such ontological framework.

Reduced learning ability as a consequence of evolutionary adaptation to nutritional stress in Drosophila melanogaster

1. Dietary conditions affect cognitive abilities of many species, but it is unclear to what extent this physiological effect translates into an evolutionary relationship. 2. A reduction of competitive ability under nutritional stress has been reported as a correlated response to selection for learning ability in Drosophila melanogaster. Here we test whether the reverse holds as well, i.e. whether an evolutionary adaptation to poor food conditions leads to a decrease in learning capacities. 3. Populations of D. melanogaster were: (i) not subject to selection (control), (ii) selected for improved learning ability, (iii) selected for survival and fast development on poor food, or (iv) subject to both selection regimes. 4. There was no detectable response to selection for learning ability. 5. Selection on poor food led to higher survival, faster development and smaller adult size as a direct response, and to reduced learning ability as a correlated response. This study supports the hypothesis that adaptation to poor nutrition is likely to trade off with the evolution of improved learning ability.

On optimal learning schedules and the marginal value of cumulative cultural evolution.

The age-dependent choice between expressing individual learning (IL) or social learning (SL) affects cumulative cultural evolution. A learning schedule in which SL precedes IL is supportive of cumulative culture because the amount of nongenetically encoded adaptive information acquired by previous generations can be absorbed by an individual and augmented. Devoting time and energy to learning, however, reduces the resources available for other life-history components. Learning schedules and life history thus coevolve. Here, we analyze a model where individuals may have up to three distinct life stages: "infants" using IL or oblique SL, "juveniles" implementing IL or horizontal SL, and adults obtaining material resources with learned information. We study the dynamic allocation of IL and SL within life stages and how this coevolves with the length of the learning stages. Although no learning may be evolutionary stable, we find conditions where cumulative cultural evolution can be selected for. In that case, the evolutionary stable learning schedule causes individuals to use oblique SL during infancy and a mixture between IL and horizontal SL when juvenile. We also find that the selected pattern of oblique SL increases the amount of information in the population, but horizontal SL does not do so.

Curriculum sequencing for an e-learning system based on learning styles

This work shows the use of adaptation techniques involved in an e-learning system that considers students' learning styles and students' knowledge states. The mentioned e-learning system is built on a multiagent framework designed to examine opportunities to improve the teaching and to motivate the students to learn what they want in a user-friendly and assisted environment

Engaging students in an e-learning experience : how to manage information resources through wikis and web blogs

The purpose of this paper is to describe the collaboration between librarians and scholars, from a virtual university, in order to facilitate collaborative learning on how to manage information resources. The personal information behaviour of e-learning students when managing information resources for academic, professional and daily life purposes was studied from 24 semi-structured face-to-face interviews. The results of the content analysis of the interview' transcriptions, highlighted that in the workplace and daily life contexts, competent information behaviour is always linked to a proactive attitude, that is to say, that participants seek for information without some extrinsic reward or avoiding punishment. In the academic context, it was observed a low level of information literacy and it seems to be related with a prevalent uninvolved attitude.

Train-the-trainer: E-learning zur Förderung einer praxisorientiertem Vermittlung von evidenzbasierter Medizin

Hintergrund: Trotz ihrer Etablierung als essentieller Bestandteil der medizinischen Weiter-/Fortbildung werden europa- wie schweizweit kaum Kurse in evidenzbasierter Medizin (ebm) angeboten, die - integriert im klinischen Alltag - gezielt Fertigkeiten in ebm vermitteln. Noch grössere Defizite finden sich bei ebm- Weiterbildungsmöglichkeiten für klinische Ausbilder (z.B. Oberärzte). Als Weiterführung eines EU-finanzierten, klinisch integrierten E-learning- Programms für Weiterbildungsassistenten (www.ebm-unity.org) entwickelte eine europäische Gruppe von medical educators gezielt für Ausbilder ein e-learning-Curriculum zur Vermittlung von ebm im Rahmen der klinischen Weiterbildung. Methode: Die Entwicklung des Curriculums umfasst folgende Schritte: Beschreibung von Lernzielen, Identifikation von klinisch relevanten Lernumgebungen, Entwicklung von Lerninhalten und exemplarischen didaktischen Strategien, zugeschnitten auf die jeweilige Lernumgebungen, Design von web-basierten Selbst-Lernsequenzen mit Möglichkeiten zur Selbstevaluation, Erstellung eines Handbuchs. Ergebnisse: Lernziele des Tutoren-Lehrgangs sind der Erwerb von Fertigkeiten zur Vermittlung der 5 klassischen ebm-Schritte: PICO- (Patient-Intervention-Comparison-Outcome)-Fragen, Literatursuche, kritische Literaturbewertung, Übertragung der Ergebnisse im eigenen Setting und Implementierung). Die Lehrbeispiele zeigen angehenden ebm-Tutoren, wie sich typische klinische Situationen wie z.B. Stationsvisite, Ambulanzsprechstunde, Journalclub, offizielle Konferenzen, Audit oder das klinische Assessment von Weiterbildungsassistenten gezielt für die Vermittlung von ebm nutzen lassen. Kurze E-Learning-Module mit exemplarischen «real-life»-Video-Clips erlauben flexibles Lernen zugeschnitten auf das knappe Zeitkontingent von Ärzten. Eine Selbst-Evaluation ermöglicht die Überprüfung der gelernten Inhalte. Die Pilotierung des Tutoren-Lehrgangs mit klinisch tätigen Tutoren sowie die Übersetzung des Moduls in weitere Sprachen sind derzeit in Vorbereitung. chlussfolgerung: Der modulare Train-the-Trainer-Kurs zur Vermittlung von ebm im klinischen Alltag schliesst eine wichtige Lücke in der Dissemination von klinischer ebm. Webbasierte Beispiele mit kurzen Sequenzen demonstrieren typische Situationen zur Vermittlung der ebm-Kernfertigkeiten und bieten medical educators wie Oberärzten einen niedrigschwelligen Einstieg in «ebm» am Krankenbett. Langfristiges Ziel ist eine europäische Qualifikation für ebm- Learning und -Teaching in der Fort- und Weiterbildung. Nach Abschluss der Evaluation steht das Curriculum interessierten Personen und Gruppen unter «not-for-profit»-Bedingungen zur Verfügung. Auskünfte erhältlich von rkunz@uhbs.ch. Finanziert durch die Europäische Kommission - Leonardo da Vinci Programme - Transfer of Innovation - Pilot Project for Lifelong Learn- ing 2007 und das Schweizerische Staatssekretariat für Bildung und Forschung.

Implementing statistical learning methods through Bayesian networks (Part 2): bayesian evaluations for results of black toner analyses in forensic document examination

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Cap a una definició inclusiva de l'e-learning

Peer-reviewed

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.