Open Source Software Production, Spontaneous Input, and Organizational Learning

This work shows that the modular organization of voluntary Open Source Software (OSS) production, whereby programmers supply effort of their accord, capitalizes more on division than on specialization of labor. This is so because voluntary OSS production is characterized by an organizational learning process that dominates the individual one. Organizational learning reveals production choices that would otherwise remain unknown, thereby increasing productivity and indirectly reinforcing incentives to undertake collective problem solving.Division of Labor; Mistake-ridden Learning; Modularity; Open Source Software; Self-selection; Voluntary Production

Learning Non-linear Structures with Gaussian Markov Random Fields

AbstractNowadays, one of the most changeling points in statistics is the analysis of high dimensional data. In such cases, it is commonly assumed that the dimensionality of the data is only artificially high: although each data point is described by thousands of features, it is assumed that it can be modeled as a function of only a few underlying parameters. Formally, it is assumed that the data points are samples from a low-dimensional manifold embedded in a high-dimensional space.In this paper, we discuss a recently proposed method, known as Maximum Entropy Unfolding (MEU), for learning non-linear structures that characterize high dimensional data.This method represents a new perspective on spectral dimensionality reduction and, joined with the theory of Gaussian Markov random fields, provides a unifying probabilistic approach to spectral dimensionality reduction techniques. Parameter estimation as well as approaches to learning the structure of the GMRF are discusse

José Albiñana and the Royal Palace

La “ciencia” de la fotohistoria de España ha atraído a muchos investigadores en la dirección de lugares específicos o de los estudios monográficos; a veces incluso en la dirección de los estudios de casos en particular. Los archivos del Palacio Real de Madrid, parte de los cuales se abordan en esta disertación, son solamente uno de ellos. La tentativa de interpretar aquéllos como fuentes de información solapadas podría resultar ser un verdadero reto, debido en gran parte a su naturaleza repetitiva y a su tendencia de ser a veces contradictorias. Los archivos de Palacio mencionan repetidas veces a José Albiñana (1819‐1879). Albiñana merece una atención detallada. El caso Albiñana/Palacio Real es un trocito de fotohistoria que ilumina una situación más general; una “micro‐historia” que puede ser metáfora por algo más grande.The "science" of the Spanish photo history has attracted many researchers in a specific direction and monographic studies; sometimes even they get interested in particular case studies. The archives of the Royal Palace in Madrid, part of which are addressed in this dissertation, are just an example. The attempt to understand those as overlapping sources of information could be a real challenge due to their repetitive nature and its tendency to turn out being contradictory at times. Jose Albiñana (1819‐1879) has been recurrently mentioned by the archives of the Palace and his personality deserves a detailed attention. The Albiñana / Palacio Real case is a piece of photo history that speaks about a general situation; a small part that can be used as a metaphor for something bigger

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions