Semiosis,thought and codes: A theoretical framework for social knowledge

I present a theoretical social psychological framework for social knowledge based mainly on the triadic concept of sign relations and the process of semiosis. The framework is based on concepts from authors aligned with interpretative semiotic approaches, such as Peirce, Deely and Eco. After defining signs and sign relations, I propose to conceive social knowledge as sign relations happening in communication processes, and employ the notion of code to account for socially shared group conventions. I suggest that the analysis of code properties such as content, social acknowledgment and history can identify differences in social knowledge forms and the concrete contexts that sustain them. The framework is discussed in comparison with other social and cultural psychological approaches based on semiosis. Finally, methodological implications are commented.     

The footwear factory's assembly sector: opposing organizational structure and quality from the ergonomic work analysis

Data from 2006 indicated that Brazilian footwear leather industry was composed of approximately 9,488 formally registered establishments, considering the leather industry, footwear and leather goods. It was responsible for 211,582 people employed. However, in spite of having many employees, this kind of organization found several problems when analyzed from the ergonomics view. With this premise, then, in order to identify bottlenecks and other engineering problems that could cause discomfort and motivation lack among workers, leading directly to the loss of the product quality, the assembly sector of women's shoes factory was characterized.     

Bayesian Poisson models for the graphical combination of dependent expert information

A supra-Bayesian (SB) wants to combine the information from a group of k experts to produce her distribution of a probability θ. Each expert gives his counts of what he thinks are the numbers of successes and failures in a sequence of independent trials, each with probability θ of success. These counts, used as a surrogate for each expert's own individual probability assessment (together with his associated level of confidence in his estimate), allow the SB to build various plausible conjugate models. Such models reflect her beliefs about the reliability of different experts and take account of different possible patterns of overlap of information between them. Corresponding combination rules are then obtained and compared with other more established rules and their properties examined.     

Decision analysis using lottery-dependent utility

In this article we show how the lottery-dependent expected utility (LDEU) model can be used in decision analysis. The LDEU model is an extension of the classical expected utility (EU) model and yet permits preference patterns that are infeasible in the EU model. We propose a framework for constructing decision trees in a particular way that permits us to use the principle of optimality and thus the divide and conquer strategy for analyzing complex problems using the LDEU model. Our approach may be applicable to some other nonlinear utility models as well. The result is that, if desired, decision analysis can be conducted without assuming the restrictive substitution principle/independence axiom.     

Financial data modeling by Poisson mixture regression

In many financial applications, Poisson mixture regression models are commonly used to analyze heterogeneous count data. When fitting these models, the observed counts are supposed to come from two or more subpopulations and parameter estimation is typically performed by means of maximum likelihood via the Expectation–Maximization algorithm. In this study, we discuss briefly the procedure for fitting Poisson mixture regression models by means of maximum likelihood, the model selection and goodness-of-fit tests. These models are applied to a real data set for credit-scoring purposes. We aim to reveal the impact of demographic and financial variables in creating different groups of clients and to predict the group to which each client belongs, as well as his expected number of defaulted payments. The model's conclusions are very interesting, revealing that the population consists of three groups, contrasting with the traditional good versus bad categorization approach of the credit-scoring systems.     

Variable Selection Methods in High-dimensional Regression—A Simulation Study

A challenging problem in the analysis of high-dimensional data is variable selection. In this study, we describe a bootstrap based technique for selecting predictors in partial least-squares regression (PLSR) and principle component regression (PCR) in high-dimensional data. Using a bootstrap-based technique for significance tests of the regression coefficients, a subset of the original variables can be selected to be included in the regression, thus obtaining a more parsimonious model with smaller prediction errors. We compare the bootstrap approach with several variable selection approaches (jack-knife and sparse formulation-based methods) on PCR and PLSR in simulation and real data.     

Negatively correlated author seniority and the number of acknowledged people: Name-recognition as a signal of scientific merit?

Evidence from five general-interest journals in economics reveals an inverse relationship between author seniority and the number of colleagues whom authors choose to thank and acknowledge. The large seniority effect is insensitive to the inclusion of controls for the number of co-authors, number of pages, number of words in the title, and journal fixed effects. The data are consistent with the hypothesis that name-recognition is an important signal used by economists in evaluating scientific merit.     

Modeling and forecasting populations by time series: The Swedish case

Time series analysis techniques are used to model and to forecast populations. An autoregressive (AR) and a moving average (MA) model, which seem to fit the population of Sweden very well, are found. Forecasts are calculated using both models and are compared with the forecasts obtained by other methods. This comparison is very favorable for the time series models. Although our study is confined to the mid-year population of Sweden, there are good reasons to expect that the technique can be successfully applied to other population parameters.     

Search and research: the influence of editorial boards on journals’ quality

This paper considers the search for the best papers by the editors of an academic journal. At each period, each editor receives a set of submissions and has to decide which paper to accept. Some editors being more demanding than others, researchers choose the quality level of their papers taking as given the composition of the editorial board. According to the specific structures of the editorial board, various equilibria may appear. We show that the journal will publish a high number of high quality papers only when the editorial board is composed by a homogeneous set of very demanding editors.