O COMPORTAMENTO DO CONSUMIDOR NA ECONOMIA INFORMAL DA REGIÃO DO GRANDE ABC PAULISTA

Diminuir o consumo de produtos oriundos da economia informal e conscientizar os consumidores acerca dos malefícios do mesmo tem sido um imperativo para os órgãos governamentais, organizações privadas e instituições não governamentais que prezam pela melhoria no ambiente de negócios. No entanto, apesar do apelo feito aos consumidores para não adquirirem produtos do mercado informal, é possível notar nas calçadas das ruas e avenidas a existência de inúmeros pontos de venda informais. Neste contexto, esta pesquisa tem como objetivo identificar e analisar os fatores influenciadores do comportamento do consumidor de produtos adquiridos na economia informal da Região do Grande ABC Paulista. Para tanto, foi realizado um estudo qualitativo, de caráter exploratório, cujos dados primários foram coletados por meio de entrevistas semiestruturadas e os dados secundários extraídos da literatura acerca do comportamento do consumidor considerando-se os fatores influenciadores: cultura, ética e responsabilidade social, bem como, a economia informal. Participaram das entrevistas pessoas economicamente ativas com idade entre 25 e 44 anos, consumidoras de produtos oriundos do comércio informal e residentes na Região do Grande ABC Paulista. Com base nos resultados da pesquisa empírica é possível inferir que os consumidores efetuam compras no comércio informal devido ao preço e acessibilidade ao ponto de venda. Trata-se de um consumo culturalmente estabelecido, devido à disseminação do mesmo entre as redes sociais das quais os consumidores fazem parte. De maneira geral, os entrevistados mostram-se conscientes sobre os malefícios sociais, ambientais e éticos da economia informal, mas pouco os consideram no momento da compra.

A Rádio-peão no ar Um estudo sobre a comunicação informal e a comunicação formal face a face no ambiente organizacional

A rádio-peão é estudada nos Estados Unidos desde o período pós II Guerra Mundial. No Brasil, este processo comunicacional ganhou relevância no final da década de 1970 com os movimentos operários que buscavam formas democráticas de diálogo, durante o regime militar. Já a comunicação formal face a face começou a ser praticada nas organizações brasileiras em meados dos anos 1990, com a chegada de novos modelos internacionais de gestão empresarial. Ao estudar estes dois formatos de comunicação, através de pesquisas bibliográfica e documental e entrevistas semi-abertas com acadêmicos e profissionais de mercado de diferentes áreas de conhecimento e atuação, foi possível um aprofundamento acerca de suas histórias, atributos e papéis desempenhados hoje, nas organizações, além de como essas formas de comunicação face a face (formal e informal) interagem entre si, de acordo com interesses pessoais ou organizacionais.(AU)

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in code division multiple access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also studied. © 2006 Elsevier B.V. All rights reserved.

Improved message passing for inference in densely connected systems

An improved inference method for densely connected systems is presented. The approach is based on passing condensed messages between variables, representing macroscopic averages of microscopic messages. We extend previous work that showed promising results in cases where the solution space is contiguous to cases where fragmentation occurs. We apply the method to the signal detection problem of Code Division Multiple Access (CDMA) for demonstrating its potential. A highly efficient practical algorithm is also derived on the basis of insight gained from the analysis. © EDP Sciences.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

A basis function approach to Bayesian inference in diffusion processes

In this paper, we present a framework for Bayesian inference in continuous-time diffusion processes. The new method is directly related to the recently proposed variational Gaussian Process approximation (VGPA) approach to Bayesian smoothing of partially observed diffusions. By adopting a basis function expansion (BF-VGPA), both the time-dependent control parameters of the approximate GP process and its moment equations are projected onto a lower-dimensional subspace. This allows us both to reduce the computational complexity and to eliminate the time discretisation used in the previous algorithm. The new algorithm is tested on an Ornstein-Uhlenbeck process. Our preliminary results show that BF-VGPA algorithm provides a reasonably accurate state estimation using a small number of basis functions.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to nonlinear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, the evidence procedure, and an optimisation algorithm. The technique was tested on two regression applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

The informal economy in the former Soviet Union and in Central and Eastern Europe

Guest editors' introduction

Workplace crime and the informal economy in Ukraine: Employee and employer perspectives

Purpose – The purpose of this paper is to explore the criminal workplace activities of both employers and employees in Ukrainian enterprises. It challenges traditional definitions of corruption and suggests that the practices that can be observed fit into the category of organised crime because of the country's economic framework. The paper also explores how the practices are partially a legacy of Soviet economic processes. Design/methodology/approach – A total of 700 household surveys were completed in three cities, Kyiv (where 450 surveys were completed), Uzhgorod (150) and Kharkiv (100). To complement these, approximately 25 in-depth interviews were undertaken with workers in each region. Furthermore, ethnographic observations and “kitchen table” interviews also played an important role in the research. Although the research was oriented towards those working in informal economies, business owners (both formal and informal) were also interviewed. Findings – As well as revealing the endemic nature of corruption in Ukrainian workplaces and the high levels of informal activity undertaken by workers, the research found that many people wish for their workplace to become more regulated. Research limitations/implications – Further interviews could have been carried out with state officials and in more locations. The implications are multiple but mainly they demonstrate the difficulty that those charged with economic reform in Ukraine must face. Originality/value – It is one of the first studies to explore these issues in Ukraine using a variety of research methods.