Uma avaliação ex-ante do impacto sobre a capacidade tecnológica da localidade acolhedora das empresas que aderiram ao regime automotivo brasileiro e os seus desdobramentos estaduais : o caso da instalação da General Motors do Brasil em Gravataí, Rio Grande do Sul

Esta tese discute três temas: políticas públicas, gestão tecnológica, e setor automotivo. Tendo por objetivo abreviar o ciclo de absorção e desenvolvimento de tecnologia, um volume expressivo de recursos tem sido transferido do setor público para o setor privado através do que é denominado de Política Pública Indutora (PPI). Os governos pretendem, assim, atrair aquelas empresas tecnologicamente mais capacitadas, na expectativa de que transfiram para a localidade onde se instalam o conhecimento que detêm. No Brasil, um dos setores-alvo deste tipo de política tem sido o automotivo, circunstância observada em diferentes momentos da história. Efetivamente, o Regime Automotivo Brasileiro pretende não apenas acelerar o desenvolvimento do país, mas também promover uma significativa transferência de tecnologia. A análise das PPI, por ser de extrema importância, é bastante influenciada e dificultada quer por seus defensores, quer por seus destratores, que as veêm sob os aspectos de sucesso ou não; mas, não bastasse essa dificuldade, há também o elevado conteúdo ideológico que sustenta as argumentações, que faz com que a avaliação se perca num quadro inconclusivo. Afinal, estas iniciativas são benéficas ou não para o país e para as economias regionais? Finalmente, a eficácia, e portanto o acerto desta estratégia só pode ser avaliado expost facto, quando já comprometidos, quiçá irremediavelmente, os recursos públicos. Por essa razão, este estudo desenvolve uma análise ex-ante das políticas públicas do tipo indutoras, fazendo uso de um modelo compreensivo que permite uma análise longitudinal, captando assim, as mudanças no ambiente. Entre outras, procurou-se responder à seguinte questão: é possível, hoje, inferir quanto à contrib uição, se positiva ou negativa, que o Regime Automotivo Brasileiro e os seus desdobramentos estaduais trarão à capacidade tecnológica no entorno da empresa atraída? O problema e a questão de pesquisa foram abordados, predominantemente, sob um enfoque qualitativo, e o método escolhido foi o estudo de caso. Com o auxílio do modelo proposto foi analisada e avaliada a potencialidade de aumento na capacidade tecnológica induzida pela instalação da unidade montadora da General Motors do Brasil, em Gravataí, Rio Grande do Sul. Ao final conclui- se que os benefícios previstos pelo Regime Automotivo Brasileiro, no que diz respeito a capacitação tecnológica local, dificilmente serão atingidos pela instalação de novas empresas automotivas ou a modernização das existentes.

Graphical models and point set matching

Point pattern matching in Euclidean Spaces is one of the fundamental problems in Pattern Recognition, having applications ranging from Computer Vision to Computational Chemistry. Whenever two complex patterns are encoded by two sets of points identifying their key features, their comparison can be seen as a point pattern matching problem. This work proposes a single approach to both exact and inexact point set matching in Euclidean Spaces of arbitrary dimension. In the case of exact matching, it is assured to find an optimal solution. For inexact matching (when noise is involved), experimental results confirm the validity of the approach. We start by regarding point pattern matching as a weighted graph matching problem. We then formulate the weighted graph matching problem as one of Bayesian inference in a probabilistic graphical model. By exploiting the existence of fundamental constraints in patterns embedded in Euclidean Spaces, we prove that for exact point set matching a simple graphical model is equivalent to the full model. It is possible to show that exact probabilistic inference in this simple model has polynomial time complexity with respect to the number of elements in the patterns to be matched. This gives rise to a technique that for exact matching provably finds a global optimum in polynomial time for any dimensionality of the underlying Euclidean Space. Computational experiments comparing this technique with well-known probabilistic relaxation labeling show significant performance improvement for inexact matching. The proposed approach is significantly more robust under augmentation of the sizes of the involved patterns. In the absence of noise, the results are always perfect.

A BDI-based approach for the assessment of driver's decision-making in commuter scenarios

The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.