Essays on incentives, procurement and regulation

Esta tese é composta de três artigos. No primeiro artigo, "Simple Contracts under Simultaneous Adverse Selection and Moral Hazard", é considerado um problema de principal-agente sob a presença simultânea dos problemas de risco moral e seleção adversa, em que a dimensão de seleção adversa se dá sobre as distribuições de probabilidade condicionais as ações do agente. No segundo artigo, "Public-Private Partnerships in the Presence of Adverse Selection" é analisada a otimalidade de parcerias público-privadas sob a presença de seleção adversa. No terceiro artigo, "Regulation Under Stock Market Information Disclosure", por sua vez, é considerado o problema da regulação de firmas de capital aberto, onde as firmas possuem incentivos para mandar sinais opostos para o regulador e o mercado.

HIV, EBV and KSHV: Viral cooperation in the pathogenesis of human malignancies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Latin America and the Caribbean in the World Economy 2008-2009: Crisis and opportunities for regional cooperation

Incluye Bibliografía

Towards new forms of economic cooperation between Latin America and Japan

Includes bibliography

Regional financial cooperation

Includes bibliography

Regional financial cooperation: experiences and challenges

Includes bibliography

Subregional financial cooperation: the experiences of Latin America and the Caribbean

Includes bibliography

An analysis of financial and monetary cooperation in Africa

Includes bibliography

Opportunities for convergence and regional cooperation: Unity Summit of Latin America and the Caribbean

Includes Bibliography