Sociologia moral das dependências motivadas: o caso da dependência amorosa

Este artigo propõe um panorama do procedimento e dos métodos da sociologia moral, que se baseiam em uma etnofilosofia da existência social comum e em uma antropologia natural. A primeira parte do texto situa a sociologia moral em relação ao desenvolvimento contemporâneo das pesquisas empíricas sobre a ética, sobretudo no âmbito da psicologia experimental e evolucionista. A segunda parte apresenta alguns elementos de uma teoria das dependências motivadas aos prazeres e às recompensas, desenvolvida a partir do aporte das neurociências da adição e de pesquisas de campo sobre as condutas aditivas e as tentativas de restabelecimento. A terceira parte propõe uma ilustração do método a partir de uma pesquisa recente sobre a dependência amorosa, feita principalmente a partir de um corpus literário e cinematográfico

Sampling from Archimedean copulas

We develop sampling algorithms for multivariate Archimedean copulas. For exchangeable copulas, where there is only one generating function, we first analyse the distribution of the copula itself, deriving a number of integral representations and a generating function representation. One of the integral representations is related, by a form of convolution, to the distribution whose Laplace transform yields the copula generating function. In the infinite-dimensional limit there is a direct connection between the distribution of the copula value and the inverse Laplace transform. Armed with these results, we present three sampling algorithms, all of which entail drawing from a one-dimensional distribution and then scaling the result to create random deviates distributed according to the copula. We implement and compare the various methods. For more general cases, in which an N-dimensional Archimedean copula is given by N-1 nested generating functions, we present algorithms in which each new variate is drawn conditional only on the value of the copula of the previously drawn variates. We also discuss the use of composite nested and exchangeable copulas for modelling random variates with a natural hierarchical structure, such as ratings and sectors for obligors in credit baskets.

A copula-VAR-X approach for industrial production modelling and forecasting

World economies, and especially European ones, have become strongly interconnected in the last decade and a joint modelling is required. We propose here the use of copulae to build flexible multivariate distributions, since they allow for a rich dependence structure and more flexible marginal distributions that better fit the features of empirical data, such as leptokurtosis. We use our approach to forecast industrial production series in the core European Monetary Union (EMU) countries and we provide evidence that the copula-Vector Autoregression (VAR) model outperforms or at worst compares similarly to normal VAR models, keeping the same computational tractability of the latter approach

ChemCam Instrument for the Mars Science Laboratory (MSL) Rover

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