Ideological Closure : Drug Prevention in a Post-political Society

The aim of this thesis is to critically examine drug prevention as a field of problematizations  – how drug prevention becomes established as a political technology within this field, how it connects to certain modes of governance, how and under which conditions it constitutes it’s problematic, the questions it asks,  it´s implications in terms of political participation and representation, the various bodies of knowledge through which it constitutes the reality upon which it acts, the limits it places on ways of being, questioning, and talking  in the world. The main analyses have been conducted in four separate but interrelated articles. Each article addresses a specific dimension of drug prevention in order to get a grasp of how this field is organized. Article 1 examines the shift that has occurred in the Swedish context during the period 1981–2011 in how drugs have been problematized, what knowledge has grounded the specific modes of problematization and which modes of governance this has enabled. In article 2, the currently dominant scientific discipline in the field of drug prevention – prevention science – is critically examined in terms of how it constructs the “drug problem” and the underlying assumptions it carries in regard to reality and political governance. Article 3 addresses the issue of communities’ democratic participation in drug prevention efforts by analyzing the theoretical foundations of the Communities That Care prevention program. The article seeks to uncover how notions of community empowerment and democratic participation are constructed, and how the “community” is established as a political entity in the program. The fourth and final article critically examines the Swedish Social and Emotional Training (SET) program and the political implications of the relationship the program establishes between the subject and emotions. The argument is made that, within the field of drug prevention, questions of political values and priorities in a problematic way are decoupled from the political field and pose a significant problem in terms of the possibilities to engage in democratic deliberation. Within this field of problematizations it becomes impossible to mobilize a politics against social injustice, poverty and inequality. At the same time, the scientific grounding of this mode of governing the drug “problem” acts to naturalize a specific – highly political – way of engaging with drugs.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional probability densities for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.