Desire thinking and craving across the continuum of problem drinking

Desire thinking has been conceptualized as a conscious and voluntary cognitive process prefiguring images, information and memories about positive target-related experience. In the last few years, desire thinking has been found to be closely involved in addictive behaviours (substance and behavioural addictions). Research in this field has investigated the role of desire thinking in increasing craving experience and leading to problematic behaviours (such as binge drinking and gambling). So far, studies on desire thinking have focused especially on drinking behaviour. Preliminary evidence is also emerging in the field of behavioural addictions. The first aim of this thesis was to investigate desire thinking across addictive behaviours, through a systematic review of existing studies (first study of the present thesis). The ten included studies highlighted a significant relationship between desire thinking and addictive behaviour in all conditions (alcohol use, nicotine use, gambling, problematic internet use), even though the nature of studies were mostly cross-sectional. The second and the third studies of my thesis aimed to explore longitudinally, in clinical and non- clinical populations, the involvement of desire thinking in increasing craving experience (supporting previous data) and assessing its impact (over and above craving) in leading to binge drinking and alcohol abuse/relapse (adding new findings in the field of alcohol problems and therapies). Findings showed that desire thinking predicted craving and binge drinking in both samples and predict relapse at follow ups in people with severe alcohol use disorder. Furthermore, the components of desire thinking were found to be differently implicated in alcohol problems (imaginal prefiguration predicts craving levels at follow-up and verbal perseveration were found to be the predictor of binge drinking frequency at follow-up. As a whole, the results of the studies reported in this thesis will provide support for the central role of desire thinking in increasing craving experience and leading to alcohol use (over and above the level of craving). In other words, engaging in desire thinking gradually leads to an escalation of craving increasing the salience of using alcohol as a means of attaining control. According with this view, therapies should aim at helping patients reducing their desire thinking and mental activities related to imagining how to reach and use their desired target

Effective Sample Size for Importance Sampling based on discrepancy measures

The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the IS context, an approximation $\widehat{ESS}$ of the theoretical ESS definition is widely applied, involving the inverse of the sum of the squares of the normalized importance weights. This formula, $\widehat{ESS}$, has become an essential piece within Sequential Monte Carlo (SMC) methods, to assess the convenience of a resampling step. From another perspective, the expression $\widehat{ESS}$ is related to the Euclidean distance between the probability mass described by the normalized weights and the discrete uniform probability mass function (pmf). In this work, we derive other possible ESS functions based on different discrepancy measures between these two pmfs. Several examples are provided involving, for instance, the geometric mean of the weights, the discrete entropy (including theperplexity measure, already proposed in literature) and the Gini coefficient among others. We list five theoretical requirements which a generic ESS function should satisfy, allowing us to classify different ESS measures. We also compare the most promising ones by means of numerical simulations

On the strategy frequency problem in batch Minority Games

Ergodic stationary states of Minority Games with S strategies per agent can be characterised in terms of the asymptotic probabilities $\phi_a$ with which an agent uses $a$ of his strategies. We propose here a simple and general method to calculate these quantities in batch canonical and grand-canonical models. Known analytic theories are easily recovered as limiting cases and, as a further application, the strategy frequency problem for the batch grand-canonical Minority Game with S=2 is solved. The generalization of these ideas to multi-asset models is also presented. Though similarly based on response function techniques, our approach is alternative to the one recently employed by Shayeghi and Coolen for canonical batch Minority Games with arbitrary number of strategies.Comment: 17 page

Theory of controlled quantum dynamics

We introduce a general formalism, based on the stochastic formulation of quantum mechanics, to obtain localized quasi-classical wave packets as dynamically controlled systems, for arbitrary anharmonic potentials. The control is in general linear, and it amounts to introduce additional quadratic and linear time-dependent terms to the given potential. In this way one can construct for general systems either coherent packets moving with constant dispersion, or dynamically squeezed packets whose spreading remains bounded for all times. In the standard operatorial framework our scheme corresponds to a suitable generalization of the displacement and scaling operators that generate the coherent and squeezed states of the harmonic oscillator.Comment: LaTeX, A4wide, 28 pages, no figures. To appear in J. Phys. A: Math. Gen., April 199

Dymanics of Generalized Coherent States

We show that generalized coherent states follow Schr\"{o}dinger dynamics in time-dependent potentials. The normalized wave-packets follow a classical evolution without spreading; in turn, the Schr\"{o}dinger potential depends on the state through the classical trajectory. This feedback mechanism with continuous dynamical re-adjustement allows the packets to remain coherent indefinetely.Comment: 8 pages, plain latex, no figure

Parallel Metropolis chains with cooperative adaptation

Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC scheme where parallel MCMC chains interact, adapting cooperatively the parameters of their proposal functions. Furthermore, the novel algorithm distributes the computational effort adaptively, rewarding the chains which are providing better performance and, possibly even stopping other ones. These extinct chains can be reactivated if the algorithm considers necessary. Numerical simulations shows the benefits of the novel scheme