Decision making in a human population living sustainably

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.

Constraining ACT-R models of decision strategies: An experimental paradigm

It has been repeatedly debated which strategies people rely on in inference. These debates have been difficult to resolve, partially because hypotheses about the decision processes assumed by these strategies have typically been formulated qualitatively, making it hard to test precise quantitative predictions about response times and other behavioral data. One way to increase the precision of strategies is to implement them in cognitive architectures such as ACT-R. Often, however, a given strategy can be implemented in several ways, with each implementation yielding different behavioral predictions. We present and report a study with an experimental paradigm that can help to identify the correct implementations of classic compensatory and non-compensatory strategies such as the take-the-best and tallying heuristics, and the weighted-linear model.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Accès des patients agés aux urgences: evolution démographique et perspectives médico-ethiques [Access of elderly patients in the emergency department: demographic evolution and ethical perspectives].

Due to actual demographic evolution, emergency departments have to face a dramatic increase in admissions of elderly people. The peculiar medical and socio-demographic characteristics of these old patients emphasize the need of specific decision processes and resources allocation. An individual-based approach, related to significant ethical values, should allow better diagnostic and therapeutic attitudes. Such a way to admit, evaluate and treat older patients implies an active collaboration with patients and their relatives, but also with all medical interveners, including in particular primary care physicians.

The role of Notch receptor signaling in T helper 17 cell differentiation

Antigenic recognition by naive CD4+ T cells induces their proliferation and differentiation into functionally distinct T helper (Th) cell. Each CD4+ Th cell subset expresses specific transcription factors and produces signature cytokines that coordinate immune responses against encountered pathogens. Among the factors influencing CD4+ Th cell differentiation, Notch signaling pathway has been reported to play a role in the differentiation and function of multiple CD4+Thcell subsets. Notch signaling is an evolutionarily conserved cell-to-cell signaling cascade involved in many cell fate decision processes. How Notch signaling modulates the differentiation of CD4+ Th cell subsets and whether Notch signaling alone is sufficient or not for the differentiation of CD4+ Th cells is still a matter of debate. Th17 cells are a distinct subset of CD4+ Th cells. They play a role in the control of extracellular bacterial and fungal infections and may lead to inflammatory and autoimmune diseases if not properly regulated. Th17 cells are defined by the expression of RAR-related orphan receptor (ROR)a and RORyT transcription factors and their secretion of IL-17A, IL-17F cytokines. The involvement of Notch signaling in Th17 cell differentiation has mostly been studied in vitro. However, neither the experimental conditions when Notch signaling might be involved in Th17 cell differentiation in vitro and in vivo nor the precise role of Notch in this process remain clear. To better define how Notch signaling impacts Th17 differentiation, we used mice with T cell specific ablation of Notchl and Notch2 (N1 N2ACD4Cre) or of Notch transcriptional repressor RBP- JK (RBP-J ACD4Cre). We show that impaired Notch signaling in T cells, when TCR activating signal were reduced, increased RORyT and IL-17 mRNA levels during in vitro Th17 cell differentiation. Following immunization with OVA in CFA, an adjuvant that induces mostly Th17 cell response, increased IL-17A mRNA and intracellular IL-17A levels were observed in draining lymph nodes of Notch-deficient CD4+T cells. Our data suggest that Notch limited Th17 cell differentiation. Despite high levels of IL-17 mRNA and intracellular IL-17 proteins observed in Notch-deficient T cells, their release of Th17 cytokines ex vivo was markedly decreased, indicating a role for Notch signaling. During the second part of this thesis, we observed that the impact of Notch on Th17 cell differentiation and effector functions was context-dependent using different in vivo experimental models, in which Th17 cells and IL-17A were reported to contribute in the disease development. Collectively, our data reveal that Notch signaling controls the fine-tuning of Th17 cell differentiation and effector functions by limiting their differentiation but promoting selectively cytokine release through Notch-dependent mechanisms that still need to be defined. -- Lors d'une réponse immunitaire et grâce à la reconnaissance antigénique, les lymphocytes CD4+ T naïfs prolifèrent, puis se différencient en CD4+ T auxiliaires ("T helper" ou Th) fonctionnellement distincts. Chaque sous-population de lymphocytes CD4+ T auxiliaires exprime des facteurs de transcription et des cytokines spécifiques qui coordonnent la réponse immunitaire contre les pathogènes rencontrés. Parmi les facteurs influençant la différenciation des lymphocytes CD4+ T auxiliaires, la voie de signalisation Notch a été identifiée comme ayant un rôle dans la différenciation et la fonction des différents sous-types de cellules CD4+ T auxiliaires. La voie de signalisation Notch est une voie évolutivement conservée, qui est impliquée dans la signalisation entre les cellules et dans de nombreux processus de décisions cellulaires. La manière dont la voie de signalisation Notch régule la différenciation des lymphocytes CD4+ T en sous-types de cellules CD4+ auxiliaires, mais également la question de savoir si la voie de signalisation Notch est capable ou non d'induire la différenciation des cellules CD4+T auxiliaires, restent à débattre. Les cellules T auxiliaires 17 (Th17) sont un sous-type distinct de cellules CD4+T. Elles jouent un rôle important dans la défense immunitaire contre des pathogènes tels que les bactéries extracellulaires et les champignons. Une dérégulation de la réponse des cellules Th17 peut conduire à des inflammations mais également à des maladies auto-immunes. Les cellules Th17 sont définies par l'expression de leurs facteurs de transcription RAR-related orphan receptor (ROR)a, RORyT et par la sécrétion de cytokines comme IL-17A, IL-17F. Le rôle de la voie de signalisation Notch dans la différenciation des cellules Th17 a principalement été démontré in vitro. Malgré tout, ni les conditions expérimentales dans lesquelles cette voie pourrait être impliquée dans la différenciation des cellules Th17 in vitro et in vivo, mais également ni la fonction exacte de Notch dans ces processus, ne sont des questions résolues. Afin de mieux définir comment la voie de signalisation Notch est impliquée dans la différenciation des cellules Th17, nous avons utilisé des souris avec une déficience spécifique dans les cellules T des récepteurs Notchl et Notch2 (N1N2ACD4Cre) ou du répresseur transcriptionnel de Notch RBP-JK (RBP-J ACD4Cre). Nous avons montré que lorsque la voie de signalisation Notch est déficiente, les niveaux d'ARN messager (ARNm) de RORyT et de IL-17A sont augmentés dans les cellules Th17 pendant la différenciation in vitro, en présence de niveaux réduits des signaux activant les cellules T CD4+. Une augmentation dans les niveaux d'ARNm de IL-17A et de IL-17A intracellulaire au niveau protéinique a été observée dans les cellules T CD4+ Notch déficientes, au niveau des ganglions drainants après immunisation avec l'OVA dans le CFA, un adjuvant induisant une réponse des cellules Th17. Nos résultats suggèrent que Notch pourrait réguler négativement l'expression de IL-17A au niveau transcriptionnel mais également protéinique. Malgré une augmentation de IL-17A au niveau de l'ARNm et protéinique dans les cellules CD4+ T Notch déficientes, paradoxalement la sécrétion de IL-17A mais également de cytokines associées aux fonctions effectrices des cellules Th17 sont profondément diminuées 6X vivo, suggérant un rôle de la voie de signalisation Notch dans ce processus. Dans la deuxième partie de ce travail de thèse, nous avons observé que l'impact de Notch dans la différenciation des cellules Th17 et dans leurs fonctions effectrices était dépendant du contexte dans d'autres modèles expérimentaux in vivo, où les cellules Th17 et l'IL-17A ont été identifiées comme ar-.riCociêSM dans le développement ds la pathologie. En résumé, nous avons montré que la voie de la signalisation Notch contrôle la régulation précise de la différenciation des cellules Th17 en limitant leur différenciation, mais en promouvant sélectivement leur relâchement en cytokines associés aux cellules Th17 par l'intermédiaire de mécanismes dépendant de Notch, qui restent toujours à déterminer. -- Lors d'une réponse immunitaire et grâce à la reconnaissance antigénique, les lymphocytes CD4+ T naïfs prolifèrent, puis se différencient en CD4+ T auxiliaires ("T helper" ou Th) fonctionnellement distincts. Chaque sous-population de lymphocytes T auxiliaires exprime des facteurs de transcription et des cytokines spécifiques qui coordonnent une réponse immunitaire contre différents pathogènes. Les mécanismes liés à la différenciation des lymphocytes CD4+ T auxiliaires sont complexes et régulés. Une mauvaise régulation de la différenciation des lymphocytes CD4+ T auxiliaires peut conduire à des maladies auto-immunes, mais également à des processus inflammatoires. Parmi les facteurs influençant la différenciation des lymphocytes T auxiliaires, la voie de signalisation Notch a été identifiée comme ayant un rôle dans la différenciation et la fonction des différents sous-types de cellules CD4+ T auxiliaires. La voie de signalisation Notch est une voie évolutivement conservée, qui est impliquée dans la signalisation entre les cellules, mais également dans de nombreux processus de décisions cellulaires. Quelle est l'implication de la voie de signalisation Notch dans la différenciation des lymphocytes CD4+ en sous-types de cellules CD4+T auxiliaires et comment cette voie agit dans ce processus, sont des questions débattues. Les cellules T auxiliaires 17 (Th17) sont une sous-population distincte de lymphocytes CD4+. Elles jouent un rôle important dans la défense immunitaire contre les bactéries extracellulaires et les champignons. Une dérégulation de la réponse des cellules Th17 a été associée à des maladies auto-immunes et à l'inflammation. Les cellules Th17 sont définies par l'expression du facteur de transcription RAR-related orphan receptor (ROR)yT et des cytokines comme IL-17A, IL-17F. Le rôle de la voie de signalisation Notch dans la différenciation des cellules Th17 a été principalement démontré dans des études expérimentales in vitro. Malgré tout, les conditions expérimentales exactes dans lesquelles la voie de signalisation de Notch pourrait être impliquée dans la différenciation des cellules Th17, mais également le rôle de Notch dans ce processus ne sont pas encore clairement élucidés. Afin de mieux définir comment la voie de signalisation Notch est impliquée dans la différenciation des cellules Th17, nous avons utilisé des souris avec une déficience spécifique dans les cellules T des récepteurs Notchl et Notch2 (N1 N2ACD4Cre) ou du répresseur transcriptionnel de Notch RBP-JK (RBP-JACD4CRE). Nous avons montré que lorsque la voie de signalisation Notch est déficiente, les niveaux d'ARN messager (ARNm) de RORyT et de IL-17 sont augmentés dans les cellules Th17 pendant leur différenciation in vitro. Cet effet de Notch sur la transcription apparaît être facultatif lorsque les conditions environnementales sont en excès in vitro. Après immunisation avec un adjuvant qui induit principalement une réponse des cellules Th17, nous avons observé que les niveaux de ARNm de IL-17A et aussi de IL-17A intracellulaire au niveau protéinique étaient augmentés dans les ganglions drainants dans les cellules CD4+ Notch déficientes. Ces résultats suggèrent que Notch pourrait réguler négativement l'expression de IL- 17 au niveau transcriptionnel mais également protéinique. Malgré des niveaux plus élevés de IL- 17 ARNm et aussi IL-17A intracellulaire dans les cellules T Notch déficientes, le relâchement en cytokines Th17 est profondément diminué indiquant un rôle de la voie de signalisation Notch dans ces processus de sécrétion. Dans la deuxième partie de cette thèse, nous avons observé que le rôle de Notch dans ia différenciation dss cellules Ti,17 et dans leurs fonctions effectrices était dépendant du contexte dans d'autres modèles expérimentaux, qui ont été rapportés comme une réponse induisant des cellules Th17. En résumé, nos données montrent que la voie de la signalisation Notch contrôle la régulation précise de la différenciation des cellules Th17 en limitant leur différenciation mais en promouvant sélectivement le relâchement en cytokines associées aux cellules Th17 par des mécanismes dépendant de Notch qui restent toujours à déterminer. Par conséquent, l'inhibition de la voie de signalisation Notch pourrait être utilisée dans des situations inflammatoires ou d'auto-immunité où la réponse des cellules Th17 est exacerbée.

Effects of anxiety on visuospatial pattern discrimination and decision-making processes: what can we learn from brain dynamics ?

Detection and discrimination of visuospatial input involve at least extracting, selecting and encoding relevant information and decision-making processes allowing selecting a response. These two operations are altered, respectively, by attentional mechanisms that change discrimination capacities, and by beliefs concerning the likelihood of uncertain events. Information processing is tuned by the attentional level that acts like a filter on perception, while decision-making processes are weighed by subjective probability of risk. In addition, it has been shown that anxiety could affect the detection of unexpected events through the modification of the level of arousal. Consequently, purpose of this study concerns whether and how decision-making and brain dynamics are affected by anxiety. To investigate these questions, the performance of women with either a high (12) or a low (12) STAI-T (State-Trait Anxiety Inventory, Spielberger, 1983) was examined in a decision-making visuospatial task where subjects have to recognize a target visual pattern from non-target patterns. The target pattern was a schematic image of furniture arranged in such a way as to give the impression of a living room. Non-target patterns were created by either the compression or the dilatation of the distances between objects. Target and non-target patterns were always presented in the same configuration. Preliminary behavioral results show no group difference in reaction time. In addition, visuo-spatial abilities were analyzed trough the signal detection theory for quantifying perceptual decisions in the presence of uncertainty (Green and Swets, 1966). This theory treats detection of a stimulus as a decision-making process determined by the nature of the stimulus and cognitive factors. Astonishingly, no difference in d' (corresponding to the distance between means of the distributions) and c (corresponds to the likelihood ratio) indexes was observed. Comparison of Event-related potentials (ERP) reveals that brain dynamics differ according to anxiety. It shows differences in component latencies, particularly a delay in anxious subjects over posterior electrode sites. However, these differences are compensated during later components by shorter latencies in anxious subjects compared to non-anxious one. These inverted effects seem indicate that the absence of difference in reaction time rely on a compensation of attentional level that tunes cortical activation in anxious subjects, but they have to hammer away to maintain performance.

Data Envelopment Analysis (DEA) : A pedagogical guide for decision makers in the public sector

This guide introduces Data Envelopment Analysis (DEA), a performance measurement technique, in such a way as to be appropriate to decision makers with little or no background in economics and operational research. The use of mathematics is kept to a minimum. This guide therefore adopts a strong practical approach in order to allow decision makers to conduct their own efficiency analysis and to easily interpret results. DEA helps decision makers for the following reasons: - By calculating an efficiency score, it indicates if a firm is efficient or has capacity for improvement. - By setting target values for input and output, it calculates how much input must be decreased or output increased in order to become efficient. - By identifying the nature of returns to scale, it indicates if a firm has to decrease or increase its scale (or size) in order to minimize the average cost. - By identifying a set of benchmarks, it specifies which other firms' processes need to be analysed in order to improve its own practices.

Decision-Making in Social Enterprises: Exploring the Link between Employee Participation and Organizational Commitment

This article studies the influence of the procedural justice resulting from participation in decision-making on employees' affective commitment in social enterprises. It also examines whether any potential link between participation and commitment is due to social exchange, as is the case with for-profit companies. The study is based on data from employees of French work integration social enterprises. The results confirm the positive relationship between procedural justice and affective commitment and the mediating role of perceived organizational support and leader-member exchanges. Managerial recommendations are then given to best maintain or increase employees' involvement in the decision-making processes of social enterprises.

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory? (Commentary on Pothos and Busemeyer)

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

Auditory perceptual decision-making based on semantic categorization of environmental sounds.

Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception were the focus of the present study. We presented 250ms duration sounds of living and man-made objects while recording 160-channel electroencephalography (EEG). Subjects categorized each sound as that of a living, man-made or unknown item. We tested whether/when the brain discriminates between sound categories even when not transpiring behaviorally. We applied a single-trial classifier that identified voltage topographies and latencies at which brain responses are most discriminative. For sounds that the subjects could not categorize, we could successfully decode the semantic category based on differences in voltage topographies during the 116-174ms post-stimulus period. Sounds that were correctly categorized as that of a living or man-made item by the same subjects exhibited two periods of differences in voltage topographies at the single-trial level. Subjects exhibited differential activity before the sound ended (starting at 112ms) and on a separate period at ~270ms post-stimulus onset. Because each of these periods could be used to reliably decode semantic categories, we interpreted the first as being related to an implicit tuning for sound representations and the second as being linked to perceptual decision-making processes. Collectively, our results show that the brain discriminates environmental sounds during early stages and independently of behavioral proficiency and that explicit sound categorization requires a subsequent processing stage.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses.

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging, overlooking any information about the time when decisions are made. Here, we carried out an electroencephalography (EEG) experiment, in order to detect the time when the brain generators responsible for these decisions have been sufficiently activated to lead to the next decision. Our analyses, based on a classification scheme, extract time-unlocked voltage topographies during reward presentation and use them to predict the type of decisions made on the subsequent trial. Classification accuracy, measured as the area under the Receiver Operator's Characteristic curve was on average 0.65 across 7 subjects. Classification accuracy was above chance levels already after 516 ms on average, across subjects. We speculate that decisions were already made before this critical period, as confirmed by a positive correlation with reaction times across subjects. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision-making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and the right supramarginal gyrus; areas responsible for modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis.