Bill of Exchange as a securing instrument (Summary) This thesis pertains to the securing bill of exchange, both on theoretical and practical levels. It deals with its particular characteristics which effectively distinguish a securing bill of exchange from bills of exchange fulfilling distinct functions, as well as from other securing instruments. The thesis provides its readers with a comprehensive view of the advantages the securing bill of exchange has to offer within a variety of private legal relations. Simultaneously, it warns against the perils which are inherent thereto, both with respect to the creditors and the debtors. The thesis thereby emphasizes a necessity for at least a certain level of knowledge concerning the nature and potential of the securing bill of exchange, which would in practice help prevent misuse of this instrument and inflicting damage on the debtors. The first chapter focuses on the causal obligation of a debtor and a creditor and its connection to the bill of exchange aiming to secure that relation. Together with the second chapter, these two provide for the fundamental nature and specific features of the securing bill of exchange. The third chapter pertains to the contractual basis for securing the receivables, and its crucial importance as it involves various contractual...

The quantitative study of conflict management is concerned with finding models which are accurate and also capable of providing a causal interpretation of results. This dissertation applies computational intelligence methods to study interstate disputes. Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy models are used to model interstate interactions. The multilayer perceptron neural network is trained in the Bayesian framework, using the Hybrid Monte Carlo method to sample from the posterior probabilities. It is found that the network is able to forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using the neural network and the genetic algorithm is then presented as a method of suggesting how conflict can be brought under control. The automatic relevance determination approach and the sensitivity analysis are used as methods of extracting causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model is optimised, using the Gustafson-Kessel clustering algorithm to partion the input space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of 80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning method to suggest how the identified conflict cases can be avoided. The casual interpretation is then formulated by a linguistic approximation of the fuzzy rules extracted from the neuro-fuzzy model. The major finding in this work is that the interpretations drawn from both the neural network and the neuro-fuzzy model are consistent.

In this thesis several non-causal offline algorithms are developed and evaluated for a vision system used for pedestrian and vehicle traffic. The reason was to investigate if the performance increase of non-causal offline algorithms alone is enough to evaluate the performance of vision system. In recent years the vision systems have become one of the most important sensors for modern vehicles active security systems. The active security systems are becoming more important today and for them to work a good object detection and tracking in the vicinity of the vehicle is needed. Thus, the vision system needs to be properly evaluated. The problem is that modern evaluation techniques are limited to a few object scenarios and thus a more versatile evaluation technique is desired for the vision system. The focus of this thesis is to research non-causal offline techniques that increases the tracking performance without increasing the number of sensors. The Unscented Kalman Filter is used for state estimation and an unscented Rauch-Tung-Striebel smoother is used to propagate information backwards in time. Different motion models such as a constant velocity and coordinated turn are evaluated. Further assumptions and techniques such as tracking vehicles using fix width and estimating topography and using it as a measurement are evaluated. Evaluation shows that errors in velocity and the uncertainty of all the states are significantly reduced using an unscented Rauch-Tung-Striebel smoother. For the evaluated scenarios it can be concluded that the choice of motion model depends on scenarios and the motion of the tracked vehicle but are roughly the same. Further the results show that assuming fix width of a vehicle do not work and measurements using non-causal estimation of topography can significantly reduce the error in position, but further studies are recommended to verify this.

Thesis advisor: Richard Cobb-Stevens / This essay addresses the problems posed by proper names in regards to their relationship to that which they name (their "reference"). This relation of a name to its reference serves as a model for how language in general relates to the world as it actually is, making the question of what a name names of vital epistemological importance. The main problem posed by proper names is whether they simply denote, or whether they connote (i.e. have some informational content), and if they connote, what picture should we give of that content? This essay critiques a variety of theories about proper names, including those of Frege, Russell, Kripke, Searle, Evans, and Sainsbury. It concludes that names of unique entities do have informational content, in the form of a yet unspecified family of definite descriptions that rigidly designate an individual, which arises out of various causal chains of communication in a community. / Thesis (BA) — Boston College, 2007. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Philosophy. / Discipline: College Honors Program.

While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey's Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students' affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience.

Educational games intend to make learning more enjoyable, but at the potential cost of compromising learning efficiency. Therefore, instead of creating educational games, we create learning environment with game-like elements: the elements of games that are engaging. Our approach is to assess each game-like element in terms of benefits such as enhancing engagement as well as its costs such as sensory or working memory overload, with a goal of maximizing both engagement and learning. We developed different four versions of a math tutor with different degree of being game-like such as adding narrative and visual feedback. Based on a study with 297 students, we found that students reported more satisfaction with more 'game-like' tutor but we were not able to detect any conclusive difference in learning among the different tutors. We collected student data of various types such as their attitude and enjoyment via surveys, performance within tutor via logging, and learning as measured by a pre/post-test. We created a causal model using software TETRAD and contrast the causal modeling approach to the results we achieve with traditional approaches such as correlation matrix and multiple regression. Relative to traditional approaches, we found that causal modeling did a better job at detecting and representing spurious association, and direct and indirect effects within variables. Causal model, augmented with domain knowledge about likely causal relationships, resulted in much more plausible and interpretable model. We propose a framework for blending exploratory results from causal modeling with randomized controlled studies to validate hypotheses.

For several decades, causal decision theory (CDT) has been the orthodox version of philosophical decision theory. However, ever since CDT was first developed there have been those who have disputed the adequacy of this theory. Then, in the last decade and a half, opposition to this theory has intensified, with a vast array of novel objections to CDT emerging. As a result, the field of philosophical decision theory has splintered, with a large number of new versions of decision theory being developed to try to plug the gap left by the apparent collapse of CDT. However, in this thesis I will defend CDT against the objections raised against it and so dispute the need to develop a new version of decision theory. In doing so, I will address old challenges to CDT, based around Newcomb’s Problem and cases where CDT provides unstable guidance. These challenges have been around for some time. While existing solutions have been presented here, these have failed to fully resolve the disquiet that these objections raise. In this thesis, I will have more to say to resolve this disquiet so that we can set these old objections aside. In this thesis, I will also address new challenges to CDT, which have arisen in the past decade and a half. These challenges are based on appeals to quantum mechanics, prophecy, and the laws of nature, among other things. Many of these objections have not previously been addressed. However, I will argue that these challenges fail to appropriately construe CDT and so fail to truly undermine this theory. Causal decision theory, I will conclude, is a robust theory. As such, while there is much work to be done in philosophical decision theory this work involves building on, rather than replacing, CDT.

Background: Interventions for young children with autism are increasingly delivered through parents. This thesis investigated baseline and process variables associating with quality of parent-therapist alliance in a parent-mediated intervention for autism, including variables relating to parents' causal beliefs and perspectives. Participants: 77 parents and 6 therapists from a RCT of a parent-mediated intervention for autism (PACT). Method: A sequential exploratory mixed methods approach, with an intermediary instrument development phase. Baseline variables, investigated for the full sample of 77 cases, comprised demographic variables, parental dichotomous causal belief variables, and a therapist average fidelity variable. Thematic analysis of intervention session transcripts informed the development of the Parental Perspectives Coding Scheme (PPCS), a video-based scheme for rating the quality of parent 'Expression' and therapist 'Integration' of parental perspectives during intervention. 5 overarching themes were initially identified, these were collapsed into three items for the PPCS; Interpretation of the Child (IOC), Parent Actions and Strategies (PAS) and Parental Self Disclosures (PSD). Item inter-rater reliabilities were satisfactory to good. Parent-therapist dialogue, for a sub-sample of 20 cases, was coded using the PPCS to create process variables for the Expression and Integration of parental perspectives. Initial analyses identified baseline and process variables with significant univariate associations with alliance; these were included in separate multivariate models of parent-rated alliance and therapist-rated alliance. Results: Parent-rated and therapist-rated alliance did not correlate. PPCS Expression and Integration scores were higher in the high parent-rated alliance group but the difference was non-significant. Parents who cited MMR as a possible cause of their child's autism rated the alliance significantly lower than those who did not. Parents with no post-16 qualifications rated the alliance significantly higher than those with higher qualifications. Each factor contributed independently to a multiple regression model, together explaining 18.3% of variance in parent-rated alliance. Therapist-rated alliance significantly correlated positively with therapist fidelity and with PPCS variables for parent Expression and therapist Integration; together these explained 58.8% of variance in therapist-rated alliance. Conclusions: Therapists should be aware that parents may rate the alliance differently from themselves and that different factors associate with their ratings. Parents' causal beliefs and level of education may influence their ratings of alliance in specific interventions.

Basándonos en las ideas de que la educación es la base y el motor fundamental de desarrollo de un país y de que las metodologías que estamos utilizando para ello no son precisamente las mejores, nuestra investigación intenta averiguar qué variables, de entre un conjunto de ellas, son las que determinan/predicen el rendimiento académico y construir un modelo causal que nos aporte un conocimiento más analítico. De la revisión teórica efectuada se desprende que todas las variables consideradas en el estudio -género, estatus, inteligencia, motivación, atribuciones, autoconcepto y personalidad-se relacionan entre sí y con el rendimiento académico de una u otra forma. Por tanto el objetivo general de este trabajo es el de establecer el rol que juegan esas variables y de qué forma en la explicación del rendimiento académico de nuestros alumnos. Este objetivo general lo desglosamos en cuatro objetivos específicos: a) determinar las diferencias, si existiesen, entre los alumnos de alto y bajo rendimiento académico en relación a todas las variables consideradas, b) establecer qué variables, si es que existiesen, son predictoras del rendimiento académico y el peso que tiene cada una de ellas, c) establecer un modelo explicativo de las interrelaciones que se producen entre ellas y con el rendimiento académico, d) establecer conclusiones que nos permitan actuar desde la realidad escolar sobre aquellas variables que influyen en el rendimiento académico. Los resultados obtenidos nos permiten establecer un modelo causal explicativo del rendimiento académico en el que 3 variables explican el 78% de la varianza de la variable dependiente (rendimiento académico), estas variables son: el estatus sociométrico de los alumnos y las alumnas, la ansiedad facilitadora del rendimiento y el autoconcepto como trabajador de los participantes. Se discute la conveniencia de que en el trabajo en el aula se considere de forma prioritaria favorecer, mejorar, estas variables.

In this study an interpretive learning framework that aims to measure learning on the classroom level is introduced. In order to develop and evaluate the value of the framework, a theoretical/empirical study is designed. The researcher attempted to illustrate how the proposed framework provides insights on the problem of classroom-level learning. The framework is developed by construction of connections between the current literature on science learning and Wittgenstein's language-game theory. In this framework learning is defined as change of classroom language-game or discourse. In the proposed framework, learning is measured by analysis of classroom discourse. The empirical explanation power of the framework is evaluated by applying the framework in the analysis of learning in a fifth-grade science classroom. The researcher attempted to analyze how students' colloquial discourse changed to a discourse that bears more resemblance to science discourse. The results of the empirical part of the investigation are presented in three parts: first, the gap between what students did and what they were supposed to do was reported. The gap showed that students during the classroom inquiry wanted to do simple comparisons by direct observation, while they were supposed to do tool-assisted observation and procedural manipulation for a complete comparison. Second, it was illustrated that the first attempt to connect the colloquial to science discourse was done by what was immediately intelligible for students and then the teacher negotiated with students in order to help them to connect the old to the new language-game more purposefully. The researcher suggested that these two events in the science classroom are critical in discourse change. Third, it was illustrated that through the academic year, the way that students did the act of comparison was improved and by the end of the year more accurate causal inferences were observable in classroom communication. At the end of the study, the researcher illustrates that the application of the proposed framework resulted in an improved version of the framework. The improved version of the proposed framework is more connected to the topic of science learning, and is able to measure the change of discourse in higher resolution.