Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 2001. / Includes bibliographical references. Also available on the Internet.

This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed in the machine learning field. The forecasting performance of these models will be compared to the Random Walk, which is the benchmark model for financial returns, and the popular autoregressive process. The machine learning models that will be used are Regression trees, Random Forests, Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Additive Regression trees (BART). A characterizing feature of financial returns data is the presence of volatility clustering, i.e. the tendency of persistent periods of low or high variance in the time series. This is in disagreement with the machine learning models which implicitly assume a constant variance. We therefore extend these models with the most widely used model for volatility clustering, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process. This allows us to jointly estimate the time varying variance and the parameters of the machine learning using an iterative procedure. These GARCH-extended machine learning models are then applied to make one-step-ahead prediction by recursive estimation that the parameters estimated by this model are also updated with the new information. In order to predict returns, information related to the economic variables and the lagged variable will be used. This study is repeated on three different exchange rate returns: EUR/SEK, EUR/USD and USD/SEK in order to obtain robust results. Our result shows that machine learning models are capable of forecasting exchange returns both on daily and monthly frequency. The results were mixed, however. Overall, it was GARCH-extended SVR that shows great potential for improving the predictive performance of the forecasting of exchange rate returns.

The intricate dynamics and tensions of social histories--the realities of adversity-and anticipations of African American women greatly inhibit many of them from reaching their potential. Despite the adverse experiences, African American women have succeeded in achieving socioeconomic upward mobility. Their ability to defeat the odds has drawn attention to their patterns of adaptation and the process by which transformative experiences evolve. There is a strong need for qualitative research focusing exclusively on the early learning experiences of African American women and the transformative learning process; since studies of this topic are limited and most of them relate to a particular characteristic and its development. The purpose of this study was to examine the transformative learning processes of African American females who, despite their lower class origins, transcended the negative social and economic forces inherent in their backgrounds, thus moving beyond the status of their parents. Mezirow's transformative learning model, perspective transformation, was the conceptual framework guiding this inquiry. The research questions for this study were: 1. How has a small selected group of African American women from lower socioeconomic backgrounds been able to break the particular poverty cycle that their parents endured to become upwardly mobile achievers? 2. What transformative learning process did they engage to overcome specific obstacles in order to attain a higher level of socioeconomic mobility? 3. To what extent are the steps of perspective transformation descriptive of the process as experienced by the women in this study? A multiple-case study design was selected to accomplish the objective of the research. Participants were recruited through informal requests and referrals. Eight women were selected from an initial pool of twelve potential participants. The data were gathered through in-depth personal interviews and analyzed using Ethnograph coding software. Data were presented in descriptive narrative case study profiles. Four categories of major themes were identified as common among the participants: (a) a value laden upbringing, (b) productive self perception, (c) influences of others, and (d) significant mobility experiences. Findings revealed only a partial experience of transformational learning from these women. A strong maternal influence led to the indoctrination of lifelong values and beliefs consistent with a process in which mothers and grandmothers had begun but were unable to complete. This intergenerational transformative learning process passed down to the next generation, in this study. Results revealed upward socioeconomic mobility and a decline of the poverty cycle. Recommendations for educators and future studies were addressed. / Ph. D.

Evolutionary game theory tries to explain the emergence of stable behaviors observed in human and animal societies. Prominent examples of such behaviors are cooperative and conformist behaviors. In the first part of the thesis, we develop a model of indirect reciprocity with institutional screening to study how institutions may promote cooperative behavior. We show that cooperation can emerge if screening institutions are sufficiently reliable at identifying cooperators. The second part presents a large-population learning model in which individuals update their beliefs through time. In the model, only one individual updates his beliefs each period. We show that a population, playing a game with two strategies, eventually learns to play a Nash equilibrium. We focus on coordination games and prove that a unique behavior arises both when players use myopic and perturbed best replies. The third part studies the payoff calculation in an evolutionary setting. By introducing mutual consent as a requirement for game play, we provide a more realistic alternative way to compute payoffs. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>

This thesis investigates to what extent do cognitive biases a ect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, e ects) are analysed in detail, including identi cation of possibly e ective debiasing techniques that can be adopted by designers of machine learning algorithms and software. This qualitative research is complemented by multiple experiments aimed to verify, whether, and to what extent, do selected cognitive biases in uence human understanding of actual rule learning results. Two experiments were performed, one focused on eliciting plausibility judgments for pairs of inductively learned rules, second experiment involved replication of the Linda experiment with crowdsourcing and two of its modi cations. Altogether nearly 3.000 human judgments were collected. We obtained empirical evidence for the insensitivity to sample size e ect. There is also limited evidence for the disjunction fallacy, misunderstanding of and , weak evidence e ect and availability heuristic. While there seems no universal approach for eliminating all the identi ed cognitive biases, it follows from our analysis that the e ect of many biases can be ameliorated by making rule-based models more concise. To this end, in the second part of thesis we propose a novel machine learning framework which postprocesses rules on the output of the seminal association rule classi cation algorithm CBA [Liu et al, 1998]. The framework uses original undiscretized numerical attributes to optimize the discovered association rules, re ning the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classi er smaller. Benchmark of our approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.

This thesis presents a biologically inspired model of learning and development. This model decomposes the lifetime of a single learning system into a number of stages, analogous to the infant, juvenile, adolescent and adult stages of development in a biological system. This model is then applied to Kohonen's SOM algorithm.In order to better understand the operation of Kohonen's SOM algorithm, a theoretical analysis of self-organisation is performed. This analysis establishes the role played by lateral connections in organisation, and the significance of the Laplacian lateral connections common to many SOM architectures.This analysis of neighbourhood interactions is then used to develop three key variations on Kohonen's SOM algorithm. Firstly, a new scheme for parameter decay, known as Butterworth Step Decay, is presented. This decay scheme provides training times comparable to the best training times possible using traditional linear decay, but precludes the need for a priori knowledge of likely training times. In addition, this decay scheme allows Kohonen's SOM to learn in a continuous manner.Secondly, a method is presented for establishing core knowledge in the fundamental representation of a SOM. This technique is known as Syllabus Presentation. This technique involves using a selected training syllabus to reinforce knowledge known to be significant. A method for developing a training syllabus, known as Percept Masking, is also presented.Thirdly, a method is presented for preventing the loss of trained representations in a continuously learning SOM. This technique, known as Arbor Pruning, involves restricting the weight update process to prevent the loss of significant representations. This technique can be used if the data domain varies within a known set of dimensions. However, it cannot be used to control forgetfulness if dimensions are added to or removed from ++ / the data domain.

Basic interpretive qualitative research design (Merriam, 2002) was used to explore the experiences and events that Aboriginal students reported during the transition from a rural to an urban setting and attend post-secondary school. Three participants, who were both Aboriginal and successful in completion of their first year of post-secondary education, were interviewed. Data was analyzed and five common themes emerged that contributed to their success. These were academic, family and community, culture, financial and social. These findings are discussed in relation to the current research in the area of Aboriginal education, including the First Nations and Métis Lifelong Learning Models. As well, recommendations and implication for future practice are included.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains x, 127 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 123-127).

Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Computer Science." Includes bibliographical references (p. 133-136).

Magister Educationis (Adult Learning and Global Change) - MEd(AL) / Scholars focusing on experiential learning argue that experience should be considered as critical for adult learning. This research paper frames experiential learning within a Constructivist framework. This paper focuses on an investigation into the ways that facilitators use the Continuous Learning Cycle, a model for learning based on Kolb's Learning Cycle, to facilitate learning through experience during the triad skills observation role-play in a workshop, which is part of an induction programme, for a retail bank. Indications are that facilitators use the Continuous Learning Cycle in limited ways, and therefore undermine the possibilities for optimal experiential learning; and that the Continuous Learning Cycle has limitations.