Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

This paper proposes a new nested algorithm (NPL) for the estimation of a class of discrete Markov decision models and studies its statistical and computational properties. Our method is based on a representation of the solution of the dynamic programming problem in the space of conditional choice probabilities. When the NPL algorithm is initialized with consistent nonparametric estimates of conditional choice probabilities, successive iterations return a sequence of estimators of the structural parameters which we call K–stage policy iteration estimators. We show that the sequence includes as extreme cases a Hotz–Miller estimator (for K=1) and Rust's nested fixed point estimator (in the limit when K→∞). Furthermore, the asymptotic distribution of all the estimators in the sequence is the same and equal to that of the maximum likelihood estimator. We illustrate the performance of our method with several examples based on Rust's bus replacement model. Monte Carlo experiments reveal a trade–off between finite sample precision and computational cost in the sequence of policy iteration estimators.     

The Effect of Misclassification Costs on Neural Network Classifiers

The potential of neural networks for classification problems has been established by numerous successful applications reported in the literature. One of the major assumptions used in almost all studies is the equal cost consequence of misclassification. With this assumption, minimizing the total number of misclassification errors is the sole objective in developing a neural network classifier. Often this is done simply to ease model development and the selection of classification decision points. However, it is not appropriate for many real situations such as quality assurance, direct marketing, bankruptcy prediction, and medical diagnosis where misclassification costs have unequal consequences for different categories. In this paper, we investigate the issue of unequal misclassification costs in neural network classifiers. Through an application in thyroid disease diagnosis, we find that different cost considerations have significant effects on the classification performance and that appropriate use of cost information can aid in optimal decision making. A cross-validation technique is employed to alleviate the problem of bias in the training set and to examine the robustness of neural network classifiers with regard to sampling variations and cost differences.     

Using Neural Networks to Model the Behavior and Decisions of Gamblers,in Particular,Cyber-Gamblers

This article describes the use of neural networks (a type of artificial intelligence) and an empirical data sample of, inter alia, the amounts of bets laid and the winnings/losses made in successive games by a number of cyber-gamblers to longitudinally model gamblers’ behavior and decisions as to such bet amounts and the temporal trajectory of winnings/losses. The data was collected by videoing Texas Holdem gamblers at a cyber-gambling website. Six “persistent” gamblers were identified, totaling 675 games. The neural networks on average were able to predict bet amounts and cumulative winnings/losses in successive games accurately to three decimal places of the dollar. A more important conclusion is that the influence of a gambler’s skills, strategies, and personality on his/her successive bet amounts and cumulative winnings/losses is almost totally reflected by the pattern(s) of his/her winnings/losses in the few initial games and his/her gambling account balance. This partially invalidates gamblers’ illusions and fallacies that they can outperform others or even bankers. For government policy-makers, gambling industry operators, economists, sociologists, psychiatrists, and psychologists, this article provides models for gamblers’ behavior and decisions. It also explores and exemplifies the usefulness of neural networks and artificial intelligence at large in the research on gambling.     

Designing Lateral Organizations: An Analysis of the Benefits,Costs, and Enablers of Nonhierarchical Organizational Forms*

Twenty-first century organizations will require designs that enable them to cope with turbulent environments. Organizations have experimented with lateral organizational designs for this purpose, but research evidence concerning these forms is sparse. We analyzed data obtained from 512 employees within eight diverse organizations implementing flexible lateral organizations. Using a sequence comparison methodology, we were able to identify and categorize the major costs, benefits, and enablers associated with implementing these forms of complex organizations. Propositions for effectively managing lateral relations were tested and managerial implications were explored.     

Testing for Granger-causality in quantiles

This paper proposes a consistent parametric test of Granger-causality in quantiles. Although the concept of Granger-causality is defined in terms of the conditional distribution, most articles have tested Granger-causality using conditional mean regression models in which the causal relations are linear. Rather than focusing on a single part of the conditional distribution, we develop a test that evaluates nonlinear causalities and possible causal relations in all conditional quantiles, which provides a sufficient condition for Granger-causality when all quantiles are considered. The proposed test statistic has correct asymptotic size, is consistent against fixed alternatives, and has power against Pitman deviations from the null hypothesis. As the proposed test statistic is asymptotically nonpivotal, we tabulate critical values via a subsampling approach. We present Monte Carlo evidence and an application considering the causal relation between the gold price, the USD/GBP exchange rate, and the oil price.     

The Glocal and Global Studies

Abstract

This article offers an overview of engagements with the glocal and traces the consequences of this research agenda for Global Studies. First, it compares the emergence of the global and the glocal in the literature. It tracks the uneven impact of the business use of the glocal and argues that this genealogy has obscured alternative accounts. Second, it offers a thematic overview of the uses of glocalization in the literature. It highlights publication clusters in specific areas of interdisciplinary interest. It further addresses key criticisms against glocalization. Finally, it explores the vicissitudes of research on glocalization on the scope and definition of Global Studies. Trends in Global Studies appear to move in an opposite direction from the one suggested by glocalization. This could lead to the emergence of a separate field of Glocal Studies, causing further fragmentation in the field.     

Nicholas II — 1868-1918

Recent changes in accreditation standards now require professors to systematically assess whether a teacher candidate actually possesses a “disposition” appropriate to the profession. This new mandate from accreditation bodies is controversial on a number of fronts. As social studies educators in particular, we like to think of ourselves as especially attentive to issues such as individual differences and rights, indoctrination, prejudice, intolerance, freedom of expression, maturation, and the worth of the individual—all of which are potentially raised by the NCATE and NCSS mandates. And as a practical matter, it places new stresses on the role of professors as both mentors and gatekeepers. In this article, two social studies teacher educators provide an overview of current theory and debate regarding dispositions, an assessment of practical problems associated with the accreditation accountability mandates, and—in spite of the persistent problems associated with defining and assessing dispositions—offer one successful model for evaluating teacher candidates’ professional dispositions that has been implemented in a social studies education program.     

Marital Quality,Context, and Interaction: A Comparison of Those Currently Receiving Government Assistance with Those Who Are Not

In this study the marital quality of respondents who were currently receiving government assistance was compared with those who were not. Contextual variables (e.g., gender, age, age at first marriage, religiosity, education, etc.) and interactional variables (i.e., escalating negativity, criticism, negative interpretation, withdrawal) were measured as potential correlates with marital quality. Results indicated that those who received government assistance differed significantly from those who did not on all six indicators of marital quality that were measured and on 8 of 11 contextual variables measured. Findings from this study will help policymakers, therapists, and other helping professionals gain an increased awareness of the needs of these two distinct populations and how to target educational programs to best address those needs.