Arrow-Debreu-modell és a Kornai-kritika harminc év után = The Arrow-Debreu Model and Kornai s critique, thirty years after

Több mint harminc év telt el Kornai János Anti-equilibrium című könyvének megjelenése óta. Ez volt az első mű a nemzetközi irodalomban, amely átfogóan bírálta az általános egyensúlyelméletet, mégpedig Debreu értékelméletén és az Arrow-Debreu modellen keresztül. A kritikára legélesebben Frank H. Hahn reagált, amire Kornai - fenntartva korábbi bírálatainak többségét - a közelmúltban megjelent önéletrajzában tért vissza. E cikkben elmélettörténeti előzményekkel együtt rekonstruáljuk a Kornai-Hahn-vita főbb pontjait, és megvizsgáljuk a kritikák és riposztok érvényességét. Látni fogjuk, hogy a legújabb közgazdasági elméletek nem mindenben igazolták Hahn ellenvetéseit. _______________ More than thirty years have passed since János Kornai s book Anti-Equilibrium appeared. This was the first work in international literature to criticize comprehensively the general theory of equilibrium, moreover through the value theory of Debreu and the Arrow-Debreu Model. The sharpest reaction to the criticism came from Frank H. Hahn, and Kornai returned to this in his recent autobiography. This article reconstructs the main points in the Kornai-Hahn debate, including its antecedents in the history of theory, and examines the validity of the criticisms and ripostes. It will be seen that Hahn s objections have not been endorsed in every respect by the latest economic theories.

Regional flood frequency analysis for Newfoundland and Labrador using the L-Moments index-flood method

The L-moments based index-flood procedure had been successfully applied for Regional Flood Frequency Analysis (RFFA) for the Island of Newfoundland in 2002 using data up to 1998. This thesis, however, considered both Labrador and the Island of Newfoundland using the L-Moments index-flood method with flood data up to 2013. For Labrador, the homogeneity test showed that Labrador can be treated as a single homogeneous region and the generalized extreme value (GEV) was found to be more robust than any other frequency distributions. The drainage area (DA) is the only significant variable for estimating the index-flood at ungauged sites in Labrador. In previous studies, the Island of Newfoundland has been considered as four homogeneous regions (A,B,C and D) as well as two Water Survey of Canada's Y and Z sub-regions. Homogeneous regions based on Y and Z was found to provide more accurate quantile estimates than those based on four homogeneous regions. Goodness-of-fit test results showed that the generalized extreme value (GEV) distribution is most suitable for the sub-regions; however, the three-parameter lognormal (LN3) gave a better performance in terms of robustness. The best fitting regional frequency distribution from 2002 has now been updated with the latest flood data, but quantile estimates with the new data were not very different from the previous study. Overall, in terms of quantile estimation, in both Labrador and the Island of Newfoundland, the index-flood procedure based on L-moments is highly recommended as it provided consistent and more accurate result than other techniques such as the regression on quantile technique that is currently used by the government.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

On the statistics of transverse permeability of randomly distributed fibers

An RVE–based stochastic numerical model is used to calculate the permeability of randomly generated porous media at different values of the fiber volume fraction for the case of transverse flow in a unidirectional ply. Analysis of the numerical results shows that the permeability is not normally distributed. With the aim of proposing a new understanding on this particular topic, permeability data are fitted using both a mixture model and a unimodal distribution. Our findings suggest that permeability can be fitted well using a mixture model based on the lognormal and power law distributions. In case of a unimodal distribution, it is found, using the maximum-likelihood estimation method (MLE), that the generalized extreme value (GEV) distribution represents the best fit. Finally, an expression of the permeability as a function of the fiber volume fraction based on the GEV distribution is discussed in light of the previous results.

Analyse multidimensionnelle de la persévérance aux études universitaires de premier cycle

L’abandon des études universitaires a attiré l’attention de plusieurs chercheurs. Toutefois, il est difficile de saisir la persévérance dans sa globalité à cause de sa complexité et le nombre important des facteurs associés. La persévérance aux études est liée aux facteurs individuels, aux facteurs contextuels et à la situation financière au cours des études. Ces facteurs ont été étudiés séparément et d’une manière isolée, et aucune étude n’a, à notre connaissance, tenté de mettre ces facteurs simultanément dans un même modèle. Dans cette thèse, nous identifions les principaux déterminants de la persévérance, tout en nous appuyant sur le modèle des attentes et des valeurs (Eccles et al., 1983), le modèle interactionnel de Tinto (1975) et les modèles d’impact financier (Paulsen & St. John, 1997; St. John, 1990; St. John et al., 1994). Cette thèse a pour objectif de valider un modèle de persévérance aux études universitaires de premier cycle. Celle-ci comporte deux études. Une étude rétrospective qui permet d’évaluer, à partir de l’expérience antérieure des étudiants (n = 731), les principaux facteurs qui ont joué un rôle sur le plan de la persévérance ou de l’abandon des études. Une étude prospective suivant sur une période de six mois (deux temps de mesure) des étudiants inscrits dans un programme de baccalauréat à l’Université Laval (n = 3 084). Pour les résultats de l’étude rétrospective, la situation financière, les performances scolaires antérieures et le fait d’avoir effectué des études préuniversitaires au Cégep prédisent la persévérance. Pour le premier temps de mesure de l’étude prospective, la perception de compétence, les attentes de succès et l’intérêt prédisent l’intention de persévérer. Deux facteurs interactionnels prédisent l’intention de persévérer à savoir : les interactions avec les pairs et l’engagement institutionnel et universitaire. En ce qui concerne le deuxième temps de mesure de l’étude prospective, l’intention de persévérer, la préoccupation de la Faculté par rapport à l’enseignement et au développement des étudiants, le développement intellectuel et académique ainsi que le fait d’avoir fait des études préuniversitaires au Cégep prédisent la persévérance. Les implications théoriques, méthodologiques et pratiques sont abordées et des pistes de recherches futures sont proposées.

Der Zusammenhang der Theorie des geplanten Verhaltens mit der selbstberichteten Individualisierungspraxis von Lehrpersonen

Einstellungen stellen als Teil der professionellen Handlungskompetenz von Lehrpersonen eine wichtige handlungsleitende Determinante des Unterrichtsgeschehens dar. Hierzu wird auf Basis der Theorie des geplanten Verhaltens ein Erwartungs-mal-Wert-theoretisches Einstellungsinstrument faktoriell validiert und der Zusammenhang der inklusiven Einstellung, Normvorstellung und Lehrerselbstwirksamkeitsüberzeugung mit der selbstberichteten Individualisierungspraxis von Lehrpersonen betrachtet. Die Ergebnisse einer exploratorischen Faktorenanalyse zeigen in Studie I drei Einstellungsfaktoren, die in Studie II konfirmatorisch bestätigt wurden. Studie III zeigt, dass die selbstberichtete Individualisierungspraxis durch die Normvorstellung und die Intention, sich den Herausforderungen eines inklusiven Unterrichtes anzunehmen, vorhergesagt werden kann. Die Intention mediiert dabei den Zusammenhang der selbstberichteten Individualisierungspraxis mit der Einstellung vollständig und mit der Normvorstellung partiell. Die Lehrerselbstwirksamkeitsüberzeugung sagt demgegenüber die selbstberichtete Individualisierungspraxis weder direkt noch indirekt vorher. (DIPF/Orig.)

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

STUDENTS’ PERCEPTIONS OF TEACHER INSTRUMENTAL SUPPORT AS A PREDICTOR OF MOTIVATION IN READING AND MATH AND ACHIEVEMENT FROM SECOND THROUGH TWELFTH GRADE

In this study, relations among students’ perceptions of instrumental help/support from their teachers and their reading and math ability beliefs, subjective task values, and academic grades, were explored from elementary through high school. These relations were examined in an overall sample of 1,062 students from the Childhood and Beyond (CAB) study dataset, a cohort-sequential study that followed students from elementary to high school and beyond. Multi-group structural equation model (SEM) analyses were used to explore these relations in adjacent grade pairs (e.g., second grade to third grade) in elementary school and from middle school through high school separately for males and females. In addition, multi-group latent growth curve (LGC) analyses were used to explore the associations among change in the variables of interest from middle school through high school separately for males and females. The results showed that students’ perceptions of instrumental help from teachers significantly positively predicted: (a) students’ math ability beliefs and reading and math task values in elementary school within the same grade for both girls and boys, and (b) students’ reading and math ability beliefs, reading and math task values, and GPA in middle and high school within the same grade for both girls and boys. Overall, students’ perceptions of instrumental help from teachers more consistently predicted ability beliefs and task values in the academic domain of math than in the academic domain of reading. Although there were some statistically significant differences in the models for girls and boys, the direction and strength of the relations in the models were generally similar for both girls and boys. The implications for these findings and suggestions for future research are discussed.

O capital fictício e crise econômica : visões para um novo paradigma

Este artigo tem o propósito inicial de conceituar o que é capital fictício e questionar seu papel na narrativa econômica ortodoxa. Outro objetivo é demonstrar os efeitos prejudiciais deste capital no Brasil. O capital fictício é toda rentabilidade proveniente da valoração derivada e artificial de um capital aplicado, já sem atuação na produção. Sua presença massiva sinaliza uma contradição interna no capitalismo atual, ainda que o pensamento convencional continue a se basear na crença de que crises econômicas são decorrentes de eventos exógenos e inesperados. A crise provém não de eventos aleatórios ou ao acaso, mas da dissociação entre circulação e produção, o que possibilita seu surgimento. Deste modo, a primeira parte deste artigo analisa a teoria do valor e do capital fictício como expressa por Marx e a heterodoxia; a segunda parte busca elucidar suas principais implicações para o caso brasileiro, e questiona a fé irrevogável que a ortodoxia tem na narrativa de crises supostamente surgidas de modelos de equilíbrio. _________________________________________________________________________________ ABSTRACT

New insights: animal-borne cameras and accelerometers reveal the secret lives of cryptic species

Logging cameras and accelerometers have opened our eyes to the secret lives of many enigmatic species. Here some of the new opportunities provided by this technology are reviewed. Recent discoveries are highlighted including the observation of selective feeding on energy-rich parts of prey. As such, biologging cameras provide new opportunities for consideration of selective feeding within the same sort of theoretical framework (marginal value theory/optimal foraging) that exploitation of prey patches has been examined. A recent study with the world's largest bony fish, the ocean sunfish (Mola mola), is highlighted where animal-borne cameras allowed the ground-truthing of data sets collected with depth recorders and accelerometers. This synergistic use of a range of biologging approaches will help drive an holistic understanding of the free-living behaviour of a range of species.

Controlling chaos? The value and the challenges of applying complexity theory to project management

The literature abounds with descriptions of failures in high-profile projects and a range of initiatives has been generated to enhance project management practice (e.g., Morris, 2006). Estimating from our own research, there are scores of other project failures that are unrecorded. Many of these failures can be explained using existing project management theory; poor risk management, inaccurate estimating, cultures of optimism dominating decision making, stakeholder mismanagement, inadequate timeframes, and so on. Nevertheless, in spite of extensive discussion and analysis of failures and attention to the presumed causes of failure, projects continue to fail in unexpected ways. In the 1990s, three U.S. state departments of motor vehicles (DMV) cancelled major projects due to time and cost overruns and inability to meet project goals (IT-Cortex, 2010). The California DMV failed to revitalize their drivers’ license and registration application process after spending $45 million. The Oregon DMV cancelled their five year, $50 million project to automate their manual, paper-based operation after three years when the estimates grew to $123 million; its duration stretched to eight years or more and the prototype was a complete failure. In 1997, the Washington state DMV cancelled their license application mitigation project because it would have been too big and obsolete by the time it was estimated to be finished. There are countless similar examples of projects that have been abandoned or that have not delivered the requirements.

Degree of value alignment : a grounded theory of rural nurse resignations

INTRODUCTION: The shortage of nurses willing to work in rural Australian healthcare settings continues to worsen. Australian rural areas have a lower retention rate of nurses than metropolitan counterparts, with more remote communities experiencing an even higher turnover of nursing staff. When retention rates are lower, patient outcomes are known to be poorer. This article reports a study that sought to explore the reasons why registered nurses resign from rural hospitals in the state of New South Wales, Australia. METHODS: Using grounded theory methods, this study explored the reasons why registered nurses resigned from New South Wales rural hospitals. Data were collected from 12 participants using semi-structured interviews; each participant was a registered nurse who had resigned from a rural hospital. Nurses who had resigned due to retirement, relocation or maternity leave were excluded. Interviews were transcribed verbatim and imported into NVivo software. The constant comparative method of data collection and analysis was followed until a core category emerged. RESULTS: Nurses resigned from rural hospitals when their personal value of how nursing should occur conflicted with the hospital's organisational values driving the practice of nursing. These conflicting values led to a change in the degree of value alignment between the nurse and hospital. The degree of value alignment occurred in three dynamic stages that nurses moved through prior to resigning. The first stage, sharing values, was a time when a nurse and a hospital shared similar values. The second stage was conceding values where, due to perceived changes in a hospital's values, a nurse felt that patient care became compromised and this led to a divergence of values. The final stage was resigning, a stage where a nurse 'gave up' as they felt that their professional integrity was severely compromised. The findings revealed that when a nurse and organisational values were not aligned, conflict was created for a nurse about how they could perform nursing that aligned with their internalised professional values and integrity. Resignation occurred when nurses were unable to realign their personal values to changed organisational values - the organisational values changed due to rural area health service restructures, centralisation of budgets and resources, cumbersome hierarchies and management structures that inhibited communication and decision making, out-dated and ineffective operating systems, insufficient and inexperienced staff, bullying, and a lack of connectedness and shared vision. CONCLUSIONS: To fully comprehend rural nurse resignations, this study identified three stages that nurses move through prior to resignation. Effective retention strategies for the nursing workforce should address contributors to a decrease in value alignment and work towards encouraging the coalescence of nurses' and hospitals' values. It is imperative that strategies enable nurses to provide high quality patient care and promote a sense of connectedness and a shared vision between nurse and hospital. Senior managers need to have clear ways to articulate and imbue organisational values and be explicit in how these values accommodate nurses' values. Ward-level nurse managers have a significant responsibility to ensure that a hospital's values (both explicit and implicit) are incorporated into ward culture.

Examining the predictive value of combining the theory of planned behaviour and the volunteer functions inventory

The functions of the volunteer functions inventory were combined with the constructs of the theory of planned behaviour (i.e., attitudes, subjective norms, and perceived behavioural control) to establish whether a stronger, single explanatory model prevailed. Undertaken in the context of episodic, skilled volunteering by individuals who were retired or approaching retirement (N = 186), the research advances on prior studies which either examined the predictive capacity of each model independently or compared their explanatory value. Using hierarchical regression analysis, the functions of the volunteer functions inventory (when controlling for demographic variables) explained an additional 7.0% of variability in individuals’ willingness to volunteer over and above that accounted for by the theory of planned behaviour. Significant predictors in the final model included attitudes, subjective norms and perceived behavioural control from the theory of planned behaviour and the understanding function from the volunteer functions inventory. It is proposed that the items comprising the understanding function may represent a deeper psychological construct (e.g., self-actualisation) not accounted for by the theory of planned behaviour. The findings highlight the potential benefit of combining these two prominent models in terms of improving understanding of volunteerism and providing a single parsimonious model for raising rates of this important behaviour.