Equities and inequality

Most US credit card holders revolve high-interest debt, often combined with substantial (i) asset accumulation by retirement, and (ii) low-rate liquid assets. Hyperbolic discounting can resolve only the former puzzle (Laibson et al., 2003). Bertaut and Haliassos (2002) proposed an 'accountant-shopper'framework for the latter. The current paper builds, solves, and simulates a fully-specified accountant-shopper model, to show that this framework canactually generate both types of co-existence, as well as target credit card utilization rates consistent with Gross and Souleles (2002). The benchmark model is compared to setups without self-control problems, with alternative mechanisms, and with impatient but fully rational shoppers.

Managing Subsidiary Integration During Corporate Expansion in Russia

Kansallisten rajojen yli laajentuvat yritykset kohtaavat kasvavia paineita yhtenäistää eri yksiköiden toimintatapoja, prosesseja ja järjestelmiä. Hyvin toteutettuna organisaation sisäinen integrointi voi johtaa tytäryritysten tuottavuuden parantumiseen ja strategisiin mittakaavaetuihin, kun taas huonosti toteutettuna integrointi voi johtaa lisääntyviin konflikteihin ja emoyhtiön kontrollin katoamiseen. Integroinnin kannalta Venäjälle perustettavat tytäryritykset asettavat suuria haasteita. Kasvava ja kehittyvä kansantalous on jatkuvassa muutoksessa kohti edistyneempiä ja tuottavampia toimintamalleja, mutta toisaalta yritystoiminnantaustalla vaikuttaa edelleenkin Neuvostoajan perinnöt, jotka muokkaavat yritysten johtamisrakenteita ja prosesseja. Nämä taustavaikuttajat vaikeuttavat kansainvälisen yrityksen yhtenäistämistä, mutta toisaalta tarjoavat suuria mahdollisuuksia yrityksille, jotka oppivat elämään Venäjän markkinoiden ehdoilla. Tämä tutkimus käyttää apunaan konstruktiivista tutkimustyötä ratkoakseenYIT:n Venäjälle perustettavien yritysten integrointiin liittyviä ongelmia ja mahdollisuuksia. Työn lopputuloksena syntyy oppimiseen pohjautuva integraatiostrategia ja tätä strategiaa tukeva integroinnin johtamisjärjestelmä.

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Background: Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements. Results: We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer- approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shock Conclusion: Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

Modelling leaf development in Oxalis latifolia

This study was carried to develop functions that could explain the growth of Oxalis latifolia, in both early stages and throughout the season, contributing to the improvement of its cultural control. Bulbs of the Cornwall form of O. latifolia were buried at 1 and 8 cm in March 1999 and 2000. Samples were destructive at fixed times, and at each time the corresponding BBCH scale codes as well as the absolute number of growing and adult leaves were noted. Using the absolute number of adult leaves (transformed to percentages), a Gaussian curve of three parameters that explains the growth during the season (R2=0.9355) was developed. The BBCH scale permitted the fit of two regression lines that were accurately adjusted for each burial depth (R2=0.9969 and R2=0.9930 respectively for 1 and 8 cm). The best moment for an early defoliation in Northern Spain could be calculated with these regression lines, and was found to be the second week of May. In addition, it was observed that a burial depth of 8 cm does not affect the growing pattern of the weed, but it affects the number of leaves they produce, which decreases to less than a half of those produced at 1 cm.

Incumbent audit firm-provided tax services and the clients with low financial reporting quality

This study investigates whether incumbent audit firm-provided tax services enhance or impair the likelihood of acknowledging client companies’ low financial reporting quality. In particular, we examine the association between tax-related fees and the likelihood of timely restatements, and internal control weakness disclosures among a sample of US companies that all have misstatements in financial information. The empirical findings indicate that companies paying higher tax-related fees are less likely to disclose SOX 404 internal control weakness disclosures, implying that underlying control problems are unacknowledged when incumbent audit firm provided tax-related fees are higher. However, the findings suggest that just providing both audit and tax-related services does not have an impact on audit quality per se, but rather it is the magnitude of the tax-related fees in particular that counts. We also find some evidence suggesting that companies paying higher tax-related fees have higher likelihood of restatement lags, whereas companies paying smaller tax-related fees to their audit firm restate financial statements in a timelier manner. Overall, the findings suggest that audit scrutiny of client companies with low quality financial reporting is weaker when the magnitude of tax-related fees is higher.

Freedom of choice and bounded rationality: a brief appraisal of behavioral economists' plea for light paternalism

Behavioral economics has addressed interesting positive and normative questions underlying the standard rational choice theory. More recently, it suggests that, in a real world of boundedly rational agents, economists could help people to improve the quality of their choices without any harm to autonomy and freedom of choice. This paper aims to scrutinize available arguments for and against current proposals of light paternalistic interventions mainly in the domain of intertemporal choice. It argues that incorporating the notion of bounded rationality in economic analysis and empirical findings of cognitive biases and self-control problems cannot make an indisputable case for paternalism.

Fixed point results for multivalued contractions on graphs and their applications

Nous présentons dans cette thèse des théorèmes de point fixe pour des contractions multivoques définies sur des espaces métriques, et, sur des espaces de jauges munis d’un graphe. Nous illustrons également les applications de ces résultats à des inclusions intégrales et à la théorie des fractales. Cette thèse est composée de quatre articles qui sont présentés dans quatre chapitres. Dans le chapitre 1, nous établissons des résultats de point fixe pour des fonctions multivoques, appelées G-contractions faibles. Celles-ci envoient des points connexes dans des points connexes et contractent la longueur des chemins. Les ensembles de points fixes sont étudiés. La propriété d’invariance homotopique d’existence d’un point fixe est également établie pour une famille de Gcontractions multivoques faibles. Dans le chapitre 2, nous établissons l’existence de solutions pour des systèmes d’inclusions intégrales de Hammerstein sous des conditions de type de monotonie mixte. L’existence de solutions pour des systèmes d’inclusions différentielles avec conditions initiales ou conditions aux limites périodiques est également obtenue. Nos résultats s’appuient sur nos théorèmes de point fixe pour des G-contractions multivoques faibles établis au chapitre 1. Dans le chapitre 3, nous appliquons ces mêmes résultats de point fixe aux systèmes de fonctions itérées assujettis à un graphe orienté. Plus précisément, nous construisons un espace métrique muni d’un graphe G et une G-contraction appropriés. En utilisant les points fixes de cette G-contraction, nous obtenons plus d’information sur les attracteurs de ces systèmes de fonctions itérées. Dans le chapitre 4, nous considérons des contractions multivoques définies sur un espace de jauges muni d’un graphe. Nous prouvons un résultat de point fixe pour des fonctions multivoques qui envoient des points connexes dans des points connexes et qui satisfont une condition de contraction généralisée. Ensuite, nous étudions des systèmes infinis de fonctions itérées assujettis à un graphe orienté (H-IIFS). Nous donnons des conditions assurant l’existence d’un attracteur unique à un H-IIFS. Enfin, nous appliquons notre résultat de point fixe pour des contractions multivoques définies sur un espace de jauges muni d’un graphe pour obtenir plus d’information sur l’attracteur d’un H-IIFS. Plus précisément, nous construisons un espace de jauges muni d’un graphe G et une G-contraction appropriés tels que ses points fixes sont des sous-attracteurs du H-IIFS.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Software Development: Testing

This presentation discusses the role and purpose of testing in the systems/Software Development Life Cycle. We examine the consequences of the 'cost curve' on defect removal and how agile methods can reduce its effects. We concentrate on Black Box Testing and use Equivalence Partitioning and Boundary Value Analysis to construct the smallest number of test cases, test scenarios necessary for a test plan.

Optimal management of natural resources. Accounting for heterogeneity

Dynamic optimization methods have become increasingly important over the last years in economics. Within the dynamic optimization techniques employed, optimal control has emerged as the most powerful tool for the theoretical economic analysis. However, there is the need to advance further and take account that many dynamic economic processes are, in addition, dependent on some other parameter different than time. One can think of relaxing the assumption of a representative (homogeneous) agent in macro- and micro-economic applications allowing for heterogeneity among the agents. For instance, the optimal adaptation and diffusion of a new technology over time, may depend on the age of the person that adopted the new technology. Therefore, the economic models must take account of heterogeneity conditions within the dynamic framework. This thesis intends to accomplish two goals. The first goal is to analyze and revise existing environmental policies that focus on defining the optimal management of natural resources over time, by taking account of the heterogeneity of environmental conditions. Thus, the thesis makes a policy orientated contribution in the field of environmental policy by defining the necessary changes to transform an environmental policy based on the assumption of homogeneity into an environmental policy which takes account of heterogeneity. As a result the newly defined environmental policy will be more efficient and likely also politically more acceptable since it is tailored more specifically to the heterogeneous environmental conditions. Additionally to its policy orientated contribution, this thesis aims making a methodological contribution by applying a new optimization technique for solving problems where the control variables depend on two or more arguments --- the so-called two-stage solution approach ---, and by applying a numerical method --- the Escalator Boxcar Train Method --- for solving distributed optimal control problems, i.e., problems where the state variables, in addition to the control variables, depend on two or more arguments. Chapter 2 presents a theoretical framework to determine optimal resource allocation over time for the production of a good by heterogeneous producers, who generate a stock externalit and derives government policies to modify the behavior of competitive producers in order to achieve optimality. Chapter 3 illustrates the method in a more specific context, and integrates the aspects of quality and time, presenting a theoretical model that allows to determine the socially optimal outcome over time and space for the problem of waterlogging in irrigated agricultural production. Chapter 4 of this thesis concentrates on forestry resources and analyses the optimal selective-logging regime of a size-distributed forest.

Enhanced convolution approach for CAC in ATM networks, an analytical study and implementation

The characteristics of service independence and flexibility of ATM networks make the control problems of such networks very critical. One of the main challenges in ATM networks is to design traffic control mechanisms that enable both economically efficient use of the network resources and desired quality of service to higher layer applications. Window flow control mechanisms of traditional packet switched networks are not well suited to real time services, at the speeds envisaged for the future networks. In this work, the utilisation of the Probability of Congestion (PC) as a bandwidth decision parameter is presented. The validity of PC utilisation is compared with QOS parameters in buffer-less environments when only the cell loss ratio (CLR) parameter is relevant. The convolution algorithm is a good solution for CAC in ATM networks with small buffers. If the source characteristics are known, the actual CLR can be very well estimated. Furthermore, this estimation is always conservative, allowing the retention of the network performance guarantees. Several experiments have been carried out and investigated to explain the deviation between the proposed method and the simulation. Time parameters for burst length and different buffer sizes have been considered. Experiments to confine the limits of the burst length with respect to the buffer size conclude that a minimum buffer size is necessary to achieve adequate cell contention. Note that propagation delay is a no dismiss limit for long distance and interactive communications, then small buffer must be used in order to minimise delay. Under previous premises, the convolution approach is the most accurate method used in bandwidth allocation. This method gives enough accuracy in both homogeneous and heterogeneous networks. But, the convolution approach has a considerable computation cost and a high number of accumulated calculations. To overcome this drawbacks, a new method of evaluation is analysed: the Enhanced Convolution Approach (ECA). In ECA, traffic is grouped in classes of identical parameters. By using the multinomial distribution function instead of the formula-based convolution, a partial state corresponding to each class of traffic is obtained. Finally, the global state probabilities are evaluated by multi-convolution of the partial results. This method avoids accumulated calculations and saves storage requirements, specially in complex scenarios. Sorting is the dominant factor for the formula-based convolution, whereas cost evaluation is the dominant factor for the enhanced convolution. A set of cut-off mechanisms are introduced to reduce the complexity of the ECA evaluation. The ECA also computes the CLR for each j-class of traffic (CLRj), an expression for the CLRj evaluation is also presented. We can conclude that by combining the ECA method with cut-off mechanisms, utilisation of ECA in real-time CAC environments as a single level scheme is always possible.

Susceptibility to the anticoagulants bromadiolone and coumatetralyl in wild Norway rats (Rattus norvegicus) from the UK and Germany

A new blood clotting response test was used to determine the susceptibility, to coumatetralyl and bromadiolone, of laboratory strains of Norway rat from Germany and the UK (Hampshire), and wild rats trapped on farms in Wales (UK) and Westphalia (Germany). Resistance factors were calculated in relation to the CD strain of Norway rat. An outbred strain of wild rats, raised from rats trapped in Germany, was found to be more susceptible to coumatetralyl by a factor of 0.5-0.6 compared to the CD strain. Homozygous and heterozygous animals of a strain of resistant rats from Westphalia were cross-resistant to coumatetralyl and bromadiolone, with a higher resistance factor for bromadiolone than that found in both UK strains. Our results show that the degree of altered susceptibility and resistance varies between strains of wild rat and between resistance foci. Some wild rat strains may be more susceptible than laboratory rat strains. Even in a well-established resistance area, it may be difficult to find infestations with resistance high enough to suspect control problems with bromadiolone, even after decades of use of this compound.

Grey-box radial basis function modelling: the art of incorporating prior knowledge

A basic principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Error estimates for a fully discrete spectral scheme for a class of nonlinear, nonlocal dispersive wave equations

We analyze a fully discrete spectral method for the numerical solution of the initial- and periodic boundary-value problem for two nonlinear, nonlocal, dispersive wave equations, the Benjamin–Ono and the Intermediate Long Wave equations. The equations are discretized in space by the standard Fourier–Galerkin spectral method and in time by the explicit leap-frog scheme. For the resulting fully discrete, conditionally stable scheme we prove an L2-error bound of spectral accuracy in space and of second-order accuracy in time.

New developments in batch process input profile optimisation

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses dynamic integrated system optimisation and parameter estimation (DISOPE) which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A new method for approximating some Jacobian trajectories required by the algorithm is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch chemical processes.