972 resultados para search process


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In this paper I devise a new channel by means of which the (empirically documented) positive correlation between ináation and income inequality can be understood. Available empirical evidence reveals that ináation increases wage dispersion. For this reason, the higher the ináation rate, the higher turns out to be the beneÖt, for a worker, of making additional draws from the distribution of wages, before deciding whether to accept or reject a job o§er. Assuming that some workers have less access to information (wage o§ers) than others, I show that the Gini coe¢ cient of income distribution turns out to be an increasing function of the wage dispersion and, consequently, of the rate of ináation. Two examples are provided to illustrate the mechanism.

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This paper investigates the income inequality generated by a jobsearch process when di§erent cohorts of homogeneous workers are allowed to have di§erent degrees of impatience. Using the fact the average wage under the invariant Markovian distribution is a decreasing function of the time preference (Cysne (2004)), I show that the Lorenz curve and the between-cohort Gini coe¢ cient of income inequality can be easily derived in this case. An example with arbitrary measures regarding the wage o§ers and the distribution of time preferences among cohorts provides some quantitative insights into how much income inequality can be generated, and into how it varies as a function of the probability of unemployment and of the probability that the worker does not Önd a job o§er each period.

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In this paper I claim that, in a long-run perspective, measurements of income inequality, under any of the usual inequality measures used in the literature, are upward biased. The reason is that such measurements are cross-sectional by nature and, therefore, do not take into consideration the turnover in the job market which, in the long run, equalizes within-group (e.g., same-education groups) inequalities. Using a job-search model, I show how to derive the within-group invariant-distribution Gini coefficient of income inequality, how to calculate the size of the bias and how to organize the data in arder to solve the problem. Two examples are provided to illustrate the argument.

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Työn tavoitteena oli selvittää organisaation ostopäätösprosessi, kun tuotteina ovat biohajoavat kasvo- ja kallokirurgian implantit. Ensin selvitettiin biohajoavien implanttien markkinapotentiaalia, biohajoavien materiaalien lisäksi implanttien valmistuksessa käytettäviä muita materiaaleja sekä implanteilta vaadittavia ominaisuuksia kirjallisuuden ja internetin sekä asiantuntijahaastatteluiden avulla. Kirjallisuuden avulla selvitettiin myös organisaatioiden ostopäätösprosessien yleisiä piirteitä ja vaiheita. Biohajoavien kasvo- ja kallokirurgian implanttien ostopäätösprosessia tutkittiin kirjallisen kyselytutkimuksen avulla, joka oli suunnattu alan asiantuntijoille Euroopassa, Yhdysvalloissa sekä Kanadassa. Tutkimuksessa selvitettiin mm. tärkeimpiä käytettävien implanttien materiaalivalintaan vaikuttavia kriteereitä, ostopäätösprosessiin osallistuvia organisaation jäseniä, sekä heidän roolejaan päätöksenteossa, implantteja koskevan informaation etsintää sekä ostopäätösprosessin vaiheita. Kirjallisuudesta, internetistä, asiantuntijahaastatteluista ja kyselytutkimuksesta saatu tieto koottiin vuokaaviomalliksi, joka kuvaa kasvo- ja kallokirurgian implanttien ostopäätösprosessia organisaatioissa. Lopuksi esitettiin myös ehdotuksia markkinointisuunnitelmaan sekä jatkotutkimusehdotukset.

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This dissertation explores the use of internal and external sources of knowledge in modern innovation processes. It builds on a framework that combines theories such as a behavioural theory of the firm, the evolutionary theory of economic change, and modern approaches to strategic management. It follows the recent increase in innovation research focusing on the firm-level examination of innovative activities instead of traditional industry-level determinants. The innovation process is seen as a problem- and slack- driven search process, which can take several directions in terms of organizational boundaries in the pursuit of new knowledge and other resources. It thus draws on recent models of technological change, according to which firms nowadays should build their innovative activities on both internal and external sources of innovation rather than relying solely on internal resources. Four different research questions are addressed, all of which are empirically investigated via a rich dataset covering Finnish innovators collected by Statistics Finland. Firstly, the study examines how the nature of problems shapes the direction of any search for new knowledge. In general it demonstrates that the nature of the problem does affect the direction of the search, although under resource constraints firms tend to use external rather than internal sources of knowledge. At the same time, it shows that those firms that are constrained in terms of finance seem to search both internally and externally. Secondly, the dissertation investigates the relationships between different kinds of internal and external sources of knowledge in an attempt to find out where firms should direct their search in order to exploit the potential of a distributed innovation process. The concept of complementarities is applied in this context. The third research question concerns how the use of external knowledge sources – openness to external knowledge – influences the financial performance of firms. Given the many advantages of openness presented in the current literature, the focus is on how it shapes profitability. The results reveal a curvilinear relationship between profitability and openness (taking an inverted U-shape), the implication being that it pays to be open up to a certain point, but being too open to external sources may be detrimental to financial performance. Finally, the dissertation addresses some challenges in CISbased innovation research that have received relatively little attention in prior studies. The general aim is to underline the fact that comprehensive understanding of the complex process of technological change requires the constant development of methodological approaches (in terms of data and measures, for example). All the empirical analyses included in the dissertation are based on the Finnish CIS (Finnish Innovation Survey 1998-2000).

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In this thesis we made the first steps towards the systematic application of a methodology for automatically building formal models of complex biological systems. Such a methodology could be useful also to design artificial systems possessing desirable properties such as robustness and evolvability. The approach we follow in this thesis is to manipulate formal models by means of adaptive search methods called metaheuristics. In the first part of the thesis we develop state-of-the-art hybrid metaheuristic algorithms to tackle two important problems in genomics, namely, the Haplotype Inference by parsimony and the Founder Sequence Reconstruction Problem. We compare our algorithms with other effective techniques in the literature, we show strength and limitations of our approaches to various problem formulations and, finally, we propose further enhancements that could possibly improve the performance of our algorithms and widen their applicability. In the second part, we concentrate on Boolean network (BN) models of gene regulatory networks (GRNs). We detail our automatic design methodology and apply it to four use cases which correspond to different design criteria and address some limitations of GRN modeling by BNs. Finally, we tackle the Density Classification Problem with the aim of showing the learning capabilities of BNs. Experimental evaluation of this methodology shows its efficacy in producing network that meet our design criteria. Our results, coherently to what has been found in other works, also suggest that networks manipulated by a search process exhibit a mixture of characteristics typical of different dynamical regimes.

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In many developing countries, clusters of small shops are the typical market-place. We investigate an economic model in which, between buyers and sellers in a marketplace, a circular causality including the search process produces agglomeration forces, given the initial location of the marketplace location exogenously in a linear city. We conclude that initial number of buyers and sellers is important in forming a large marketplace.

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The paper presents a critical analysis of the extant literature pertaining to the networking behaviours of young jobseekers in both offline and online environments. A framework derived from information behaviour theory is proposed as a basis for conducting further research in this area. Method. Relevant material for the review was sourced from key research domains such as library and information science, job search research, and organisational research. Analysis. Three key research themes emerged from the analysis of the literature: (1) social networks, and the use of informal channels of information during job search, (2) the role of networking behaviours in job search, and (3) the adoption of social media tools. Tom Wilson’s general model of information behaviour was also identified as a suitable framework to conduct further research. Results. Social networks have a crucial informational utility during the job search process. However, the processes whereby young jobseekers engage in networking behaviours, both offline and online, remain largely unexplored. Conclusion. Identification and analysis of the key research themes reveal opportunities to acquire further knowledge regarding the networking behaviours of young jobseekers. Wilson’s model can be used as a framework to provide a holistic understanding of the networking process, from an information behaviour perspective.

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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

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The ability to solve conflicting beliefs is crucial for multi- agent systems where the information is dynamic, incomplete and dis- tributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance sy- stem and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are au- tomatically detected by system’s reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodolo- gies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Con- flicts is, basically, a selection process based on the assessment of the cre- dibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a “next best” relaxation strategy.

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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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Highlights: * Workers’ Compensation CommissionerChristopher Godfrey Testifies Before Congress........................................pg. 2 *Lakes Area and Kossuth County ECI to Hold Joint Meeting.............................pg. 2 *IWD Assists Ex-Offenders in the Work Search Process.......................................pg. 3 *eFile for a Fast Income Tax Refund..pg. 3 *IowaWORKS Greater Siouxland Naturalization Ceremony...................pg. 5