628 resultados para Maximizing
Resumo:
We model the joint production of entrepreneurs and workers where the former provide both entrepreneurial (strategic) and managerial (coordination, motivation) services, and management services are shared with individual workers in an output maximizing way. The static equilibrium of the model determines the endogenous share of entrepreneurs in the economy in a given moment of time. The time dynamics of the solution implies that a given growth rate in quality of entrepreneurial services contributes to productivity growth proportionally to the share of entrepreneurs at the start of the period and improvement in quality of entrepreneurial services is convergence enhancing. Model predictions are tested with data from OECD countries in the period 1970-2002. We find that improvements in quality of entrepreneurial services over time explain up to 100% of observed average productivity growth in these countries.
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Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.
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Limited dispersal may favor the evolution of helping behaviors between relatives as it increases their relatedness, and it may inhibit such evolution as it increases local competition between these relatives. Here, we explore one way out of this dilemma: if the helping behavior allows groups to expand in size, then the kin-competition pressure opposing its evolution can be greatly reduced. We explore the effects of two kinds of stochasticity allowing for such deme expansion. First, we study the evolution of helping under environmental stochasticity that may induce complete patch extinction. Helping evolves if it results in a decrease in the probability of extinction or if it enhances the rate of patch recolonization through propagules formed by fission of nonextinct groups. This mode of dispersal is indeed commonly found in social species. Second, we consider the evolution of helping in the presence of demographic stochasticity. When fecundity is below its value maximizing deme size (undersaturation), helping evolves, but under stringent conditions unless positive density dependence (Allee effect) interferes with demographic stochasticity. When fecundity is above its value maximizing deme size (oversaturation), helping may also evolve, but only if it reduces negative density-dependent competition.
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We extend the model of collective action in which groups compete for a budged by endogenizing the group platform, namely the specific mixture of public/private good and the distribution of the private good to group members which can be uniform or performance-based. While the group-optimal platform contains a degree of publicness that increases in group size and divides the private benefits uniformly, a success-maximizing leader uses incentives and distorts the platform towards more private benefits - a distortion that increases with group size. In both settings we obtain the anti-Olson type result that win probability increases with group size.
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Unilateral migration policies impose externalities on other countries. In order to try to internalize these externalities, countries sign bilateral migration agreements. One element of these agreements is the emphasis on enforcing migration policies: immigrant-receiving countries agree to allow more immigrants from their emigrant-sending partner if they cooperate in enforcing their migration policy at the border. I present a simple theoretical model that justifies this behavior in a two-country setting with welfare maximizing governments. These governments establish migration quotas that need to be enforced at a cost. I prove that uncoordinated migration policies are inefficient. Both countries can improve welfare by exchanging a more "generous" migration quota for expenditure on enforcement policy. Contrary to what could be expected, this result does not depend on the enforcement technology that both countries employ.
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This paper studies information transmission between multiple agents with di¤erent preferences and a welfare maximizing decision maker who chooses the quality or quantity of a public good (e.g. provision of public health service; carbon emissions policy; pace of lectures in a classroom) that is consumed by all of them. Communication in such circumstances suffers from the agents' incentive to "exaggerate" their preferences relative to the average of the other agents, since the decision maker's reaction to each agent's message is weaker than in one-to-one communication. As the number of agents becomes larger the quality of information transmission diminishes. The use of binary messages (e.g. "yes" or "no") is shown to be a robust mode of communication when the main source of informational distortion is exaggeration.
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The aim of this paper is to investigate the welfare effect of a change in the public firms objective function in oligopoly when the government takes into account the distortionary effect of rising funds by taxation (shadow cost of public funds). We analyze the impact of a shift from welfare- to profit-maximizing behaviour of the public firm on the timing of competition by endogenizing the determination of simultaneous (Nash-Cournot) versus sequential (Stackelberg) games using the game with observable delay proposed by Hamilton and Slutsky (1990). Differently from previous work that assumed the timing of competition, we show that, absent efficiency gains, instructing the public firm to play as a private one never increases welfare. Moreover, even when large efficiency gains result from the shift in public firm's objective, an inefficient public firm that maximizes welfare may be preferred.
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In this paper, we provide an explanation of why privatization may attract foreign investors willing to enter a regional market. Privatization turns the formerly-public firm into a less aggressive competitor since profit-maximizing output is lower than the welfaremaximizing one. The drawback is that social welfare generally decreases. We also investigate tax/subsidy competition for FDI and put forward its potentially positive role. On the one hand, it may reduce the negative impact on welfare of an FDI-attracting privatization. On the other hand, it may prevent a welfare-reducing investment by the foreign firm. This sheds light on the substitute/complementary relationship between the two policies and the two objectives of governments.
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We consider negotiations selecting one-dimensional policies. Individuals have single-peaked preferences, and they are impatient. Decisions arise from a bargaining game with random proposers and (super) majority approval, ranging from the simple majority up to unanimity. The existence and uniqueness of stationary subgame perfect equilibrium is established, and its explicit characterization provided. We supply an explicit formula to determine the unique alternative that prevails, as impatience vanishes, for each majority. As an application, we examine the efficiency of majority rules. For symmetric distributions of peaks unanimity is the unanimously preferred majority rule. For asymmetric populations rules maximizing social surplus are characterized.
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Colistin is a last resort's antibacterial treatment in critically ill patients with multi-drug resistant Gram-negative infections. As appropriate colistin exposure is the key for maximizing efficacy while minimizing toxicity, individualized dosing optimization guided by therapeutic drug monitoring is a top clinical priority. Objective of the present work was to develop a rapid and robust HPLC-MS/MS assay for quantification of colistin plasma concentrations. This novel methodology validated according to international standards simultaneously quantifies the microbiologically active compounds colistin A and B, plus the pro-drug colistin methanesulfonate (colistimethate, CMS). 96-well micro-Elution SPE on Oasis Hydrophilic-Lipophilic-Balanced (HLB) followed by direct analysis by Hydrophilic Interaction Liquid Chromatography (HILIC) with Ethylene Bridged Hybrid - BEH - Amide phase column coupled to tandem mass spectrometry allows a high-throughput with no significant matrix effect. The technique is highly sensitive (limit of quantification 0.014 and 0.006μg/mL for colistin A and B), precise (intra-/inter-assay CV 0.6-8.4%) and accurate (intra-/inter-assay deviation from nominal concentrations -4.4 to +6.3%) over the clinically relevant analytical range 0.05-20μg/mL. Colistin A and B in plasma and whole blood samples are reliably quantified over 48h at room temperature and at +4°C (<6% deviation from nominal values) and after three freeze-thaw cycles. Colistimethate acidic hydrolysis (1M H2SO4) to colistin A and B in plasma was completed in vitro after 15min of sonication while the pro-drug hydrolyzed spontaneously in plasma ex vivo after 4h at room temperature: this information is of utmost importance for interpretation of analytical results. Quantification is precise and accurate when using serum, citrated or EDTA plasma as biological matrix, while use of heparin plasma is not appropriate. This new analytical technique providing optimized quantification in real-life conditions of the microbiologically active compounds colistin A and B offers a highly efficient tool for routine therapeutic drug monitoring aimed at individualizing drug dosing against life-threatening infections.
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Therapeutic drug monitoring (TDM) may contribute to optimizing the efficacy and safety of antifungal therapy because of the large variability in drug pharmacokinetics. Rapid, sensitive, and selective laboratory methods are needed for efficient TDM. Quantification of several antifungals in a single analytical run may best fulfill these requirements. We therefore developed a multiplex ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method requiring 100 μl of plasma for simultaneous quantification within 7 min of fluconazole, itraconazole, hydroxyitraconazole, posaconazole, voriconazole, voriconazole-N-oxide, caspofungin, and anidulafungin. Protein precipitation with acetonitrile was used in a single extraction procedure for eight analytes. After reverse-phase chromatographic separation, antifungals were quantified by electrospray ionization-triple-quadrupole mass spectrometry by selected reaction monitoring detection using the positive mode. Deuterated isotopic compounds of azole antifungals were used as internal standards. The method was validated based on FDA recommendations, including assessment of extraction yields, matrix effect variability (<9.2%), and analytical recovery (80.1 to 107%). The method is sensitive (lower limits of azole quantification, 0.01 to 0.1 μg/ml; those of echinocandin quantification, 0.06 to 0.1 μg/ml), accurate (intra- and interassay biases of -9.9 to +5% and -4.0 to +8.8%, respectively), and precise (intra- and interassay coefficients of variation of 1.2 to 11.1% and 1.2 to 8.9%, respectively) over clinical concentration ranges (upper limits of quantification, 5 to 50 μg/ml). Thus, we developed a simple, rapid, and robust multiplex UPLC-MS/MS assay for simultaneous quantification of plasma concentrations of six antifungals and two metabolites. This offers, by optimized and cost-effective lab resource utilization, an efficient tool for daily routine TDM aimed at maximizing the real-time efficacy and safety of different recommended single-drug antifungal regimens and combination salvage therapies, as well as a tool for clinical research.
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Vector species has not hitherto been studied as influencing metacyclogenesis of Trypanosoma cruzi, while the role of the parasite strain has been frequently stressed as of dominant importance in this process. In order to fill this gap in our knowledge, metacyclogenesis was monitored in nine triatomine species. The first part of this paper presents photographs of the main and intermediate parasite stages in each vector species studied. In the second part of the study the proportional distribution of all these forms, as seen in Giemsa stained smears is summarized, thus providing an opportunity to analyze both: the length of time between the ingestion of the blood trypomastigotes and the appearance of metacyclic forms and the rates of developmental stages leading to these latter. The most remarkable observation was that metacyclogenesis rates in vivo appear to be vector dependent, reaching 50 in Rhodnius neglectus, 37 in its congener R. prolixus and being dramatically lower in the majority of Triatoma species (5 in T. sordida, 3 in T. brasiliensis and 0 in T. pseudomaculata) at the 120th day of infection. These observations suggest that through screening of different vector species it is possible to find some that are capable of minimizing or maximizing metacyclic production.
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Nowadays, service providers in the Cloud offer complex services ready to be used as it was a commodity like water or electricity to their customers with any other extra effort for them. However, providing these services implies a high management effort which requires a lot of human interaction. Furthermore, an efficient resource management mechanism considering only provider's resources is, though necessary, not enough, because the provider's profit is limited by the amount of resources it owns. Dynamically outsourcing resources to other providers in response to demand variation avoids this problem and makes the provider to get more profit. A key technology for achieving these goals is virtualization which facilitates provider's management and provides on-demand virtual environments, which are isolated and consolidated in order to achieve a better utilization of the provider's resources. Nevertheless, dealing with some virtualization capabilities implies an effort for the user in order to take benefit from them. In order to avoid this problem, we are contributing the research community with a virtualized environment manager which aims to provide virtual machines that fulfils with the user requirements. Another challenge is sharing resources among different federated Cloud providers while exploiting the features of virtualization in a new approach for facilitating providers' management. This project aims for reducing provider's costs and at the same time fulfilling the quality of service agreed with the customers while maximizing the provider's revenue. It considers resource management at several layers, namely locally to each node in the provider, among different nodes in the provider, and among different federated providers. This latter layer supports the novel capabilities of outsourcing when the local resources are not enough to fulfil the users demand, and offering resources to other providers when the local resources are underused.
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Multiple sclerosis (MS) is a life-long, potentially debilitating disease of the central nervous system (CNS). MS is considered to be an immune-mediated disease, and the presence of autoreactive peripheral lymphocytes in CNS compartments is believed to be critical in the process of demyelination and tissue damage in MS. Although MS is not currently a curable disease, several disease-modifying therapies (DMTs) are now available, or are in development. These DMTs are all thought to primarily suppress autoimmune activity within the CNS. Each therapy has its own mechanism of action (MoA) and, as a consequence, each has a different efficacy and safety profile. Neurologists can now select therapies on a more individual, patient-tailored basis, with the aim of maximizing potential for long-term efficacy without interruptions in treatment. The MoA and clinical profile of MS therapies are important considerations when making that choice or when switching therapies due to suboptimal disease response. This article therefore reviews the known and putative immunological MoAs alongside a summary of the clinical profile of therapies approved for relapsing forms of MS, and those in late-stage development, based on published data from pivotal randomized, controlled trials.
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Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.