890 resultados para Input bias
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Ever since Adam Smith, economists have argued that share contracts do not provide proper incentives. This paper uses tenancy data from India to assess the existence of missing incentives in this classical example of moral hazard. Sharecroppers are found to be less productive than owners, but as productive as fixed-rent tenants. Also, the productivity gap between owners and both types of tenants is driven by sample-selection issues. An endogenous selection rule matches tenancy contracts with less-skilled farmers and lower-quality lands. Due to complementarity, such a matching affects tenants’ input choices. Controlling for that, the contract form has no effect on the expected output. Next, I explicitly model farmer’s optimal decisions to test the existence of non-contractible inputs being misused. No evidence of missing incentives is found.
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We analyze the impact on consumer prices of the size and bias of price comparison search engines. In the context of a model related to Burdett and Judd (1983) and Varian (1980), we develop and test experimentally several theoretical predictions. The experimental results confirm the model’s predictions regarding the impact of the number of firms, and the type of bias of the search engine, but reject the model’s predictions regarding changes in the size of the index. The explanatory power of an econometric model for the price distributions is significantly improved when variables accounting for risk attitudes are introduced.
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Decision makers often use ‘rules of thumb’, or heuristics, to help them handling decision situations (Kahneman and Tversky, 1979b). Those cognitive shortcuts are taken by the brain to cope with complexity and time limitation of decisions, by reducing the burden of information processing (Hodgkinson et al, 1999; Newell and Simon, 1972). Although crucial for decision-making, heuristics come at the cost of occasionally sending us off course, that is, make us fall into judgment traps (Tversky and Kahneman, 1974). Over fifty years of psychological research has shown that heuristics can lead to systematic errors, or biases, in decision-making. This study focuses on two particularly impactful biases to decision-making – the overconfidence and confirmation biases. A specific group – top management school students and recent graduates - were subject to classic experiments to measure their level of susceptibility to those biases. This population is bound to take decision positions at companies, and eventually make decisions that will impact not only their companies but society at large. The results show that this population is strongly biased by overconfidence, but less so to the confirmation bias. No significant relationship between the level of susceptibility to the overconfidence and to the confirmation bias was found.
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Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.
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A presente dissertação apresenta uma análise da concentração do portfólio de a-ções de investidores brasileiros nas próprias empresas onde trabalham com o intuito de observar se o Home Bias se aplica à amostra analisada. Nosso estudo foi reali-zado com uma amostra extraída da custódia de 86 clientes de uma corretora de va-lores mobiliários, sendo estes dados reais de mercado. Restringimos a seleção da amostra de forma que metade fosse de clientes que trabalham em empresas de ca-pital aberto e a outra metade não. Foi feita análise cross section de quanto os inves-tidores alocam em ações das empresas para a qual trabalham e verificou-se qual o percentual desta participação em seus portfólios, em comparação a uma amostra de controle de investidores que não trabalham nesta mesma empresa. Além destas a-nálises, separamos a amostra pelo valor total do portfólio e realizamos os mesmos estudos com estes dois grupos diferentes da amostra. Como uma análise de robus-tez, identificamos empresas listadas que não adotam a remuneração com ações como uma sub-amostra do estudo. Como resultado, encontramos evidências de que os funcionários investem significativamente mais (seja em proporção do portfólio ou em valores) em ações das empresas onde trabalham em relação aos demais inves-tidores, evidenciando um viés de familiaridade na tomada de decisão de investimen-tos.
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Os estudos sobre as expectativas de inflação no Brasil rejeitam a hipótese de racionalidade. Essa rejeição se dá por meio de testes estatísticos que identificam a existência de um viés sistemático quando comparamos a expectativa de inflação e a inflação realizada. Atualizamos alguns destes testes com o tamanho de amostra disponível atualmente. No presente trabalho, realizamos um experimento de Monte Carlo que simula o comportamento da inflação e da sua expectativa em um modelo DSGE. Esse modelo inclui uma regra monetária sujeita a choques transitórios e permanentes (que representam uma mudança de regime). A partir das séries simuladas com esses modelos, realizamos testes estatísticos para verificar se os resultados são semelhantes aos observados na prática. O exercício de simulação realizado não foi capaz de gerar séries com essas mesmas características, não trazendo evidência que esse mecanismo de aprendizado possa explicar o viés encontrado nas expectativas de inflação.
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In this Thesis, the development of the dynamic model of multirotor unmanned aerial vehicle with vertical takeoff and landing characteristics, considering input nonlinearities and a full state robust backstepping controller are presented. The dynamic model is expressed using the Newton-Euler laws, aiming to obtain a better mathematical representation of the mechanical system for system analysis and control design, not only when it is hovering, but also when it is taking-off, or landing, or flying to perform a task. The input nonlinearities are the deadzone and saturation, where the gravitational effect and the inherent physical constrains of the rotors are related and addressed. The experimental multirotor aerial vehicle is equipped with an inertial measurement unit and a sonar sensor, which appropriately provides measurements of attitude and altitude. A real-time attitude estimation scheme based on the extended Kalman filter using quaternions was developed. Then, for robustness analysis, sensors were modeled as the ideal value with addition of an unknown bias and unknown white noise. The bounded robust attitude/altitude controller were derived based on globally uniformly practically asymptotically stable for real systems, that remains globally uniformly asymptotically stable if and only if their solutions are globally uniformly bounded, dealing with convergence and stability into a ball of the state space with non-null radius, under some assumptions. The Lyapunov analysis technique was used to prove the stability of the closed-loop system, compute bounds on control gains and guaranteeing desired bounds on attitude dynamics tracking errors in the presence of measurement disturbances. The controller laws were tested in numerical simulations and in an experimental hexarotor, developed at the UFRN Robotics Laboratory
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A main purpose of a mathematical nutrition model (a.k.a., feeding systems) is to provide a mathematical approach for determining the amount and composition of the diet necessary for a certain level of animal productive performance. Therefore, feeding systems should be able to predict voluntary feed intake and to partition nutrients into different productive functions and performances. In the last decades, several feeding systems for goats have been developed. The objective of this paper is to compare and evaluate the main goat feeding systems (AFRC, CSIRO, NRC, and SRNS), using data of individual growing goat kids from seven studies conducted in Brazil. The feeding systems were evaluated by regressing the residuals (observed minus predicted) on the predicted values centered on their means. The comparisons showed that these systems differ in their approach for estimating dry matter intake (DMI) and energy requirements for growing goats. The AFRC system was the most accurate for predicting DMI (mean bias = 91 g/d, P < 0.001; linear bias 0.874). The average ADG accounted for a large part of the bias in the prediction of DMI by CSIRO, NRC, and, mainly, AFRC systems. The CSIRO model gave the most accurate predictions of ADG when observed DMI was used as input in the models (mean bias 12 g/d, P < 0.001; linear bias -0.229). while the AFRC was the most accurate when predicted DMI was used (mean bias 8g/d. P > 0.1; linear bias -0.347). (C) 2011 Elsevier B.V. All rights reserved.
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This work proposes hardware architecture, VHDL described, developed to embedded Artificial Neural Network (ANN), Multilayer Perceptron (MLP). The present work idealizes that, in this architecture, ANN applications could easily embed several different topologies of MLP network industrial field. The MLP topology in which the architecture can be configured is defined by a simple and specifically data input (instructions) that determines the layers and Perceptron quantity of the network. In order to set several MLP topologies, many components (datapath) and a controller were developed to execute these instructions. Thus, an user defines a group of previously known instructions which determine ANN characteristics. The system will guarantee the MLP execution through the neural processors (Perceptrons), the components of datapath and the controller that were developed. In other way, the biases and the weights must be static, the ANN that will be embedded must had been trained previously, in off-line way. The knowledge of system internal characteristics and the VHDL language by the user are not needed. The reconfigurable FPGA device was used to implement, simulate and test all the system, allowing application in several real daily problems
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Recent studies have demonstrated that sheath dynamics in plasma immersion ion implantation (PIII) is significantly affected by an external magnetic field, especially in the case when the magnetic field is parallel to the workpiece surface or intersects it at small angles. In this work we report the results from two-dimensional, particle-in-cell (PIC) computer simulations of magnetic field enhanced plasma immersion implantation system at different bias voltages. The simulations begin with initial low-density nitrogen plasma, which extends with uniform density through a grounded cylindrical chamber. Negative bias voltage is applied to a cylindrical target located on the axis of the vacuum chamber. An axial magnetic field is created by a solenoid installed inside the target holder. A set of simulations at a fixed magnetic field of 0.0025 T at the target surface is performed. Secondary electron emission from the target subjected to ion bombardment is also included. It is found that the plasma density around the cylindrical target increases because of intense background gas ionization by the electrons drifting in the crossed E x B fields. Suppression of the sheath expansion and increase of the implantation current density in front of the high-density plasma region are observed. The effect of target bias on the sheath dynamics and implantation current of the magnetic field enhanced PIII is discussed. (C) 2007 Elsevier B.V. All rights reserved.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Purpose: This study evaluated possible publication bias and its related factors in implant-related research over time. Materials and Methods: Articles published in Clinical Implant Dentistry and Related Research, Clinical Oral Implants Research, Implant Dentistry, Journal of Oral Implantology, and The International Journal of Oral & Maxillofacial Implants between 2005 and 2009 were reviewed. Nonoriginal articles were excluded. For each article included, study outcome, extramural funding source, type of study, and geographic origin were recorded. Descriptive and analytic statistics (alpha = .05), including the chi-square test and logistic regression analysis, were performed where appropriate. Results: From a total of 2,085 articles, 1,503 met the inclusion criteria. of the articles analyzed, 1,226 (81.6%), 160 (10.6%), and 117 (7.8%) articles reported positive, negative, and neutral outcomes, respectively. In vitro studies, studies from Asia, and funded animal studies were more likely to report positive outcomes compared to others (P = .02, P < .0001, and P = .009, respectively). Industry-funded studies represented the lowest frequency of positive outcomes versus studies funded by other sources. Conclusions: There were a high number of implant-related studies reporting positive outcomes in the five selected journals. Some selected factors were associated with positive outcome bias. In general, funding was not associated with a positive outcome, except for animal studies. Industry-supported research did not show any association with the publication of positive outcomes. INT J ORAL MAXILLOFAC IMPLANTS 2011;26:1024-1032
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Several runs of the BEAM4 model were carried out, combining several sets of input parameters from von Bertalanffy's growth curve (Lt = L-x[1 - e(-k(-to))]) and the natural mortality (M), with sets or parameters from the length-weight relationship (W=aL(b)). Further simulations were made with variations of +/- 20%, in the input parameters: recruitment (R), catchability coefficient (q), and the lengths at which 50 and 75% of the fishes are retained by the net (L-50%, and L-75%). The data used were those of the pair trawl fisheries for corvina Micropogonias furnieri, off southeastern Brazil. Results showed variations in the output (landed weight) ranging from - 62 to 147% associated with the diverse sets of VBGF and LWR parameters, and lower variations associated with the other input parameters tested. (C) 2001 Elsevier B.V. B.V. All rights reserved.