The provision of relative performance feedback information: An experimental analysis of performance and happiness

This paper studies the effect of providing relative performance feedback information onindividual performance and on individual affective response, when agents are rewardedaccording to their absolute performance. In a laboratory set-up, agents perform a realeffort task and when receiving feedback, they are asked to rate their happiness, arousaland feeling of dominance. Control subjects learn only their absolute performance, whilethe treated subjects additionally learn the average performance in the session.Performance is 17 percent higher when relative performance feedback is provided.Furthermore, although feedback increases the performance independent of the content(i.e., performing above or below the average), the content is determinant for theaffective response. When subjects are treated, the inequality in the happiness and thefeeling of dominance between those subjects performing above and below the averageincreases by 8 and 6 percentage points, respectively.

Biplots of fuzzy coded data

A biplot, which is the multivariate generalization of the two-variable scatterplot, can be used to visualize the results of many multivariate techniques, especially those that are based on the singular value decomposition. We consider data sets consisting of continuous-scale measurements, their fuzzy coding and the biplots that visualize them, using a fuzzy version of multiple correspondence analysis. Of special interest is the way quality of fit of the biplot is measured, since it is well-known that regular (i.e., crisp) multiple correspondence analysis seriously under-estimates this measure. We show how the results of fuzzy multiple correspondence analysis can be defuzzified to obtain estimated values of the original data, and prove that this implies an orthogonal decomposition of variance. This permits a measure of fit to be calculated in the familiar form of a percentage of explained variance, which is directly comparable to the corresponding fit measure used in principal component analysis of the original data. The approach is motivated initially by its application to a simulated data set, showing how the fuzzy approach can lead to diagnosing nonlinear relationships, and finally it is applied to a real set of meteorological data.

Relative payoffs and happiness: An experimental study

Some current utility models presume that people are concerned with their relative standing in a reference group. If this is true, do certain types care more about this than others? Using simple binary decisions and self-reported happiness, we investigate both the prevalence of ``difference aversion'' and whether happiness levels influence the taste for social comparisons. Our decision tasks distinguish between a person s desire to achieving the social optimum, equality or advantageous relative standing. Most people appear to disregard relative payoffs, instead typically making choices resulting in higher social payoffs. While we do not find a strong general correlation between happiness and concern for relative payoffs, we do observe that a willingness to lower another person s payoff below one s own (competitive preferences) seems correlated with unhappiness.

What form of relative performance evaluation?

We study relative performance evaluation in executive compensation whenexecutives have private information about their ability. We assume that thejoint distribution of an individual firm s profit and market movements dependson the ability of the executive that runs the firm. In the equilibrium of theexecutive labor market, compensation schemes exploit this fact to sortexecutives of di ?erent abilities. This implies that executive compensation isincreasing in own performance, but may also be increasing in industryperformance-a sharp departure from standard relative performance evaluation.This result provides an explanation for the scarcity of relative performanceconsiderations in executive compensation documented by the empirical literature.

A wavelet-based method for the determination of the relative resolution between remotely sensed images

Spatial resolution is a key parameter of all remote sensing satellites and platforms. The nominal spatial resolution of satellites is a well-known characteristic because it is directly related to the area in ground that represents a pixel in the detector. Nevertheless, in practice, the actual resolution of a specific image obtained from a satellite is difficult to know precisely because it depends on many other factors such as atmospheric conditions. However, if one has two or more images of the same region, it is possible to compare their relative resolutions. In this paper, a wavelet-decomposition-based method for the determination of the relative resolution between two remotely sensed images of the same area is proposed. The method can be applied to panchromatic, multispectral, and mixed (one panchromatic and one multispectral) images. As an example, the method was applied to compute the relative resolution between SPOT-3, Landsat-5, and Landsat-7 panchromatic and multispectral images taken under similar as well as under very different conditions. On the other hand, if the true absolute resolution of one of the images of the pair is known, the resolution of the other can be computed. Thus, in the last part of this paper, a spatial calibrator that is designed and constructed to help compute the absolute resolution of a single remotely sensed image is described, and an example of its use is presented.

Influence of configurational atomic order on the relative stability of bcc and close-packed structures in Cu-based alloys

We present a study of the influence of atomic order on the relative stability of the bcc and the 18R martensitic structures in a Cu2.96Al0.92Be0.12 crystal. Calorimetric measurements have shown that disorder increases the stability of the 18R phase, contrary to what happens in Cu-Zn-Al alloys for which it is the bcc phase that is stabilized by disordering the system. This different behavior has been explained in terms of a model recently reported. We have also proved that the entropy change at the martensitic transition is independent of the state of atomic order of the crystal, as predicted theoretically. Our results suggest that differences in the vibrational spectrum of the crystal due to different states of atomic order must be equal in the bcc and in the close-packed phases.

Application of fuzzy-rule-based postprocessing to correlation methods in pattern recognition

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

A fuzzy-based decision model application on strategic management

In this article, the objective is to demonstrate the effects of different decision styles on strategic decisions and likewise, on an organization. The technique that was presented in the study is based on the transformation of linguistic variables to numerical value intervals. In this model, the study benefits from fuzzy logic methodology and fuzzy numbers. This fuzzy methodology approach allows us to examine the relations between decision making styles and strategic management processes when there is uncertainty. The purpose is to provide results to companies that may help them to exercise the most appropriate decision making style for its different strategic management processes. The study is leaving more research topics for further studies that may be applied to other decision making areas within the strategic management process.

Lattices for 3-Dimensional Fuzzy Data generated by Fuzzy Galois Connections

Vagueness and high dimensional space data are usual features of current data. The paper is an approach to identify conceptual structures among fuzzy three dimensional data sets in order to get conceptual hierarchy. We propose a fuzzy extension of the Galois connections that allows to demonstrate an isomorphism theorem between fuzzy sets closures which is the basis for generating lattices ordered-sets

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

The genomic distribution of intraspecific and interspecific sequence divergence of human segmental duplications relative to human/chimpanzee chromosomal rearrangements

Background: It has been suggested that chromosomal rearrangements harbor the molecular footprint of the biological phenomena which they induce, in the form, for instance, of changes in the sequence divergence rates of linked genes. So far, all the studies of these potential associations have focused on the relationship between structural changes and the rates of evolution of single-copy DNA and have tried to exclude segmental duplications (SDs). This is paradoxical, since SDs are one of the primary forces driving the evolution of structure and function in our genomes and have been linked not only with novel genes acquiring new functions, but also with overall higher DNA sequence divergence and major chromosomal rearrangements.Results: Here we take the opposite view and focus on SDs. We analyze several of the features of SDs, including the rates of intraspecific divergence between paralogous copies of human SDs and of interspecific divergence between human SDs and chimpanzee DNA. We study how divergence measures relate to chromosomal rearrangements, while considering other factors that affect evolutionary rates in single copy DNA. Conclusion: We find that interspecific SD divergence behaves similarly to divergence of single-copy DNA. In contrast, old and recent paralogous copies of SDs do present different patterns of intraspecific divergence. Also, we show that some relatively recent SDs accumulate in regions that carry inversions in sister lineages.

Using the optical flow to implement a relative virtual mouse controlled by head movements

The following paper introduces the work conducted to create a relative virtual mouse based on the interpretation of head movements and face gesture through a low cost camera and the optical flow of the images. This virtual device is designed specifically as an alternative non-contact pointer for people with mobility impairments in the upper extremities and reduced head control. The proposed virtual device was compared with a conventional mouse, a touchpad and a digital joystick. Validation results show performances close to a digital joystick but far away from a conventional mouse.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.