996 resultados para SPECTRAL SUM-RULES


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We extend the classic Merton (1969, 1971) problem that investigates the joint consumption-savings and portfolio-selection problem under capital risk by assuming sophisticated but time-inconsistent agents. We introduce stochastic hyperbolic preferences as in Harris and Laibson (2013) and find closed-form solutions for Merton's optimal consumption and portfolio selection problem in continuous time. We find that the portfolio rule remains identical to the time-consistent solution with power utility and no borrowing constraints. However,the marginal propensity to consume out of wealth is unambiguously greater than the time-consistent, exponential case and,importantly, it is also more responsive to changes in risk. These results suggest that hyperbolic discounting with sophisticated agents offers promise for contributing to explaining important aspects of asset market data.

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In this paper we address several issues related to collective dichotomous decision-making by means of quaternary voting rules, i.e., when voters may choose between four actions: voting yes, voting no, abstaining and not turning up-which are aggregated by a voting rule into a dichotomous decision: acceptance or rejection of a proposal. In particular we study the links between the actions and preferences of the actors. We show that quaternary rules (unlike binary rules, where only two actions -yes or no- are possible) leave room for "manipulability" (i.e., strategic behaviour). Thus a preference profile does not in general determine an action profile. We also deal with the notions of success and decisiveness and their ex ante assessment for quaternary voting rules, and discuss the role of information and coordination in this context.

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Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

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We conduct experiments to investigate the effects of different majority requirements on bargaining outcomes in small and large groups. In particular, we use a Baron-Ferejohn protocol and investigate the effects of decision rules on delay (number of bargaining rounds needed to reach agreement) and measures of "fairness" (inclusiveness of coalitions, equality of the distribution within a coalition). We find that larger groups and unanimity rule are associated with significantly larger decision making costs in the sense that first round proposals more often fail, leading to more costly delay. The higher rate of failure under unanimity rule and in large groups is a combination of three facts: (1) in these conditions, a larger number of individuals must agree, (2) an important fraction of individuals reject offers below the equal share, and (3) proposers demand more (relative to the equal share) in large groups.