A general impossibility result on strategy-proof social choice hyperfunctions

A social choice hyperfunction picks a non-empty set of alternatives at each admissible preference profile over sets of alternatives. We analyze the manipulability of social choice hyperfunctions. We identify a domain D^λ of lexicographic orderings which exhibits an impossibility of the Gibbard–Satterthwaite type. Moreover, this impossibility is inherited by all well-known superdomains of D^λ. As most of the standard extension axioms induce superdomains of D^λ while social choice correspondences are particular social choice hyperfunctions, we are able to generalize many impossibility results in the literature.     

The power of dimensional analysis in production systems design

Dimensional Analysis (DA) is a well-known methodology in physics, chemistry and other traditional engineering areas. In its simplest form, DA is used to check the meaningfulness of a set of equations (dimensional homogeneity). In the last century, the dimensional theory has been profoundly investigated: its highest achievement is the Buckingham theorem (or pi-theorem), which states that any equation modelling a physical problem can be rearranged in terms of dimensionless ratios, thus saving variables to be handled, and especially enriching the inner physical knowledge of the studied phenomenon.In this paper we investigate how DA can be applied to Operations Management (OM) topics and which benefits it can bring to researchers in this area. A literature review is performed to clarify the main operative issues regarding DA application (assumptions and limitations); then existing applications of DA to OM are explored, pointing out that few researchers have tried to apply this methodology in the OM research field.Stemming from this analysis, we applied the pi-theorem to the design of a Flexible Manufacturing System. A complex problem, requiring 13 dimensional quantities to be expressed, is first studied via simulation; then DA is applied, reducing the number of variables to 9 dimensionless ratios. The reduced problem has a suitable size to be analytically explored and a regression model is formulated which, compared with the simulation study, offers the same precision in analysing the FMS behaviour, being more compact and powerful. This application shows the potential of DA in OM research, and will hopefully draw the attention of researches to this powerful, but unfamiliar and therefore neglected, methodology.     

一类泛函脉冲微分方程周期正解的存在性

利用Krasnoselskii不动点定理,讨论一类带参数的泛函脉冲微分方程多个周期正解存在的充分条件,在f(x)和I_k(x) 均为非超线性和非次线性的条件下,得到该类微分方程多个周期正解存在的一些新结果。     

Information Disclosure as Environmental Regulation: A Theoretical Analysis

Governments around the world are beginning to embrace a new form of environmental regulation – mandatory disclosure of information. While information disclosure programs appear to have an impact on subsequent firm behavior – often resulting in lower levels of pollution – little is known about the costs and benefits of these programs and whether or not they enhance social welfare. This paper presents a simple bargaining model where mandatory information disclosure is used to overcome a lack of information on the part of the public. We characterize the conditions under which information disclosure will lead to a reduction in emissions, and ultimately, the conditions under which it will enhance social welfare. Several extensions of the model are briefly explored, including the effect of two sources of pollution – only one of which is subject to information disclosure. This paper was prepared while V. Santhakumar was a Visiting Scholar at the Vanderbilt Center for Environmental Management Studies, Vanderbilt University.     

Turbo码的Taylor-Log_MAP译码算法

以Turbo码基本理论和算法为基础,依据无线信息传输的实际要求和Taylor级数的基本原理,提出了一种Turbo码的Taylor-Log-MAP高效译码算法。该算法对基本的Log-MAP算法中K运算利用Taylor级数进行展开,针对实际的信道需求对展开式进行截断,实现了Turbo码的最佳译码。与传统的对数域最大后验概率译码算法相比,该算法基本保持了优良的译码性能,同时避免了复杂的对数运算,减小了运算量。仿真结果表明,与现有的RS码性能相比,使用Turbo码可以获取5 dB的信噪比增益。     

MC's for MCMC'ists

We develop a minimum amount of theory of Markov chains at as low a level of abstraction as possible in order to prove two fundamental probability laws for standard Markov chain Monte Carlo algorithms:
1. The law of large numbers explains why the algorithm works: it states that the empirical means calculated from the samples converge towards their "true" expected values, viz. expectations with respect to the invariant distribution of the associated Markov chain (=the target distribution of the simulation).
2. The central limit theorem expresses the deviations of the empirical means from their expected values in terms of asymptotically normally distributed random variables. We also present a formula and an estimator for the associated variance.     

An improved bound for the Shapley–Folkman theorem

We provide an up to 30% improvement in the Shapley–Folkman theorem error-bound, and briefly discuss its consequences for the course allocation problem.     

The Generalized Composite Commodity Theorem and Food Demand Estimation

This article reports tests of aggregation over consumer food products and estimates of aggregate food demand elasticities. Evidence that food demand variables follow unit root processes leads us to build on and simplify existing tests of the Generalized Composite Commodity Theorem. We compute food demand elasticities using a method of cointegration that is shown to apply to a convenient but nonlinear functional form. Estimates are based on consumer reported expenditure data rather than commercial disappearance data.