公共决策失误的根源探析

剖析公共决策失误,可从决策因素、决策过程和决策体制三个方面入手。决策因素包含决策主体和决策情境,前者构成决策的价值取向,后者则提供决策的事实前提;造成决策失误的主体因素,主要是主体利益与主体价值观;在决策过程中,决策问题的界定出现偏差、决策目标确立不当、决策方案脱离实际、方案选择缺乏科学性等可造成决策失误;从公共决策体制来看,集权决策体制比分权决策体制、个人决策模式特别是个人专断的决策体制比集体决策模式发生决策失误的可能性要大,在分权决策体制和集体决策模式中,因决策程序不当也可造成决策失误。     

现代化发展的失衡与危机--阿根廷"新自由主义"改革的再思考

1989-2001年阿根廷的"新自由主义"改革历程,证明了经济与政治的不均衡发展是导致发展中国家陷入现代化困境的重要因素之一;经济现代化具有天然的不均衡性,国家必须实现政治发展,以政治上的均衡手段,即通过提供更加完整、更加充盈、公正的均衡性公共物品,消除或减轻经济现代化 的不均衡后果,引导经济现代化的正确发展方向.     

我国现代美育发展历程及其启示

在大力推进素质教育的今天,美育的重要性被越来越认可和重视。文章梳理了我国20世纪美育发展历程及其特征,并针对其中存在的美育研究的理想色彩与教育、心理研究的片面性以及美学界与教育学界、心理学界缺乏共识等问题,提出未来美育研究需关注或改进的地方。     

基于SEM-AHP方法的核电厂定量人为因素评价研究

文章基于人为因素分析与分类系统,分析了115起核电厂执照运行事件报告(Licensed Operation Event Report, LOER),并对影响核电厂事件发生的人为因素进行分类,采用结构方程模型方法得出各影响因素的路径系数,两两比较得到层次分析模型的判断矩阵,进而确定各因素的权重。同时对核电厂人为因素的重要性做排序分析,找到核电厂运行过程中的安全管理薄弱环节,从而促进核电厂系统平稳运行。     

论我国违宪审查程序

我国宪法与立法法对违宪审查程序已有原则性规定,而法规备案审查工作程序、司法解释备案审查工作程序的修订增加了违宪审查的工作程序,使我国违宪审查开始具有可操作性。但是我国违宪审查程序仍有待进一步完善。     

互联网之火：网络舆情和时代焦虑 ——从南京的突发事件谈起

社会转型时期各种矛盾凸显激化,以门为表征的突发事件集中爆发。这其中,互联网既是真相披露者,也是情绪煽动者;既有为建构所必须的解构,也有勇猛不能掩饰的粗暴。十几年来,互联网不断颁布新的尺度,强迫传统媒体调整竞合,从而逐渐形成新的传播语境,宣告新的价值体系。在新语境下,精英与草根的位格关系和互动结构,都发生了颠覆式的变化。他们之间的话语冲突源自于精英对网络的隔膜和草根的群体盲目,这种双重误区,使得社会焦虑指数交替升级,政府与群众、草根与精英、中心与边缘之间不断发生进退博弈,比照自适。网络话语的冲突也正是时代焦虑的媒体映像,网络舆情既是民意的自然体现,也可为官方决策提供参考。     

论信息系统——ERP项目监理

信息系统工程监理在我国目前还是一个新生事物,无论是在理论研究上,还是在社会实践中,都还处于一个探索和起步阶段.要真正发挥ERP工程监理的作用,解决ERP成功率不高的问题,必须按照ERP工程的特点,建立相应的监理机制和实施模式.文章力图从ERP项目的特点和存在的问题出发,探讨ERP项目监理的有关问题.     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

Estimation of geodesic tortuosity and constrictivity in stationary random closed sets

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths, whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real data sets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multiphase material that is incorporated in solid oxide fuel cells.     

Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point

Recent work on point processes includes studying posterior convergence rates of estimating a continuous intensity function. In this article, convergence rates for estimating the intensity function and change‐point are derived for the more general case of a piecewise continuous intensity function. We study the problem of estimating the intensity function of an inhomogeneous Poisson process with a change‐point using non‐parametric Bayesian methods. An Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain estimates of the intensity function and the change‐point which is illustrated using simulation studies and applications. The Canadian Journal of Statistics 47: 604–618; 2019 © 2019 Statistical Society of Canada