The Delphi technique as a forecasting tool: issues and analysis

This paper systematically reviews empirical studies looking at the effectiveness of the Delphi technique, and provides a critique of this research. Findings suggest that Delphi groups outperform statistical groups (by 12 studies to two with two ‘ties’) and standard interacting groups (by five studies to one with two ‘ties’), although there is no consistent evidence that the technique outperforms other structured group procedures. However, important differences exist between the typical laboratory version of the technique and the original concept of Delphi, which make generalisations about ‘Delphi’ per se difficult. These differences derive from a lack of control of important group, task, and technique characteristics (such as the relative level of panellist expertise and the nature of feedback used). Indeed, there are theoretical and empirical reasons to believe that a Delphi conducted according to ‘ideal’ specifications might perform better than the standard laboratory interpretations. It is concluded that a different focus of research is required to answer questions on Delphi effectiveness, focusing on an analysis of the process of judgment change within nominal groups.     

统计过程技术在质量控制中的应用

首先介绍了统计过程控制(简称SPC)的概念,并分析了其在过程质量控制中的重要作用。接着着重阐述了SPC的核心监控工具——控制图及其使用程序。最后提出了企业在实施SPC技术应遵从的步骤以及在实施过程中应注意的问题。     

统计规律与马克思主义基本原理方法的选择

统计规律是社会学中常常运用到的逻辑概念,将社会学中的逻辑概念借鉴到对马克思主义的研究中,并借此来探讨研究马克思主义基本原理是思辨还是实证的过程有助于我们更深入地把握马克思主义理论中的基本原理,把握正确的世界观和方法论,更好地指导我们走中国特色社会主义的道路。     

A Paradigm for Masking (Camouflaging) Information

This is an expository paper. Here we propose a decision-theoretic framework for addressing aspects of the confidentiality of information problems in publicly released data. Our basic premise is that the problem needs to be conceptualized by looking at the actions of three agents: a data collector, a legitimate data user, and an intruder. Here we aim to prescribe the actions of the first agent who desires to provide useful information to the second agent, but must protect against possible misuse by the third. The first agent is under the constraint that the released data has to be public to all; this in some societies may not be the case.
A novel aspect of our paper is that all utilities—fundamental to decision making—are in terms of Shannon's information entropy. Thus what gets released is a distribution whose entropy maximizes the expected utility of the first agent. This means that the distribution that gets released will be different from that which generates the collected data. The discrepancy between the two distributions can be assessed via the Kullback–Leibler cross-entropy function. Our proposed strategy therefore boils down to the notion that it is the information content of the data, not the actual data, that gets masked. Current practice of "statistical disclosure limitation" masks the observed data via transformations or cell suppression. These transformations are guided by balancing what are known as "disclosure risks" and "data utility". The entropy indexed utility functions we propose are isomorphic to the above two entities. Thus our approach provides a formal link to that which is currently practiced in statistical disclosure limitation.     

What executives get wrong about statistics: Moving from statistical significance to effect sizes and practical impact

Statistical significance functions as an arbiter of sorts for data analysis purporting to show a relationship between two or more variables. Unfortunately, in far too many situations, statistical significance may lead decision-makers relying on data and analytics to improve business decisions astray, particularly in the context of big data. In this article, I outline reasons why executives should develop a healthy discernment when they see the phrase “statistically significant” in media outlets, internal analyses, consulting reports, and other sources. To overcome the limitations of focusing on statistical significance, I propose executives shift their attention toward the effect size reported from a statistical model. While not without limitation, effect sizes are more useful to decision-makers, highlight the practical implication of analyses, and help in quantifying the uncertainty inherent to working with data.     

A new approach for detecting shifts in forecast accuracy

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. However, commonly-used statistical procedures implicitly place a lot of weight on type I errors (or false positives), which results in a relatively low power of the tests to identify forecast breakdowns in small samples. We develop a procedure which aims to capture the policy cost of missing a break. We use data-based rules to find the test size that optimally trades off the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, although often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting, but also increases the test power. As a result, we can tailor our choice of the critical values for each series not only to the in-sample properties of each series, but also to the way in which the series of forecast errors covary.     

Determination of indicators and standards for tourism impacts in protected Karaj River, Iran

This study was conducted with the main objective of determining the indicators and standards for the impact of tourism on the protected Karaj River in Tehran Province, Iran. The survey was carried out during the visitors’ peak season at several recreational areas along the protected Karaj River. Discriminative analysis was used to differentiate between the sites in terms of visitor satisfaction. The indicators were as follows: trash in the water, trash along the river banks, water quality, number of visitors, visitors’ distance to river, and visitors’ awareness of indicators. The following standards are suggested to improve the management of the site, including less than 12 percent of trash (both on the water surface and river banks), a family biotic index of less than five, less than 625 individuals per hectare, settlement points of more than 9 m from the river and a score of more than 32 on the scale of visitors’ awareness of potential impacts.     

中国外贸增长质量的评价指标构建与实证研究

我国外贸高速增长主要是靠数量扩张实现的,这种粗放式增长方式已难以为继,提高我国外贸增长质量迫在眉睫。然而,目前国内还没有一套评价指标体系可以对外贸增长质量进行全面、客观的评价。这样,提高外贸增长质量就容易成为一个空洞的概念。有鉴于此,本文建立了一套外贸增长质量的评价指标体系,并在此基础上构建了实证分析模型,对我国外贸增长质量及其影响因素进行分析。本文的研究表明,20世纪90年代以来,我国外贸增长质量总体呈现不断改善的趋势;技术进步、人力资本、制度创新与产业结构对我国外贸增长质量有积极影响。     

Is your C-suite risk literate?

Managing risk effectively is essential for business success, and thus understanding risk is vital for business executives. Effective risk management increases firm value, whereas poor risk management will damage shareholder wealth. In this article, we examine the risk literacy of business executives and find significant variation among the leaders we sample. Our findings suggest that business executives would be well served to evaluate their own risk literacy, and remediate, if necessary; to work to increase risk literacy in the company’s employees; and to include risk literacy as an important and routine part of the firm’s overall culture. Because the importance of risk literacy increases with business complexity, we believe that risk literacy will only become more important in the future.     

基于财务指标的中小企业信用评估研究--以大连市为例

基于相关文献回顾的基础上,选取反映中小企业盈利性、偿债性、营运性和成长性的13个财务指标,采用因子分析法拟合为4个公共因子,通过因子载荷建立了信用水平综合得分函数,结果表明,样本企业中大冷股份、时代万恒和大橡塑的信用状况不甚理想,需引起关注。最后,从企业、银行和政府等方面对防范中小企业信用风险及加强信用水平建设提出了对策建议。