Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task

We conducted laboratory experiments for analyzing the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) relative to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) who were required to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While we did not find statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets did not outperform FTF for any of the questions and were inferior for three questions. The relative performances of nominal groups and FTF were mixed and the differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value relative to a simple average of the forecasts. In addition, we examined participants’ perceptions of the group and the group process. The participants rated personal communications more favorably than computer-mediated interactions. The group interactions in FTF and nominal groups were perceived as being highly cooperative and effective. Prediction markets were rated least favourably: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

基于主成分分析和支持向量机的个人信用评估

本文针对信用评估指标维数较高的问题,运用主成分分析与支持向量机理论建立了一个新的个人信用评估预测模型。为反映该模型在信用评估分类方面的优越性,又分别建立了基于神经网络、K近邻判别分析等多种理论的信用评估模型,并用同一组数据对不同的模型分别进行训练,然后比较其预测分类正确率。实验结果表明,基于主成分分析与支持向量机理论的个人信用评估模型具有较优的预测分类正确率。     

货代发展战略研究

基于国际互联网的信息技术的飞速发展及当代物流行业的发展和逐步形成,使得传统的国际货代业受到了巨大的挑战和冲击。向现代物流企业转型是我国国际货代企业在竞争中生存和发展的有效途径。在对已有研究分析的基础上,阐述了我国国际货代业的发展现状及存在的问题,提出了我国货代企业转型的三种发展战略,即第三方物流、第四方物流和供应链。研究结果将为我国国际货代企业向现代物流企业转型提供参考。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

预算功能取向与部门预算绩效评价理论导向

基于我国政策层面的近期预算绩效评价体系构建与远期绩效预算改革的战略部署,深度整理美国预算管理改革的轨迹,以经典预算功能取向分析范式为契合点,透视绩效预算与部门预算的内在拟和,演绎提出我国部门预算改革蕴含的"控制核心,绩效导向"的混合功通用取向.基于这种价值取向,引申确立我国面向绩效预算的预算绩效评价理论导向:源于实际的理论起点、面向未来与着眼现实的理论构架、过程控制与结果评价融合的实现体系.     

经贸文体翻译之生命——准确性

实现经贸文体翻译的准确性,必须准确把握词汇含义,理解文化内涵,表现文体风格。     

企业家与企业文化建设

企业家创造了企业积极向上的价值观、经营理念、管理文化、制度文化和行为文化。企业家通过调节、控制、角色示范、挑选员工、任用干部、分配报酬、借用“外脑”等途径和方法 ,传播和增强优秀企业文化。企业家面对环境的变化和知识经济的挑战 ,应把握好企业运行的方向 ,积极地变革企业文化 ,扬弃制约企业创新的行为惯性 ,再造全新的企业 ,从而增强企业文化的竞争优势。     

数控机床加工精度异常故障的诊断与处理

社会的迅速发展推动了现代制造技术的进步,也实现了数控机床的推广。由于其是根据加工程序自动加工零件,因此具有加工精度高、生产效率高、加工功能强等特点。然而在实际应用中,因操作人员对影响数控加工精度的因素不甚了解,在加工中,无法进行高精度施工,影响整个工程的质量。为了保证施工质量,文章根据笔者自身多年的工作经验,结合理论分析,对数控机床加工精度异常的故障,找出有效的的诊断措施及相关的应对措施,望能起到抛砖引玉的效果。     

信息分享对原始设备制造商与其合同制造商竞争的影响

研究了原始设备制造商的预测信息分享对一个原始设备制造商和一个与其同时有合作和竞争的合同制造商组成的供应链系统的影响,建立制造商间信息分享的模型,该模型包括一个原始设备制造商和一个合同制造商。研究发现,原始设备制造商关于市场潜在需求预测信息的分享对其预期利润是不利的,同时需求信息预测的精度对原始设备制造商信息分享的决策也有影响,原始设备制造商没有动机与其供应链成员进行信息分享,但信息分享使得供应链整体利润增加。最后,建立一个信息分享补偿机制分享供应链利润的增加量,以期通过信息分享补偿机制促使原始设备制造商有动机进行信息分享,从而实现其与合同制造商的“双赢”。