建设工程管理决策中的系统分析方法

管理决策的科学性取决于管理方法、手段和技术的有效性。基于决策问题的系统分析方法是实施科学决策的基础,决策问题的提出是系统科学发展的动因。建设工程管理决策树立系统观点、应用系统方法进行量化和优化是提高其决策质量的有效方法。从系统分析与决策、建设工程管理决策的系统观以及建设选址问题中系统分析方法的应用三个方面理论与实际相结合,阐明了系统量化与优化对于建设工程管理决策的辅助支持作用,以期增强建设工程管理决策活动应用系统方法的自觉性,提高管理决策的质量和能力,促进建筑业的健康良性发展。     

Threshold decisions: how social workers prioritize referrals of child concern

This paper examines local authority social workers' decision‐making when considering referrals of children, where the concerns are on the margin of child protection procedures. In doing so, it describes the findings of a qualitative research study undertaken in the policy context of attempts to ‘refocus’ social work practice in the late 1990s and early twenty‐first century. Data collection involved interviews with social workers and parents in 23 cases. Conclusions are that referrals were evaluated on the basis of five key factors, specificity, severity, risk, parental accountability and corroboration, the use of which determined whether an initial assessment or an investigation of alleged abuse took place. The analysis builds on previous work in the child protection field, but demonstrates how the application of these factors differs between cases of child concern and cases of child protection. Policy implications concern the complexity of decision‐making in the uncertain context of limited referral information and it is proposed that the simplistic notion of a continuum of abuse is now outdated. Copyright © 2006 John Wiley & Sons, Ltd.     

一种基于神经网络和决策树的信用评估新方法

分析了数据挖掘在国内外金融领域的应用及研究现状,提出了一种基于神经网络和决策树相结合的信用评估新方法。该方法通过RBF神经网络,进行条件属性裁减,并利用决策树抽取出评估规则。此方法利用神经网络的“黑箱”工作特性,选择重要条件属性,并利用决策树自动生成评估规则,大大提高了信用评估的效率和客观性。     

基于数据挖掘的客户关系分析评价系统

分析功能的深化是客户关系管理(CRM)的一大发展趋势,应用数据挖掘技术建立的CRM分析评价系统,可以对客户的行为以及市场趋势进行有效的分析,为企业的各种决策提供定性和定量的依据。该系统主要模块包括:客户细分模块、客户行为分析模块和市场趋势分析模块。以上各个模块涉及了企业关心的各种客户行为及市场行为,通过各个模块的分析,企业可以辨认出高端客户,理解各种客户行为模式,并掌握市场发展趋势以保证企业在激烈的竞争中立于不败之地。     

对偶线性规划问题性质探析

探讨对偶线性规划的原始问题与对偶问题的属性，阐述两者的区别和内在联系，用较简便的方法论证其重要性质，揭示可行解与目标函数、可行解与最优解的关系，指出线性规划问题最优解从约束条件较少的对偶问题寻求为另一较简便之方法。     

西方国家高校组织的自主权及其启示

西方国家高校组织的自主权是通过大学内部立法、决策领导、组织协调、咨询等办学活动来实现的。大学自治组织系统主要由董事会 (校务会 )、校长、评议会等组成。校长由董事会 (校务会 )选举或产生 ,代表其行使学校决策及日常事务管理。大学自治组织一般拥有组织教学、筹集经费、开展学术研究、人事使用等权力 ,而这些权力的行使一方面要受到大学内部各种权力系统、管理层以及利益集团的制约 ,同时还要受到来自大学外部的各种专门组织和社会团体的监督     

组合预测误差信息矩阵研究

研究组合预测误差信息矩阵的结构与组合预测方法性质之间的联系,首次提出冗余信息概念,对最优组合预测方法的组合结构进行了研究。     

高等学校专业设置工作刍议

当前,在大规模扩招和扩大办学自主权的背景下,高等学校专业设置的重要性越来越凸现。为切实做好高等学校的专业设置工作,避免盲目性与随意性,需要转变观念,充分论证,组织力量进行“前端设计”;加强科学性与民主性的决策,充分发挥学术权力作用,并正确运用行政权力;同时,在按设计方案进行操作中,要以专业设置工作贯穿于其它各项工作之中进行。     

Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.     

Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.