非对称信息供应链激励机制模型研究

针对单个制造商和单个零售商所组成的二级供应链,基于市场需求为指数形式,分别建立了对称信息下的供应链模型以及以零售商的单位营销支出为类型的非对称信息激励模型。首先,研究了对称信息情况下的集中控制模型和分散模型的最优解;其次,在对称信息的基础上,运用极值原理得到了激励模型的最优解;最后运用数值仿真对模型进行分析。结果表明,所设置的激励机制对整个供应链的收益有改善作用。     

非对称信息供应链激励机制模型研究

针对单个制造商和单个零售商所组成的二级供应链,基于市场需求为指数形式,分别建立了对称信息下的供应链模型以及以零售商的单位营销支出为类型的非对称信息激励模型。首先,研究了对称信息情况下的集中控制模型和分散模型的最优解;其次,在对称信息的基础上,运用极值原理得到了激励模型的最优解;最后运用数值仿真对模型进行分析。结果表明,所设置的激励机制对整个供应链的收益有改善作用。     

引入供应商的公平偏好的供应链激励契约设计

利用委托代理模型研究了零售商具有公平偏好情况下的二级供应链委托代理问题.在假设零售商具有公平偏好以及零售商的最大努力水平和最小努力水平可预测的前提下,分别设计了对称信息和不对称信息两种情况下的供应链激励契约.     

中小企业融资信息甄别模型的构建及应用

本文以中小企业融资中信息不对称带来的负面影响为切入点,结合信息甄别理论,运用模糊综合评价法构建中小企业融资信息甄别模型并进行实证研究。模型的应用将有助于中小企业摆脱融资困境,并使其进入良性循环。     

中小企业融资信息甄别模型的构建及应用

本文以中小企业融资中信息不对称带来的负面影响为切入点,结合信息甄别理论,运用模糊综合评价法构建中小企业融资信息甄别模型并进行实证研究.模型的应用将有助于中小企业摆脱融资困境,并使其进入良性循环.     

引入自有品牌的供应链激励机制设计研究

针对引入自有品牌的供应链激励机制设计问题,首先在对称信息和非对称信息下建立由两个制造商和一个零售商组成的供应链激励机制模型,接着分析引入自有品牌的供应链激励机制均衡结果,最后对自有品牌引入前后的均衡结果进行比较。通过分析发现,零售商在制造商品牌上的努力水平始终不超过其在自有品牌上的努力水平;在对称信息下,零售商引入自有品牌肯定会使制造商受到损失,而在非对称信息下制造商可能会从中获利。     

引入自有品牌的供应链激励机制设计研究

针对引入自有品牌的供应链激励机制设计问题,首先在对称信息和非对称信息下建立由两个制造商和一个零售商组成的供应链激励机制模型,接着分析引入自有品牌的供应链激励机制均衡结果,最后对自有品牌引入前后的均衡结果进行比较.通过分析发现,零售商在制造商品牌上的努力水平始终不超过其在自有品牌上的努力水平;在对称信息下,零售商引入自有品牌肯定会使制造商受到损失,而在非对称信息下制造商可能会从中获利.     

信息不对称与保险市场失灵问题?

一、保险中存在的信息不对称 信息不对称理论的内容可以概括为三点:(1)有关交易的信息在双方之间的分布是不对称的,即逆向选择模型;(2)交易完成后由于参与人选择错误行动引起的信息不对称,即道德风险模型;(3)交易双方对各自在信息占有方面的相对地位都是清楚的。一般说来,许多经济学家认为隐藏信息的道德风险和信息甄别模型与逆向选择模型是一回事,它们都是由于参与双方对相关信息占有的不对称,导致在交易完成前后分别发生     

存在自营店的供应链薪酬激励机制研究

针对存在自营店的供应链薪酬激励机制设计问题,首先在对称信息和非对称信息下建立了由制造商、零售商及自营店组成的供应链薪酬激励机制模型,然后分析存在自营店的薪酬激励机制设计结果及其信息价值,最后对自营店引入前后的模型结果进行比较。研究表明,在对称信息和非对称信息下,零售商在零售店中的努力水平始终不超过制造商在自营店中的努力水平;制造商通过引入自营店可以获得额外的期望收益,并且产出系数越大、努力成本越低,制造商所获得的额外期望收益越多。     

信息不对称下的供应链契约设计

针对一个供应商与多个独立零售商组成的二级供应链,建立了零售商类型为其私人信息下的Stackelberg博弈模型。分析表明,文中提出的线性转移支付契约不仅可以激励零售商说出其真实类型,而且可以激励零售商积极进行业务流程重组、提高管理效率,增强供应链竞争力;但与信息对称情况下相比,供应商期望利润有所降低,且整个供应链存在效率损失。最后用实例验证了上述结论,并分析了零售商进行流程重组后的管理效率对供应链利润和效率的影响。     

A mathematical model for scheduling screening tests for progressive diseases.

In this paper, a mathematical model describing the stochastic process underlying the etiology of noncontagious, curable, progressive diseases is developed. In our previous study of progressive diseases, the reliabilities of screening tests were assumed to be constants, independent of how long the individual has had the disease. The purpose of this study is to develop more insight into modeling the disease process, and the interaction of screening tests with the natural behavior of the disease. To accomplish this task, more realistic screening parameters are selected, and optimum policies for mass screening programs under different reliabilities of screening tests are developed. These modifications make the model general enough to be applicable to a larger class of progressive diseases that includes breast cancer. Solutions to two simplified versions of the model for childhood neuroblastoma illustrate the use of our proposed procedure.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

When your resume is (not) turning you down: Modelling ethnic bias in resume screening

Resume screening is the first hurdle applicants typically face when they apply for a job. Despite the many empirical studies showing bias at the resume‐screening stage, fairness at this funnelling stage has not been reviewed systematically. In this paper, a three‐stage model of biased resume screening is presented. We first discuss relevant theoretical perspectives (e.g., job market signalling and impression formation theories) to explain why resume screening is vulnerable to biased decision‐making and ethnic discrimination in particular. On the basis of the best available evidence, we consider contingencies of ethnic discrimination in the applicant, the decision‐maker, and the broader context (e.g., organisation), as well as the effectiveness of interventions that might counter ethnic bias in resume screening. The paper ends with a critical agenda for further research and practice.     

Opportunity analysis of newborn screening programs

There exist congenital diseases that reduce newborns’ potential opportunities. This reduction is sometimes alleviated if the congenital disease is detected early by a newborn screening program. We propose a new outcome measurement procedure for newborn screening programs, based on the opportunity gains they offer. We show that, under plausible assumptions, the ranking of the available screening programs for a particular disease, according to this new outcome measurement procedure, does not depend on the metric of opportunity. We also apply our model to the current debate about choosing between a selective or a universal newborn hearing screening program to detect congenital hearing impairments.     

Economic design of a two-stage screening procedure with a prescribed outgoing quality

Summary An economic two-stage screening procedure based on screening and performance variables is proposed. A screening variable is used first to decide whether an item should be accepted, rejected, or undecided. The performance variable is then used to classify the undecided items. The two variables are assumed to have a bivariate normal distribution. A cost model is constructed which involves costs due to screening and performance inspections and misclassification errors. Optimal cutoff values on the screening variable minimizing the expected cost are obtained subject to the constraint that the outgoint quality exceeds a prespecified level. Methods of finding the optimal cutoff values are presented for both parameters-known and parameters-unknown cases.     

Adverse selection,limited compensation,and the design of environmental liability insurance contract in the case of enterprise bankruptcy

In the case of incomplete environmental liability insurance, enterprises are likely to go bankrupt. Rational enterprises generally do not want to insure the environmental loss liability in case of bankruptcy even if they are compensated by insurance companies. This means that the premium calculated now is high, which may be the main reason for the current enterprises to cherish insurance. Based on this, a basic model of environmental liability insurance contract considering bankruptcy under ex ante asymmetric information is established. The model is improved by using the information screening combination tool, which further improves the efficiency of information screening.     

A DISPLACED POISSON PROCESS APPLIED TO SCREENING FOR BREAST CANCER EN STRATIFIED SAMPLES WITH FALSE NEGATIVES*

A displaced Poisson process model allowing for stratified populations and “false negatives” is presented for describing the progress of certain chronic diseases. This model, which builds on earlier work by the authors, allows for the simple estimation of various parameters and distributions of interest in screening. A Monte Carlo simulation study illustrating and partially verifying the model predictions is given.     

On the screening power of incentive schemes

We demonstrate the screening power of incentive schemes. The model is a synthesis of a signaling model and the principal-agent problem. A non-cooperative stage where the agent may send a signal precedes a contractual stage where the agent serves a principal under an incentive scheme. After establishing the existence of a revealing equilibrium in an environment with long-lived principals and overlapping contracts, the paper discusses the implications for efficiency of the distribution of surplus. Finally, it is shown that under commitment the agency relationship may serve self-selection purposes and hence reduce the need of costly signaling.     

Supplier selection in agile supply chains: An information-processing model and an illustration

Agile supply chains need to be highly flexible in order to reconfigure quickly in response to changes in their environment. An effective supplier selection process is essential for this. This paper develops a model that helps overcome the information-processing difficulties inherent in screening a large number of potential suppliers in the early stages of the selection process. Based on radial basis function artificial neural network (RBF-ANN), the model enables potential suppliers to be assessed against multiple criteria using both quantitative and qualitative measures. Its efficacy is illustrated using empirical data from the Chinese electrical appliance and equipment manufacturing industries.     

A priority assignment model for standards and conditions in a long term care survey

Presently a survey document is used in New York State to enforce Federal and State regulatory standards for long term care facilities (skilled nursing facilities and health related facilities). This document is used to establish Medicare and Medicaid certification for these facilities.

This study describes the creation of a new survey process—a first stage consisting of a screening survey followed, if necessary, by an intensive survey. A priority assignment model which utilizes the judgements of experts from the New York State Office of Health Systems Management (OHSM) has been developed. This model is employed to develop ranks, priority weights and classifications for the various standards and the Federal conditions which the standards comprise (as well as equivalent State regulations).

Saaty's Analytical Hierarchy Process is used to obtain the ranks and priority weights. The consistency of the experts' judgements is measured using Kendall's coefficient of agreement. An optimal split algorithm is used to subdivide the standards and conditions according to impact (high, moderate or low) on patient care, health and safety.

The results are to be used as a management tool in developing decision rules for defining the scope of the intensive survey on the basis of screening survey results.