A column generation-based approach to solve the preference scheduling problem for nurses with downgrading

The demand for nurses in virtually all western countries has been outpacing the supply for more than a decade. The situation is now at the point where the rules for good practice are being stretched to the limit and patient care is in jeopardy. The purpose of this paper is to present several ideas for maximizing the use of the available staff and to quantify the resultant benefits. Two approaches are investigated for substituting nurses with higher level skills for those with lower level skills when there is sufficient idle time to do so. Idle time is usually due to scheduling constraints and contractual agreements that prevent a hospital from arbitrarily assigning nurses to shifts over the week.When the substitution is skill-related, as it is here, it is often called downgrading. The models that we develop are for preference scheduling, which means that individual preferences are taken into account when constructing monthly rosters. There are several reasons for doing this in today's environment, the most important is the need to boost staff morale and increase retention. The problem is modeled as an integer program and solved with a column generation technique that relies on intelligent heuristics for identifying good candidate schedules. The computations show that high quality solutions, as measured by the reduction in the need for non-unit nurses as well as the degree to which preferences are satisfied, can usually be obtained in a matter of minutes.     

The role of ratio differences in the framing of numerical information

It is well documented that presentation of qualitatively equivalent information in different frames has considerable impacts on consumer preferences and evaluations. In this research, we demonstrate a type of framing effects that targets numerical information of an attribute.In three experiments, we find that the perceived differences between two options loom larger when equivalent attribute information is expressed in a large ratio frame than when it is expressed in a small ratio frame. Moreover, preferences shift toward an option when its superior attribute is presented in a large rather than a small ratio condition. We also show that this framing effect goes beyond the effects of loss aversion and those of numerical anchoring.     

The value of a stochastic information structure

Upon observing a signal, a Bayesian decision maker updates her probability distribution over the state space, chooses an action, and receives a payoff that depends on the state and the action taken. An information structure determines the set of possible signals and the probability of each signal given a state. For a fixed decision problem, the value of an information structure is the maximal expected utility that the decision maker can get when the observed signals are governed by this structure. Thus, every decision problem induces a preference order over information structures according to their value. We characterize preference orders that can be obtained in this way. We also characterize the functions defined over information structures that measure their value.     

商业银行贷款担保偏好行为与信贷交易成本

商业银行具有担保偏好行为,即偏好发放担保贷款.信贷交易存在很高的不确定性.银行为了降低不确定性,有两种方案可供选择:一是商业银行进行较强的筛选、监督和审查活动,获取企业的信息;二是商业银行依靠担保,节约筛选成本、监督成本和审查成本.有限理性的商业银行当然会选择后者.但是,担保行为不能完全替代银行的筛选、监督和审查活动,因为担保品本身也存在诸如价值波动等不确定性.     

参与者公平偏好对协作型众包创新绩效激励机制的影响

协作型众包创新是以最大化创新绩效为目标的众包模式,相关实践案例已有所显现,但协作型众包创新过程中参与方的努力行为经常受到其公平偏好心理的影响。针对这一问题,采用委托代理理论,通过设计仅基于个人创新绩效(AR)和基于总创新绩效(TR)的两种激励机制提升协作型众包创新绩效,并深入探讨公平偏好对激励机制的影响。结果表明,AR机制中参赛者不可能出现利他性努力行为,而TR机制下利己性努力和利他性努力均与公平偏好负相关;两种激励机制下众包创新绩效均随公平偏好敏感度增大而下降,参赛者数量增加是否有利于提升众包创新绩效也取决于公平偏好程度;TR机制一定会产生比AR机制更高的众包绩效及发包方经济收益,但这种价值效应会被参赛者的公平偏好心理所弱化。     

医疗决策情景下医生利他性测量的实验经济学研究

随着实验经济学、行为博弈论的发展,传统经济学中的自利假设受到利他倾向的挑战。利他性在医疗决策中体现为医生行为偏离个人利益最大化,关注患者健康福利,被认为是医生绩效和医疗服务质量的关键决定因素之一,对公益性导向和满足社会公众利益最大化目标的落实具有重要意义。但是利他性难以测量影响了对医生利他性的研究和有效激励机制的建立。本研究基于显示偏好原理并采用独裁者博弈为基础设计的受控实验室实验模拟医生医疗决策情景,根据医生决策结果,分别基于医生效用函数计算和运用随机效用模型估算医生的利他性。结果显示医生决策过程中对患者健康效益给予了正向权重。医生的利他性存在异质性。基于医生效用函数计算的利他性均值为0.82,表明医生对患者健康效益的权重为82%。96%的医生利他性大于0.5,50%的医生利他性大于0.85。运用随机效用模型估算的利他性(患者健康效益与医生利润的边际替代率)为1.078>1,49%的医生边际替代率大于1,说明相比个人利益医生更加重视患者健康效益。两种方法下测量的利他性显著正相关(Spearman’s ρ=0.715, p<0.001)。提示本研究应用的医疗决策情景下利他性的测量方法可实现医生利他性的量化,基于两种方法测量的医生利他性可以相互印证。     

From revealed preference to preference revelation

Utility functions are regarded as elements of a linear space that is paired with a dual representation of choices to demonstrate the similarity between preference revelation and the duality of prices and quantities in revealed preference. With respect to preference revelation, quasilinear versus ordinal utility and choices in an abstract set versus choices in a linear space are distinguished and their separate and common features are explored. The central thread uniting the various strands is the subdifferentiability of convex functions.     

区域服务贸易安排“特惠实质”的政治经济分析

区域服务贸易安排的不断蔓延标志着世界服务贸易的管理模式正在发生变化。本文基于与多边服务贸易自由化安排相对照的视角,对区域服务贸易安排提供的特惠待遇内容及实质展开政治经济分析。研究结果显示,由于区域服务贸易安排内涵着重要的"特惠侵蚀"机制:一是引入"非成员最惠国待遇条款",二是设立相对自由的服务原产地规则,实际付诸实施的区域服务贸易"特惠待遇"远不如根据协定文本测算得那么显著。从"服务"的特殊属性出发,本文指出有一系列的政治经济因素会促使区域服务贸易安排具有更强的将特惠承诺向区外扩展适用的潜在动力。区域服务贸易"特惠待遇"在本质上具有"渗漏性"这点会有助于推进多边服务贸易自由化进程。     

不同激励偏好下创新奖励对研发人员创新行为的影响 ——自我决定与特质激活理论整合视角

基于自我决定理论与特质激活理论整合观点,构建以激励偏好为调节变量、创新自我效能感为中介变量的研究框架,探讨创新奖励对创新行为的影响机制及边界条件。研究表明：创新奖励对创新行为有显著正向影响,创新自我效能感部分中介创新奖励对创新行为的影响;在创新奖励强情境中,内激励偏好、外激励偏好分别正向调节创新奖励对创新自我效能感的影响,且正向调节创新奖励通过创新自我效能感影响创新行为的中介作用。研究揭示了当研发人员普遍认为创新工作内在激励不足时,在不同激励偏好下创新奖励是如何影响创新行为的,并有助于完善创新奖励制度以促进研发人员创新行为。     

组织公平、知识资本开发与技术创新能力的相关性研究——基于高科技中小风险企业的数据检验

组织公平是现代企业发展的潜在性要求,是行为经济理论在管理领域的深化。知识资本是现代企业的主体资本,是企业技术创新能力成长的源泉。组织公平与知识资本开发存在着高度的内在一致性。组织公平环境下的知识资本开发具有更大的成效,进而促进企业技术创新能力的成长。基于高科技中小风险企业的样本数据,经验性的研究揭示了组织公平效应的微观机理,从而为我国企业加强知识资本的开发与技术创新能力的培育提供现实性的理论借鉴。研究结果表明:组织公平环境下的知识资本开发显现出显著的成效,并显著地促进技术创新能力的成长,但在局部激励路径上有待改进。