Myopia,amnesia, and consistent intertemporal choice

Social scientists often explain observed streams of behavior as the outcome of constrained choice at successive dates, with actors choosing among planned sequences of actions extending into the future. This paper presents four axioms for consistent intertemporal choice in such models. Within this framework, myopia and amnesia are defined to be choice processes in which future feasible actions and past decisions, respectively, play no role in current choice. Each of these modeling strategies is argued to have some plausibility on the basis of bounded rationality considerations. It is shown that an anticipatory process reduces to a behaviorally equivalent myopic process if and only if binary choices are lexically organized. Amnesia, or the absence of endogenous preference formation, is characterized by the condition that binary choices be ‘ahistorical’. The paper closes with some remarks on the methodological role of the consistency axioms, and the problems posed by inconsistent choice processes.     

A Mössbauer study of hyperfine interaction in the systems Co x Mn3−x−y Fe y O4 and Ni x Mn3−x−y Fe y O4

A Mössbauer study of systems Co _x Mn_3?x?y Fe _y O₄ and Ni _x Mn_3?x?y Fe _y O₄ for values ofx=0·1, 0·5, 1·0 andy ranging from 0·1 to 2·0 in steps of 0·2 have been made. At room temperature samples fory values ranging in between 0·1 to 0·5 exhibit paramagnetic behaviour while all spectra for values ofy between 0·6 to 0·8 show relaxation effects. Well-defined hyperfine Zeeman spectra are observed for all the samples withy>0·8 and resolved in two sextets corresponding to octahedral and tetrahedral site symmetries and a central doublet probably due to the presence of super-paramagnetic particles in the system. The hyperfine field at⁵⁷Fe nucleus reduces with decreasing iron cobalt and nickel concentration. These observations have been explained in terms of site preference of cations and exchange interactions.     

Distributionally robust inference for extreme Value-at-Risk

Under general multivariate regular variation conditions, the extreme Value-at-Risk of a portfolio can be expressed as an integral of a known kernel with respect to a generally unknown spectral measure supported on the unit simplex. The estimation of the spectral measure is challenging in practice and virtually impossible in high dimensions. This motivates the problem studied in this work, which is to find universal lower and upper bounds of the extreme Value-at-Risk under practically estimable constraints. That is, we study the infimum and supremum of the extreme Value-at-Risk functional, over the infinite dimensional space of all possible spectral measures that meet a finite set of constraints. We focus on extremal coefficient constraints, which are popular and easy to interpret in practice. Our contributions are twofold. First, we show that optimization problems over an infinite dimensional space of spectral measures are in fact dual problems to linear semi-infinite programs (LSIPs) – linear optimization problems in Euclidean space with an uncountable set of linear constraints. This allows us to prove that the optimal solutions are in fact attained by discrete spectral measures supported on finitely many atoms. Second, in the case of balanced portfolia, we establish further structural results for the lower bounds as well as closed form solutions for both the lower- and upper-bounds of extreme Value-at-Risk in the special case of a single extremal coefficient constraint. The solutions unveil important connections to the Tawn–Molchanov max-stable models. The results are illustrated with two applications: a real data example and closed-form formulae in a market plus sectors framework.     

需求可变性降低对均值CVaR约束销售努力决策库存系统的影响

本文运用Levy提出的变换研究需求可变性降低对风险偏好零售商的库存决策、销售努力决策和期望效用的影响,用均值CVaR刻画零售商的风险偏好特性,它包括风险厌恶、风险追求,也具有损失规避的特性。首先,运用该变换定量刻画需求可变性的降低,证明该变换蕴含经典随机占优中的割准则序和二阶随机占优等。其次,给出系统的最优订货量、最优期望效用和最优销售努力水平,得到它们关于风险偏好系数的单调性,并给出降低需求可变性对期望效用的影响。第三,针对风险中性、风险厌恶(最大化CVaR)和风险追求(最小化CVaR)这三种特殊情况得到相应的结果,并给出企业在库存决策和促销决策的管理启示。最后,通过数值例子验证了得到的研究结果并给出相应的管理启示。     

回报型众筹定价模型

构建了众筹融资、向支持者提供产品和向普通消费者出售产品的回报型众筹三阶段模型,研究兼顾增加发起人收益与提高项目众筹成功率的发起人定价决策,并运用比较静态方法探讨了发起人给支持者提供的增值服务和投资者的消费耐心程度因素对发起人定价决策的影响。研究表明,发起人与消费者是一个关于价格和效用的动态博弈过程,并最终达到纳什均衡;支持者所得的增值服务和消费者消费的时间偏好在改善发起人收益与项目众筹成功率之间并不协同,因此发起人的产品定价和筹资目标设定应折中设置在一个合理的范围。结论为发起人合理的产品定价和项目筹资成功率的提高提供一定理论依据和实践参考。     

考虑路径风险的需求不确定应急物流定位-路径问题

针对自然灾害对路网造成的破坏性和受灾点物资需求量的不确定性,综合考虑应急物流路径风险和物资分配的时间公平性,将路径风险定量化为路径通行能力风险、路径损毁风险和路径复杂性,基于鲁棒优化理论,以最小化车辆最长配送时间和应急物流系统总成本为目标,建立了双目标多物资定位-路径优化模型,采用遗传算法对其求解,以汶川地震应急救援相关数据为例进行了数据仿真实验。实验结果的对比分析表明:在考虑路径风险下,随着控制系数的增加,系统总成本和最长配送时间目标值均呈现增大的趋势;受灾点的物资需求扰动越大,构建应急物流系统的总成本越高。算例结果也验证了鲁棒优化方法在处理不确定需求方面的有效性,为解决自然灾害后应急设施点的开设和救援物资的安全及时准确配送提供了有效的方法。     

鲁棒半监督ν-支持向量分类机

支持向量机在近十年成为机器学习的主要学习技术,而且已经成功应用到有监督学习问题中。Fung和Mangasarian利用支持向量机对于既有已标类别样本又有未知类别样本的训练集进行训练,方法主要是利用少量已标明类别的样本进行训练得到一个分类器的同时对于未标明类别的样本进行分类,使得间隔最大化。此优化问题中假定样本是精确的,而在现实生活中,样本通常带有统计误差。因此,考虑样本带有扰动信息的半监督两类分类问题,给出鲁棒半监督v-支持向量分类算法。该算法的参数v易于选择,而数值试验也表明该算法具有良好的稳定性和较好的分类结果。     

调剂问题的Vague指派方法研究

调剂工作是硕士研究生招生录取过程中一项非常重要的工作.论文以硕士研究生调剂工作为例,通过分析基本特征,将其转化为指派问题进行研究.并借助于Vague集思想,运用面试投票的方法获得效益矩阵.根据评价者的不同偏好特征采用记分函数将Vague值转化为实数,得到了实数满意度矩阵.进而不仅实现了学生和录取单位的共赢,也实现了资源的优化配置.该方法一方面为此类问题的解决提供了一种有效途径,另一方面也在理论上找到了Vague指派的一般步骤,通过数据计算保证了该方法在问题解决上的有效性和可行性.     

区间直觉模糊判断矩阵群决策中的逆判问题

研究了区间直觉模糊判断矩阵的群决策问题.定义了两种区间直觉模糊集相似度公式,给出两种与决策群体意见一致性程度最高的理想区间直觉模糊判断矩阵构造优化方法.利用矩阵对不同专家判断矩阵中相同位置元素的一致性进行分析,并对不同专家的判断信息进行整体相似程度分析,最后通过算例说明了该方法的有效性和实用性.