Capturing preferences for inequality aversion in decision support

We investigate the situation where there is interest in ranking distributions (of income, of wealth, of health, of service levels) across a population, in which individuals are considered preferentially indistinguishable and where there is some limited information about social preferences. We use a natural dominance relation, generalised Lorenz dominance, used in welfare comparisons in economic theory. In some settings there may be additional information about preferences (for example, if there is policy statement that one distribution is preferred to another) and any dominance relation should respect such preferences. However, characterising this sort of conditional dominance relation (specifically, dominance with respect to the set of all symmetric increasing quasiconcave functions in line with given preference information) turns out to be computationally challenging. This challenge comes about because, through the assumption of symmetry, any one preference statement (“I prefer giving $100 to Jane and $110 to John over giving $150 to Jane and $90 to John”) implies a large number of other preference statements (“I prefer giving $110 to Jane and $100 to John over giving $150 to Jane and $90 to John”; “I prefer giving $100 to Jane and $110 to John over giving $90 to Jane and $150 to John”). We present theoretical results that help deal with these challenges and present tractable linear programming formulations for testing whether dominance holds between any given pair of distributions. We also propose an interactive decision support procedure for ranking a given set of distributions and demonstrate its performance through computational testing.     

均值限制相似优势粗糙集及决策分析模型研究

针对不完备信息系统中的偏好多属性决策问题,提出了一种基于均值限制相似优势粗糙集的决策分析模型.首先提出了均值限制相似优势关系的概念;然后在均值限制相似优势关系下得到知识的粗糙近似和属性约简,给出了分类决策规则.与相似优势关系和限制相似优势关系比较研究的结果表明:均值限制优势关系的分类精度和质量介于二者之间,而分类误差率则优于相似优势关系和限制相似优势关系,得到的决策规则可信度更高,决策模型与实际情况更加相符.     

Logic optimality for multi-objective optimization

Pareto dominance is one of the most basic concepts in multi-objective optimization. However, it is inefficient when the number of objectives is large because in this case it leads to an unmanageable number of Pareto solutions. In order to solve this problem, a new concept of logic dominance is defined by considering the number of improved objectives and the quantity of improvement simultaneously, where probabilistic logic is applied to measure the quantity of improvement. Based on logic dominance, the corresponding logic nondominated solution is defined as a feasible solution which is not dominated by other ones based on this new relationship, and it is proved that each logic nondominated solution is also a Pareto solution. Essentially, logic dominance is an extension of Pareto dominance. Since there are already several extensions for Pareto dominance, some comparisons are given in terms of numerical examples, which indicates that logic dominance is more efficient. As an application of logic dominance, a house choice problem with five objectives is considered.     

New multi-objective method to solve reentrant hybrid flow shop scheduling problem

This paper focuses on the multi-objective resolution of a reentrant hybrid flow shop scheduling problem (RHFS). In our case the two objectives are: the maximization of the utilization rate of the bottleneck and the minimization of the maximum completion time. This problem is solved with a new multi-objective genetic algorithm called L-NSGA which uses the Lorenz dominance relationship. The results of L-NSGA are compared with NSGA2, SPEA2 and an exact method. A stochastic model of the system is proposed and used with a discrete event simulation module. A test protocol is applied to compare the four methods on various configurations of the problem. The comparison is established using two standard multi-objective metrics. The Lorenz dominance relationship provides a stronger selection than the Pareto dominance and gives better results than the latter. The computational tests show that L-NSGA provides better solutions than NSGA2 and SPEA2; moreover, its solutions are closer to the optimal front. The efficiency of our method is verified in an industrial field-experiment.     

Some results on cooperative interval games

Uncertainty is a daily presence in the real world. It affects our decision-making and may have influence on cooperation. On many occasions, uncertainty is so severe that we can only predict some upper and lower bounds for the outcome of our actions, i.e. payoffs lie in some intervals. A suitable game theoretic model to support decision-making in collaborative situations with interval data is that of cooperative interval games. Solution concepts that associate with each cooperative interval game sets of interval allocations with appealing properties provide a natural way to capture the uncertainty of coalition values into the players’ payoffs. In this paper, the relations between some set-valued solution concepts using interval payoffs, namely the interval core, the interval dominance core, the square interval dominance core and the interval stable sets for cooperative interval games, are studied. It is shown that the interval core is the unique stable set on the class of convex interval games.     

Probability dominance in random outcomes

An intuitively appealing probability dominance relation for random outcomes is defined and investigated. Its properties, strengths, and weaknesses relative to stochastic dominance and meanvariance dominance are studied. It is shown that the proposed probability dominance complements existing solution concepts and strengthens one's confidence in decision making under uncertainty. Fundamental characteristics of nondominated random payoffs and methods for identifying them, for both general and specific classes, are reported. Application pitfalls and possible extensions are also discussed.     

两类随机控制风险秩序的等价性分析

本文首先对比分析了两类风险秩序:随机控制秩序与对偶随机控制秩序.得到并证明了下述命题:(1)效用自由秩序等价于随机控制秩序;(2)畸变自由秩序等价于对偶随机控制秩序;(3)第一、第二阶随机控制秩序等价于第一、第二阶的对偶随机控制秩序,但对高于三阶的情况由实例说明不一定成立.     

一类不完全信息的多属性决策的方案支配优化模型

研究了一类不完全信息的多属性决策问题,建立了方案支配关系的优化模型.给出了属性权重集合的极点的两种求法,提供了求解模型的算法.所提供的算法只需要简单的矩阵运算,从而降低了计算时间,并且用数值例子来描述该算法.     

Portfolio construction based on stochastic dominance and target return distributions

Mean-risk models have been widely used in portfolio optimization. However, such models may produce portfolios that are dominated with respect to second order stochastic dominance and therefore not optimal for rational and risk-averse investors. This paper considers the problem of constructing a portfolio which is non-dominated with respect to second order stochastic dominance and whose return distribution has specified desirable properties. The problem is multi-objective and is transformed into a single objective problem by using the reference point method, in which target levels, known as aspiration points, are specified for the objective functions. A model is proposed in which the aspiration points relate to ordered outcomes for the portfolio return. This concept is extended by additionally specifying reservation points, which act pre-emptively in the optimization model. The theoretical properties of the models are studied. The performance of the models on real data drawn from the Hang Seng index is also investigated.     

Valid inequalities and restrictions for stochastic programming problems with first order stochastic dominance constraints

Stochastic dominance relations are well studied in statistics, decision theory and economics. Recently, there has been significant interest in introducing dominance relations into stochastic optimization problems as constraints. In the discrete case, stochastic optimization models involving second order stochastic dominance constraints can be solved by linear programming. However, problems involving first order stochastic dominance constraints are potentially hard due to the non-convexity of the associated feasible regions. In this paper we consider a mixed 0–1 linear programming formulation of a discrete first order constrained optimization model and present a relaxation based on second order constraints. We derive some valid inequalities and restrictions by employing the probabilistic structure of the problem. We also generate cuts that are valid inequalities for the disjunctive relaxations arising from the underlying combinatorial structure of the problem by applying the lift-and-project procedure. We describe three heuristic algorithms to construct feasible solutions, based on conditional second order constraints, variable fixing, and conditional value at risk. Finally, we present numerical results for several instances of a real world portfolio optimization problem. This research was supported by the NSF awards DMS-0603728 and DMI-0354678.     

Partial stochastic dominance via optimal transport

In recent years, a range of measures of “partial” stochastic dominance have been introduced. These measures attempt to determine the extent to which one distribution is dominated by another. We assess these measures from intuitive, axiomatic, computational and statistical perspectives. Our investigation leads us to recommend a measure related to optimal transport as a natural default.     

Maximum probability dominance and portfolio theory

The concept of probability dominance and some of its parametric and nonparametric aspects are explored here in comparing two or more competing portfolios of returns. These aspects deal with the theory of stochastic dominance and that of mean-variance efficiency, which have been applied frequently in portfolio literature. Some illustrative applications are also developed here for evaluating the performance of mutual fund portfolios.Grateful thanks are due to P. L. Yu and the anonymous referee for their many constructive suggestions.     

On Extending the LP Computable Risk Measures to Account Downside Risk

A mathematical model of portfolio optimization is usually quantified with mean-risk models offering a lucid form of two criteria with possible trade-off analysis. In the classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. Following Sharpe’s work on linear approximation to the mean-variance model, many attempts have been made to linearize the portfolio optimization problem. There were introduced several alternative risk measures which are computationally attractive as (for discrete random variables) they result in solving linear programming (LP) problems. Typical LP computable risk measures, like the mean absolute deviation (MAD) or the Gini’s mean absolute difference (GMD) are symmetric with respect to the below-mean and over-mean performances. The paper shows how the measures can be further combined to extend their modeling capabilities with respect to enhancement of the below-mean downside risk aversion. The relations of the below-mean downside stochastic dominance are formally introduced and the corresponding techniques to enhance risk measures are derived.The resulting mean-risk models generate efficient solutions with respect to second degree stochastic dominance, while at the same time preserving simplicity and LP computability of the original models. The models are tested on real-life historical data.The research was supported by the grant PBZ-KBN-016/P03/99 from The State Committee for Scientific Research.     

PSD准则与累积展望理论不一致的讨论

Levy M和Levy H设计了一些实验,应用展望随机占优准则(PSD)给出方案的偏好序,实验结果拒绝了累积展望理论;而Wakker认为实验的数据恰恰支持了累积展望理论.试图讨论PSD准则拒绝累积展望理论的原因,PSD准则以概率的线性加权为基础,累积展望理论的重要假设是对概率进行了编译,二者存在着差异,据此,提出广义展望随机占优准则,认为在此准则下Levy M和Levy H的实验数据无法给出方案的偏好序,因此不能否认累积展望理论的正确性.     

基于随机参照点的均值-风险分析

本文依据参照依赖偏好模型提出了基于随机参照点的风险度量方法,进而构建了均值-风险模型,并讨论了该决策方法与随机占优之间的一致性。研究发现,该决策方法不仅与一级随机占优是一致的而且与二级随机占优也是一致的。由于二级随机占优与期望效用理论的一致性,因而所构建的均值-风险模型与期望效用理论也是一致的。     

P2P网络借贷市场利率主导权偏离程度的研究——基于双边随机前沿模型

近年来P2P网络借贷作为一种典型的互联网金融模式获得了跳跃式的发展,由于借贷双方信息不对称,导致我国P2P网贷市场利率普遍偏高。本文利用双边随机前沿分析(SFA)方法对我国P2P网贷市场借贷双方利率主导权力进行测算,并对借贷双方的主导权力对贷款利率的影响效应进行定量分析,同时对借款者个体特征对借贷双方利率主导权力的影响进行比较分析。实证结果表明,出借方拥有明显的主导权力,随着学历、年龄、收入、信用等级的增高,借款人地位将有所改善。     

一种属性信息不完全的TODIM决策方法

针对属性信息不完全的多属性决策问题,考虑到决策者具有参照依赖和损失规避行为,提出一种不完全信息的TODIM决策方法。首先,在考虑决策者参照依赖和损失规避行为的基础上,计算每个方案相对于其它方案关于每个属性的优势度;然后计算每个方案相对于其它所有方案的总体优势度;再以最大化所有方案的总体优势度作为目标函数,建立确定最优方案的优化模型。进一步,利用TODIM方法的思想,计算每个方案相对于其它所有方案的总体优势度,从而对方案进行排序。最后通过一个风险投资的算例验证该方法的可行性和有效性。     

Supply chain performance and consumer surplus under alternative structures of channel dominance

Supply chain partnerships exhibit varying degrees of power distribution among the agents. This has implications for pricing and operational decisions in the channel and eventually influences the end customers. To understand how different power schemes affect the supply chain partners’ performance and consumer surplus, we study channel structures with a dominant manufacturer, a dominant retailer, and no single-agent dominance. Under random and price sensitive demand, channel dominance is interpreted in our setting as exerting power to determine the retail and wholesale prices as well as to transfer the inventory risk to the weaker party. We analyze all problems in a game-theory based framework and characterize the equilibrium retail price, wholesale price, and order/production quantity. We show that the manufacturer-dominated channel structure leads to the highest production quantity, the lowest retail price, and the largest expected surplus for an individual buyer; on the other hand, the entire channel profit and the total consumer surplus are highest when the retailer holds the channel dominance. While both the manufacturer and the retailer are better off when they become a power agent individually, channel dominance does not always guarantee higher share of channel profits, as we show under the manufacturer-dominated structure. Further insights are derived analytically and numerically from comparisons of the manufacturer/retailer dominance schemes with the no single-agent dominance structure and integrated channel. We also study extensions to investigate the effect of demand model and risk sharing, and we address industry settings with alternative schemes of holding cost, shortage penalty and salvage value.     

Estimation of the mean in partially observed branching processes with general immigration

In the paper we investigate asymptotic properties of the branching process with non-stationary immigration which are sufficient for a natural estimator of the offspring mean based on partial observations to be strongly consistent and asymptotically normal. The estimator uses only a binomially distributed subset of the population of each generation. This approach allows us to obtain results without conditions on the criticality of the process which makes possible to develop a unified estimation procedure without knowledge of the range of the offspring mean. These results are to be contrasted with the existing literature related to i.i.d. immigration case where the asymptotic normality depends on the criticality of the process and are new for the fully observed processes as well. Examples of applications in the process with immigration with regularly varying mean and variance and subcritical processes with i.i.d. immigration are also considered.

     

Relaxations of linear programming problems with first order stochastic dominance constraints

Linear stochastic programming problems with first order stochastic dominance (FSD) constraints are non-convex. For their mixed 0-1 linear programming formulation we present two convex relaxations based on second order stochastic dominance (SSD). We develop necessary and sufficient conditions for FSD, used to obtain a disjunctive programming formulation and to strengthen one of the SSD-based relaxations.