不完全偏好下随机选择问题中的极大元定理

提出了不完全偏好的概念.在不完全偏好意义下获得了随机选择问题中的极大元定理和其它一些定理,统一和推广了许多已知结果.     

不完全偏好信息下房地产投资的模糊优选

解决了不完全偏好信息下含有模糊决策元素的房地产投资方案的优选问题.分析形成了房地产投资方案的评价指标体系,并在Hamming距离与Euclidean距离测度的基础上,建立了不完全偏好信息下的模糊多属性决策模型,能处理决策元素为一般模糊数的决策问题,同时提供了所建非线性规划模型的交叉迭代解法.实际算例的比较结果表明,本文提出的决策模型及算法优于文献中关于偏好信息完全确知的经典模型与算法.     

公平偏好下双渠道供应链最优定价策略与协调

在市场需求是一种依赖于销售价格的线性结构下,建立由一个制造商和一个零售商组成的两级双渠道供应链模型.在改进收入共享契约下,分别针对零售商不利不公平厌恶和有利不公平厌恶两种情形,探讨公平偏好对双渠道供应链最优定价策略及契约协调性的影响.研究表明,最优零售价格随零售商不利不公平厌恶偏好增大而减小,随其有利不公平厌恶偏好增大而增大;最优直销价格不受零售商公平偏好影响,制造商始终完全占有直销渠道的销售收入;最优批发价格大小与零售商收入共享比例和公平偏好程度有关;改进收入共享契约仅在零售商不利不公平厌恶偏好下能够实现双渠道供应链协调.     

不完全市场下考虑损失厌恶的连续时间投资组合选择

在不完全市场条件下研究了一般情形下的损失厌恶投资者的连续时间投资组合选择模型. 面对市场风险, 投资者的偏好由一个S-型的价值函数定义. 通过把不完全市场转换为完全市场, 利用鞅方法和复制技术, 分别获得了投资者的最优期末财富以及最优投资策略. 最后讨论了一个分段幂函数的例子, 在模型系数为确定的常数情形下, 得到了最优解的显示表达式.     

两种记分函数下多属性拍卖投标均衡策略

以拍卖人期望收益最大化为机制设计目标,讨论两种不同偏好的记分函数条件下,最高得分密封投标拍卖和连续完全信息多属性英式拍卖中,卖者的最优投标策略和买者的最优拍卖设计问题,主要结论是:1)无论选择哪种拍卖方式和记分函数,拍卖人均有动机隐瞒自己的真实偏好,除非竞价人是同质的或参与人数足够多.2)竞价人最优属性策略qi*与拍卖...     

一种基于偏好序信息的双边匹配方法

针对双方主体给出匹配偏好序信息的双边匹配问题,提出了一种决策分析方法。首先通过构建双边匹配满意度函数,将匹配偏好序信息转化为匹配满意度,然后通过集结双方主体相互间的匹配满意度得到综合匹配满意度,进一步地,将综合匹配满意度视为双边主体之间匹配的权,进而将基于偏好序信息的双边匹配问题转化为完全二分图中的权匹配问题。为了解决双边匹配问题,构建了最大权匹配问题的优化模型,并通过模型的求解确定最优双边匹配结果。在本文最后,通过一个算例说明了提出方法的实用性和有效性。     

纯交换经济环境中的双人贝叶斯机制研究

将Dutta和Sen(1994)关于不完全信息环境中二人贝叶斯执行问题扩展到纯交换经济环境中,建立了纯交换经济的双人贝叶斯机制模型,给出当偏好关系和初始禀赋都是不完全信息环境下,一个社会选择规则贝叶斯可执行的充分条件和必要条件.     

订单信息对双供应商公平偏好的实验研究

为了研究订单信息对双渠道进货供应商公平偏好的影响，本文考察由一个制造商和两个同质供应商组成的双渠道进货供应链，其中供应商按照价格折扣给制造商供货。为了获取更多优惠，制造商将会给一个供应商分配尽可能多的订单，因而引起小订单供应商的不满。这种小订单供应商对公平的偏好可能导致其拒绝供货。通过设计和实施实验室实验，本文对比订单是完全信息和不完全信息两种情境下被试的决策行为倾向。实验结果表明，在两种情境下被试决策都偏离理论预期，公平偏好是造成这一现象的主要因素。供应商在不完全信息下表现出更强的公平偏好。基于实验所观察到的现象，分别建立了完全信息和不完全信息下的行为模型，并通过参数估计考察了它们的有效性。研究结论表明，在双渠道进货时应重点关注小订单供应商的公平偏好，不要故意隐藏订单信息。     

不确定偏好序下的双边匹配博弈

针对带有不确定偏好序的双边匹配问题,现有方法大都仅注重整体收益之和,忽略了参与人的个体收益以及在交互选择中的策略运用.基于最大满意度准则,给出不确定序下的收益（满意度）矩阵的推导过程;然后,从个体理性视角,结合矩阵博弈的思想构建一种兼顾整体和个体收益的博弈匹配优化模型,并证明模型最优解满足纳什均衡.最后,进一步探讨各种策略选择及其优劣分析.     

不完全犹豫模糊语言偏好关系的不一致性修复方法

针对隶属度为犹豫模糊语言的不完全偏好关系,对其进行补全,并提出了一种基于偏差度的修复方法.首先,通过定义转换函数I以及f,并利用积性一致性补全不完全犹豫模糊语言偏好关系(IHFLPR).其次,基于误差偏好值以及两个犹豫度差值大小的比较准则,确定最大偏差度,并依据修复公式对不一致性进行修复.最后,通过实例以及与Xu的方法进行比较分析表明了该方法的有效性.     

Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method

The MUlticriteria Satisfaction Analysis (MUSA) method for measuring and analysing customer satisfaction is presented in this paper. The MUSA method is a preference disaggregation model following the principles of ordinal regression analysis (inference procedure). The integrated methodology evaluates the satisfaction level of a set of individuals (customers, employees, etc.) based on their values and expressed preferences. Using satisfaction survey's data, the MUSA method aggregates the different preferences in unique satisfaction functions. This aggregation–disaggregation process is achieved with the minimum possible errors. The main advantage of the MUSA method is that it fully considers the qualitative form of customers' judgements and preferences. The development of a set of quantitative indices and perceptual maps makes possible the provision of an effective support for the satisfaction evaluation problem. This paper also presents the reliability analysis of the provided results, along with a simple numerical example that demonstrates the implementation process of the MUSA method. Finally, several extensions and future research in the context of the presented method are discussed.     

A new model and solution method for product line design with pricing

Existing customer preference based product design models do not consider product prices and consumer budgets. These models assume that a purchase is based only on the satisfaction obtained from the product, irrespective of the product price and customer budget. However, when products are expensive relative to buyers' budgets, the effect of prices and budgets must be considered in addition to customer satisfaction. Most current models, moreover, assume that a low preference for one product characteristic is compensated by high preference for another, which may not hold for unacceptable levels of characteristics. For such products, we incorporate prices, budget constraints, and minimum acceptable thresholds in our model. To solve the model we develop a highly accurate, robust and efficient Beam Search (BS) based heuristic that identifies optimal or near optimal product lines. The heuristic is tested on 300 simulated problems and an application. It is also compared to a Genetic Algorithms (GA) based heuristic. We found that our heuristic worked better than the GA heuristic in identifying optimal and near optimal solutions quickly. We also give detailed examples that illustrate the heuristic and demonstrate a pricing analysis application of the model.     

多数满意偏好规则的充分必要条件

研究基于满意选择的群体决策的一个基本数学理论问题. 给出并证明了群体在方案集上的任一群体满意偏好映射是多数满意偏好规则的充分必要条件.     

Constructing a monotonic quadratic objective function in n variables from a few two-dimensional indifferences

We develop a model for constructing quadratic objective functions in n target variables. At the input, a decision maker is asked a few simple questions about his ordinal preferences (comparing two-dimensional alternatives in terms `better', `worse', `indifferent'). At the output, the model mathematically derives a quadratic objective function used to evaluate n-dimensional alternatives.Thus the model deals with some imaginary decisions (criteria aggregates) at the input, and disaggregates the decision maker's preference into partial criteria and their cross-correlations (=a quadratic objective function). Therefore, the model provides an approximation step which is next to the disaggregation of a preference into additively separable linear criteria with weight coefficients.The model is based on least squares fitting a quadratic indifference hypersurface (if n=2, indifference curve) to several alternatives which are supposed to be equivalent in preference. The resulting ordinal preference is independent of the cardinal utility scale used in intermediate computations which implies that the model is ordinal. The monotonicity of the quadratic objective function is implemented by means of a finite number of linear constraints, so that the computational model is reduced to restricted least squares.In illustration, we construct a quadratic objective function of German economic policy in four target variables: inflation, unemployment, GNP growth, and increase in public debt. This objective function is used to evaluate the German economic development in 1980–1994.In another application, we construct a quadratic objective function of ski station customers. Then it is used to adjust prices of 10 ski stations to the South of Stuttgart.In Appendix A we provide an original fast algorithm for restricted least squares and quadratic programming used in the main model.     

Methods for determining the statistical part worth value of factors in conjoint analysis

MONANOVA is one type of conjoint analysis used for measuring the part worth value of factors to the total evaluation, exclusively using preference ranking data of a group of commercial products designed by presorted factors. Its criterion, called Stress, is the same as that of monotone regression in MDS. Consequently, part worth values obtained from MONANOVA do not necessarily lead to definite solutions but give an approximate comparison of each factor's contribution to the total evaluation of the products.In this paper, we would like to discuss two problems with MONANOVA: namely, its reliability and stability. We would then like to propose two possible solutions to these problems: the first combines regression and monotone methods; the second employs quadratic fractional programming. With these, we hope to obtain each factor's contribution to the total evaluation as a partial correlation coefficient and to demonstrate that one can compare the factor's contribution to the total evaluation with constant stability.     

The emergence of cooperation in tie strength models

In this paper, we propose a tie strength model to explain the emergence of cooperation in spatial prisoner's dilemma games, assuming that cooperators preferentially allocate their investments to friends with strong ties. Two types of prisoner's dilemma models are examined in this study: the traditional two-strategy model considering only cooperators and defectors; the expanded three-strategy model consisting cooperators, defectors and extortioners. The results show that tie strength model contributes to the promotion of cooperation in both types of prisoner's dilemma games. However, we point out that the influence of the investment preference is quite different in the two prisoner's dilemma game settings. In the two-strategy prisoner's dilemma game, only small preference contributes to the promotion of cooperation. Once this preference exceeds a critical value, cooperation will be prohibited. We explain this phenomenon by arguing that extremely strong investment preference undermines the ability of cooperative clusters to resist defectors. Moreover, we extend the analysis into the three-strategy case and discover that the catalytic effect of extortioners can eliminate this first up and then down trend in the two-strategy model. The equilibrium fraction of cooperators is always positively correlated to the level of investment preference in three-strategy models.     

具有不确定偏好序信息的双边匹配决策问题研究

针对具有不确定偏好序信息的双边匹配决策问题,提出了一种决策方法。首先,给出了不确定偏好序和双边匹配决策问题的描述;接着,引入了满意度和支付的概念;在此基础上,考虑到匹配主体对之间的满意度要求及中介收益,构建了求解该双边匹配决策问题的多目标优化模型,采用基于隶属函数的加权和方法求解该模型获得双边匹配方案。最后,通过一个算例说明了该方法的可行性和有效性。     

Generalized Farkas' theorem and optimization of infinitely constrained problems

This note is concerned with the generalization of Farkas' theorem of the alternative and its application to derive the necessary optimality conditions for min-max problems with satisfaction conditions. Farkas' theorem is generalized to a system of linear inequalities with max operations. The problems studied require a solution at which the worst objective value attains its minimum over a set of solutions fulfilling satisfaction conditions. The satisfaction conditions claim that plural performance criteria should be kept below the permissible level, whatever disturbances may happen or whatever opponents' decisions may be taken. We present a generalized Farkas' theorem in order to derive the necessary optimality conditions for the problems of this class.     

Fuzzy facility location problem with preference of candidate sites

Models so far considered as facility location problems treat either min–max or max–min criterion, that is, the facility is either desirable or undesirable. We propose the following model considering the satisfaction degree with respect to the distance from the facility for each customer (residents) and preference of the site in an urban area. The objective is to find the site of the facility which maximizes the minimal satisfaction degree among all demand points and maximizes the preference of the site. Since generally speaking, there exists no site that maximizes both criteria, we seek non-dominated sites after defining non-domination.