秘书问题研究综述：何时停止搜索信息?

秘书问题是一类序贯观察与选择问题,描述了一种动态的信息搜索与决策过程,其问题实质是决定何时停止观察选项、而不是哪一个选项被选择.已有研究成果从解决该问题的策略方法角度,可以分为最优解策略与启发式策略,二者的差异主要体现在理论依据与研究方法上.最优解策略基于决策者完全理性假设,运用数学模型论证了解决该问题的最优决策行为。但许多实证研究发现,人们往往并没有遵循最优决策行为。相比较最优解策略而言,人们通常停止搜索信息太早或者说搜索量太少。这种基于决策者有限理性假设的描述性研究,在解释人们最优选择行为偏离的基础上,提出了解决秘书问题的一些启发式策略.最后,本文通过对已有研究成果的梳理与分析,提出了进一步研究的问题与方向。     

基于后悔理论的灰色应急决策方案动态调整方法

突发事件发展具有可变性、动态性、随机性等特点,这要求决策者能根据实际情况及时调整应急方案,在此过程中决策者往往表现出“有限理性”的心理特征。针对以上情形,提出一种基于后悔理论的决策方法,以解决不确定环境下考虑决策者心理因素的应急方案动态调整问题。该方法首先描述分析了基于灰数信息的应急调整方案的生成过程;然后从后悔规避的视角构造了调整方案集关于处置效果、调整成本、应对损失三方面的灰色感知效用矩阵;进一步,用转移概率矩阵预测突发事件的演化概率,计算各调整方案的灰色综合感知效用值以选出最佳调整方案;最后,通过实例验证了该方法的可行性和有效性。结果表明,该方法贴近决策实际、具有较强的实用性;能够为应急决策的方案调整问题提供方法指导和理论支持。     

基于错误矩阵方程的解决城市交通拥堵决策研究

针对解决城市交通拥堵决策问题,首先给出了错误优化矩阵的概念,在此基础上引出错误矩阵方程的概念,利用消错理论中的错误优化矩阵方程,从错误优化的角度来研究并解决城市交通拥堵的决策方法.相应结合实际状况给出当前状态矩阵,从而进行下一步的求解,步步推理获得了决策人满意的方案集,为决策者提供最优建议.     

有限理性条件下考虑决策者动态期望的多属性决策方法

有限理性条件下针对带有决策者期望的多属性决策问题,提出一种基于累积前景理论的决策分析方法。在本文中,首先考虑了决策者的有限理性的心理行为特征,以决策者在不同时期对各属性的特定期望作为参照点,然后将具有正态分布信息形式的决策矩阵转化为相对于各参照点的益损决策矩阵,在此基础上,考虑决策者对待收益和损失的不同理性态度,依据累积前景理论计算各时期中每个方案的前景值,并计算关于整个时期的综合前景值,然后依据综合前景值的大小对所有方案进行排序。最后,通过一个算例说明了该方法的可行性和有效性。     

基于证据理论和Vague集的多属性群决策方法研究

为了解决人们在复杂环境中决策困难的问题,论文基于Vague集描述不确定事物的优势,通过证据理论对Vague集进行合成,得到一种信息集结的多属性群决策方法。该算法首先考虑专家评分的可信度,在分析Vague集与证据理论的数学关系后,使用证据理论将各方案在各属性下的专家集证据集结。然后通过Vague集记分函数进行属性权重的计算,将方案集在属性集下的Vague评价值进行加权修正,再通过证据理论将属性集证据集结得到各方案最终的Vague评价值。之后使用记分函数计算每一方案的得分来确定最优方案。最后通过算例进一步说明所提方法的可行性与有效性。文章给出的算法使决策者在不确定环境下可以进行理性决策,从而选出最优方案。     

损失厌恶偏好与资产组合选择研究

在不确定性条件下,期望的不可计算性、行动结果比较的局限性以及投资个体选择的非理性使理性假定的选择理论脱离现实,因此重新探讨决策选择准则是必要的.以行为金融理论中不确定性状态下的有限理性与满意准则为依据,引入与满意准则一致且体现损失厌恶偏好的VaR作为风险指标,构建行为资产组合模型,在一种简单新颖的M-V模型的矩阵解法基础上,探寻了正态与部分非正态性假设下VaR-BPT模型的显性最优解或有效前沿,解决了现实中最优投资组合选择的可操作性难题,并在中国股票市场验证了正态性转换方法是处理非正态分布下资产组合选择问题的一种优秀方法.     

考虑决策者心理行为的直觉语言多属性决策方法

针对应用直觉语言集来表达决策信息的语言多属性决策问题,在考虑决策者有限理性的心理行为基础上,提出一种决策方法。该方法通过比较每个属性下方案之间的得分函数和精确函数, 构建方案的收益-损失分析矩阵。在考虑决策者参照依赖和损失规避心理行为基础上,计算每个方案相对于其它方案在每个属性下的收益-损失值优先度;在此基础上,计算备选方案的综合优先度, 并根据其大小对方案进行排序择优。最后通过一个算例验证所提出方法的有效性和合理性。     

考虑居民选择行为的应急避难场所选址问题研究

突发事件发生后,应急管理部门将面临选择启用哪些应急避难场所供居民避难的决策。与其它设施选址不同,处于有限理性状态下的居民可选择是否去避难以及去哪个避难所避难。为使未被服务的居民数量最小化,考虑居民的有限理性选择行为至关重要。为此,考虑居民避难距离及避难所吸引力等因素,基于竞争选址理论构建避难所对居民的效用函数。在此基础上,运用多项logit模型刻画居民的有限理性选择行为,建立基于有限理性选择行为的选址模型,并与基于效用最优选择行为和基于系统最优原则的选址模型进行对比以验证模型的合理性。为了高效地求解模型,设计了模拟退火算法和遗传算法两种启发式算法。算例分析结果验证了模型和算法的可行性和有效性,并揭示了居民理性程度、选址个数及财政预算均对选址结果有重要影响。     

决策者具有(严格)凸性偏好结构下的一类交互式多目标决策方法

为了更好地解决决策者具有(严格)凸性偏好结构下的多目标决策问题,一般目标空间为有界凸域的情形常常可以转化为目标空间为有界闭凸区域的情形,首先分析了切割平面及该平面上偏好最优点与被切割平面分割成的为有界闭凸区域的目标空间或目标空间的子集的两个部分之间的关系;然后分析并指出了对于包含全局偏好最优目标方案点的为有界闭凸域的目标空间及其子集(准最优目标集),在确定了切割平面上的偏好最优点后,通过适当地选取供决策者与切割平面的偏好最优点进行比较判断的目标方案点,经过一次比较就可以确定一个新的范围更小的包含全局偏好最优目标方案点的目标空间的有界闭凸子区域(准最优目标集).为获取切割平面上的偏好最优点,提出了改进的坐标轮换法.在这些结论和方法的基础上,提出了决策者具有(严格)凸性偏好结构下的一类交互式多目标决策方法,要求决策者提供较易的偏好性息,决策效能较好.     

基于实时公交信息的乘客乘车决策研究

实时交通信息的发布使乘客出行更具灵活性.调查乘客对实时公交系统的应用情况及功能需求,并提取乘客乘车决策规则.考虑乘客决策的"有限理性"特征构建决策模型:将乘车舒适性因素转换为时间成本,运用云模型建立定量时间到定性概念的映射;依据出行参考点确定出行方案的因素权重,建立期望效用函数;构建不同的出行场景,运用决策模型进行出行方案排序.结果表明:该模型能够更大限度地克服乘客理性限制对决策的影响,对多线路多因素方案下的乘客乘车行为做出决策,决策结果符合乘客决策规则,有助于为公交资源的规划与管理提供指导依据.     

Multiple objectives decomposition–coordination methods for hierarchical organizations

In this paper, the integration of goal programming models and hierarchical programming models is analyzed. The systems under study are assumed to consist of interconnected subsystems with multiple goals in each. Three possible cases regarding the number of decision makers will be considered: (1) one decision maker for the overall goals and one decision maker for each subsystem, (2) conflicting decision makers who are interested in their subsystems, and (3) just one decision maker for the overall system. Next, conditions are stated under which the problem of obtaining satisfying solutions for problems (1) and (3) can be reduced to the problem of obtaining satisfying solutions for the case (2). In order to determine such solutions, hierarchical techniques which exploit the structure of a decomposable system are analyzed. The empirical implementation of the two algorithms proposed shows their efficiency in terms of processing time.     

An interactive decision procedure with multiple attributes under risk

Consider a finite set of alternatives under risk which have multiple attributes. MARPI is an interactive computer-based procedure to find an efficient choice in the sense of linear expected utility. The choice is based on incomplete information about the decision maker's preferences which is elicited and processed in a sequential way. The information includes qualitative properties of the multivariate utility function such as monotonicity, risk aversion, and separability. Further, in case of an additively separable utility function, bounds on the scaling constants are elicited, and preferences (not necessarily indifferences) between sure amounts and lotteries are asked from the decision maker. The lotteries are Bernoulli lotteries generated by MARPI using special strategies. At every stage of the procedure the efficient set of alternatives is determined with respect to the information elicited so far.The procedure has been fully implemented on a PC. The paper exhibits the basic ideas of MARPI and some details of its implementation.     

The Advantages of Fuzzy Optimization Models in Practical Use

Classical mathematical programming models require well-defined coefficients and right hand sides. In order to avoid a non satisfying modeling usually a broad information gathering and processing is necessary. In case of real problems some model parameters can be only roughly estimated. While in case of classical models the vague data is replaced by "average data", fuzzy models offer the opportunity to model subjective imaginations of the decision maker as precisely as a decision maker will be able to describe it. Thus the risk of applying a wrong model of the reality and selecting solutions which do not reflect the real problem can be clearly reduced. The modeling of real problems by means of deterministic and stochastic models requires extensive information processing. On the other hand we know that an optimum solution is finally defined only by few restrictions. Especially in case of larger systems we notice afterwards that most of the information is useless. The dilemma of data processing is due to the fact that first we have to calculate the solution in order to define, whether the information must be well-defined or whether vague data may be sufficient. Based on multicriteria programming problems it should be demonstrated that the dilemma of data processing in case of real programming problems can be handled adequately by modeling them as fuzzy system combined with an interactive problem-solving. Describing the real problem by means of a fuzzy system first of all only the available information or such information which can be achieved easily will be considered. Then we try to develop an optimum solution. With reference to the cost-benefit relation further information can be gathered in order to describe the solution more precisely. Furthermore it should be pointed out that some interactive fuzzy solution algorithms, e.g. FULPAL provide the opportunity to solve mixed integer multicriteria programming models as well.     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

Monte Carlo simulation techniques for group decision making with incomplete information

In this paper we deal with group decision-making problems where several decision makers elicit their own preferences separately. The decision makers’ preferences are quantified using a decision support system, which admits incomplete information concerning the decision makers’ responses to the questions they are asked. Consequently, each decision maker proposes classes of utility functions and attribute weight intervals for the different attributes. We introduce an approach based on Monte Carlo simulation techniques for aggregating decision maker preferences that could be the starting point for a negotiation process, if necessary. The negotiation process would basically involve the decision maker tightening the imprecise component utilities and weights to output more meaningful results and achieve a consensus alternative. We focus on how attribute weights and the component utilities associated with a consequence are randomly generated in the aggregation process taking into account the decision-makers’ preferences, i.e., their respective attribute weight intervals and classes of utility functions. Finally, an application to the evaluation of intervention strategies for restoring a radionuclide contaminated lake illustrates the usefulness and flexibility of this iterative process.     

An integrated model-based interactive approach to FMAGDM with incomplete preference information

Incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations are very useful to express decision makers’ incomplete preferences over attributes or alternatives in the process of decision making under fuzzy environments. The aim of this paper is to investigate fuzzy multiple attribute group decision making problems where the attribute values are represented in intuitionistic fuzzy numbers and the information on attribute weights is provided by decision makers by means of one or some of the different preference structures, including weak ranking, strict ranking, difference ranking, multiple ranking, interval numbers, incomplete fuzzy preference relations, incomplete multiplicative preference relations, and incomplete linguistic preference relations. We transform all individual intuitionistic fuzzy decision matrices into the interval decision matrices and construct their expected decision matrices, and then aggregate all these expected decision matrices into a collective one. We establish an integrated model by unifying the collective decision matrix and all the given different structures of incomplete weight preference information, and develop an integrated model-based approach to interacting with the decision makers so as to adjust all the inconsistent incomplete fuzzy preference relations, inconsistent incomplete linguistic preference relations and inconsistent incomplete multiplicative preference relations into the ones with acceptable consistency. The developed approach can derive the attribute weights and the ranking of the alternatives directly from the integrated model, and thus it has the following prominent characteristics: (1) it does not need to construct the complete fuzzy preference relations, complete linguistic preference relations and complete multiplicative preference relations from the incomplete fuzzy preference relations, incomplete linguistic preference relations and incomplete multiplicative preference relations, respectively; (2) it does not need to unify the different structures of incomplete preferences, and thus can simplify the calculation and avoid distorting the given preference information; and (3) it can sufficiently reflect and adjust the subjective desirability of decision makers in the process of interaction. A practical example is also provided to illustrate the developed approach.     

Robustness and information levels in case-based multiple criteria sorting

Case-based preference elicitation methods for multiple criteria sorting problems have the advantage of posing rather small cognitive demands on a decision maker, but they may lead to ambiguous results when preference parameters are not uniquely determined. We use a simulation approach to determine the extent of this problem and to study the impact of additional case information on the quality of results. Our experiments compare two decision analysis tools, case-based distance sorting and the simple additive weighting method, in terms of the effects of additional case information on sorting performance, depending on problem dimension – number of groups, number of criteria, etc. Our results confirm the expected benefit of additional case information on the precision of estimates of the decision maker’s preferences. Problem dimension, however, has some unexpected effects.     

An Extended Model for Establishing Dominance in Multiattribute Decisionmaking

This paper is concerned with the use of incomplete information about utilities and weights in multiattribute decisionmaking. Because of time pressure and/or lack of knowledge, a decision maker may only be able to provide incomplete information which might be expressed as a set of linear inequalities. If the decision maker's information on both weights and utilities is imprecisely identified, then the model for establishing pairwise dominance becomes a non-linear program. A method for obtaining non-dominated alternatives without solving the non-linear program is proposed using a simple weighted-additive function.     

FIDIC合同条件下工程索赔的博弈模型*

索赔是工程项目各参与主体共同关注的焦点,索赔决策直接影响业主方和承包方的利益。研究通过分析工程索赔的特性,结合FIDIC合同条件下的索赔条款,提出索赔的博弈基础;分析博弈论和贝叶斯法则与索赔决策之间的契合程度,构建工程索赔的基本博弈模型;基于工程索赔的四种情境,构建完全信息有限多轮博弈模型、资金时间价值对博弈模型的影响、不完全信息有限多轮博弈模型和非完全对立的不完全信息有限多轮博弈模型并求解“纳什均衡”,模拟工程参与方做出索赔决策的路径,寻求相应情形下承包方和业主方的最优策略,提出工程参与方优化索赔策略的建议。