基于预测油价的风险控制决策分析

本文是在建立和求解某石油集团公司针对某种油品的高、中、低三种预测价格的风险型决策模型中，提出了基于风险偏好控制的最大收益原则的决策方法，并进一步讨论了这种决策方法的优势所在。     

Learning in Dynamic Decision Making: The Recognition Process

The apparent difficulty that humans experience when asked to manage dynamic complexity might be related to their inability to discriminate among familiar classes of objects (i.e., flawed recognition). In this study we examined the change in individuals' recognition ability, as measured by the change in the similarity of decisions they made when confronted repeatedly with consistent dynamic situations of varying degrees of similarity. The study generated two primary findings. First, decisions became increasingly similar with task practice, a result that suggests gradually improving discrimination by the participants. Second, the similarity was determined by the interaction of many task features rather than individual task features. The general principles highlighted by this study are applicable to dynamic situations. For example, with practice, decision makers should be able to learn to identify the time at which to intervene to achieve the maximal effect during dynamic decision making.     

区间数排序的包含度度量及构造方法

本引用包含度和偏序包含度概念，指出[1]、[2]、[3]的可能度是一种区间数的包含度。在此基础上，利用三角模构造了一类偏序包含度，建立了区间数比较的包含度构造方法，并用各种包含度对[1]的算例进行排序，取得满意效果。     

Self-consistency of decision rules for group decision making

The author treats, in this paper, a group of decision makers, where each of them already has preference on a given set of alternatives but the group as a whole does not have a decision rule to make their group decision, yet. Then, the author examines which decision rules are appropriate. As a criterion of “appropriateness” the author proposes the concepts of self-consistency and universal self-consistency of decision rules. Examining the existence of universally self-consistent decision rules in two cases: (1) decision situations with three decision makers and two alternatives, and (2) those with three decision makers and three alternatives, the author has found that all decision rules are universally self-consistent in the case (1), whereas all universally self-consistent decision rules have one and just one vetoer in the essential cases in (2). The result in the case (2) implies incompatibility of universal self-consistency with symmetry. An example of applications of the concept of self-consistency to a bankruptcy problem is also provided in this paper, where compatibility of self-consistency with symmetry in a particular decision situation is shown.     

二层次消费与投资决策优化动态规划模型

在动态多阶段情形,投资者面临的环境不仅只有投资环境,还包括消费环境.投资者关于投资与消费的决策具有层次性.因为消费事关人的生存需要,是优先要考虑的问题,且投资的最终目的还是为了消费,所以使消费最大化应是高一层次的目标,而使投资最大化则应是次一级的目标.因此,试图建立一个二层次消费与投资决策优化动态规划模型,以便更好地模拟现实世界的情况.讨论了该模型的动态决策过程和最优解的性质.     

Another set of conditions for Markov decision processes with average sample-path costs

This paper deals with discrete-time Markov decision processes with average sample-path costs (ASPC) in Borel spaces. The costs may have neither upper nor lower bounds. We propose new conditions for the existence of ε-ASPC-optimal (deterministic) stationary policies in the class of all randomized history-dependent policies. Our conditions are weaker than those in the previous literature. Moreover, some sufficient conditions for the existence of ASPC optimal stationary policies are imposed on the primitive data of the model. In particular, the stochastic monotonicity condition in this paper has first been used to study the ASPC criterion. Also, the approach provided here is slightly different from the “optimality equation approach” widely used in the previous literature. On the other hand, under mild assumptions we show that average expected cost optimality and ASPC-optimality are equivalent. Finally, we use a controlled queueing system to illustrate our results.     

On some algorithms for limiting average Markov decision processes

We consider limiting average Markov decision processes (MDP) with finite state and action spaces. We propose some algorithms to determine optimal strategies for deterministic and general MDPs. These algorithms are based on graph theory and the construction of levels in some aggregated MDP.     

Sample-path optimality and variance-maximization for Markov decision processes

This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and under which we prove the existence of ASPR-optimal stationary policies and variance optimal policies. Our conditions are weaker than those in the previous literature. Moreover, our results are illustrated by a controlled queueing system. Research partially supported by the Natural Science Foundation of Guangdong Province (Grant No: 06025063) and the Natural Science Foundation of China (Grant No: 10626021).     

商业建筑人员疏散系统模型的构建与决策

针对我国商业建筑火灾事故频发、群死群伤事故严重的现状,对全国商业建筑特大火灾案例进行统计调查,建立人员疏散系统模型数据采用贝叶斯统计分析方法进行分析,克服了火灾数据缺乏、且难以进行大量重复实验的困难,得到各基本事件发生概率的条件期望估计与方差.通过误差传播对系统进行敏感性分析.通过对系统故障树的分析,得到在火灾发生情况下商业建筑人员疏散失败的概率及方差,并进行系统决策.     

PROMISE: A DSS for multiple objective stochastic linear programming problems

Most of the multiple objective linear programming (MOLP) methods which have been proposed in the last fifteen years suppose deterministic contexts, but because many real problems imply uncertainty, some methods have been recently developed to deal with MOLP problems in stochastic contexts. In order to help the decision maker (DM) who is placed before such stochastic MOLP problems, we have built a Decision Support System called PROMISE. On the one hand, our DSS enables the DM to identify many current stochastic contexts: risky situations and also situations of partial uncertainty. On the other hand, according to the nature of the uncertainty, our DSS enables the DM to choose the most appropriate interactive stochastic MOLP method among the available methods, if such a method exists, and to solve his problem via the chosen method.