Learning to align organizational design and data

The relationship between organizational learning and organizational design is explored. In particular, we examine the information processing aspects of organizational learning as they apply to a two-valued decision making task and the relation of such aspects to organizational structure. Our primary contribution is to extend Carley's (1992) model of this process. The original model assumes that all data input into the decision making processes are of equal importance or weight in determining the correct overall organizational decision. The extension described here allows for the more natural situation of non-uniform weights of evidence. Further extensions to the model are also discussed. Such organizational learning performance measures provide an interesting framework for analyzing the recent trend towards flatter organizational structures. This research suggests that flatter structures are not always better, but rather that data environment, ultimate performance goals, and relative need for speed in learning can be used to form a contingency model for choosing organizational structure.     

股份制公司的综合投资方案决策模型

对股份制公司的综合投资方案的决策问题进行了研究.首先依据多个投资方案的风险与收益并存的实际情况,建立了最佳投资组合方案的多目标决策模型.然后,由董事会综合各股东所持股份和相互评价权值,利用群决策的方法得到一个最终投资方案,此方案在理论上能使公司获得最大收益.     

多指标区间决策的理想点贴近法

研究了指标的权重不能完全确定但知道其所在区域的条件下的多指标决策问题 ,给出了方案与理想解的贴近度及其算法 ,按贴近度的大小可以对方案进行排序 .它同传统的决策方法相比较 ,具有需要信息量少 ,简单可靠等特点 .最后用该决策方法分析了一个实际问题 .     

组合DEA方法与成熟度模型对项目效益的评价

为全面考虑资金、管理决策能力等因素对项目效益的影响，本运用数据包络分析与项目成熟度模型结合的方法来对企业各个项目之间的相对效益进行评价，应用结果表明该评价方法对于企业资源的最优配置、提高总体效益是十分有用的。     

基于C~2R模型的广义链式网络DEA模型

传统网络DEA方法是将传统DEA方法评价过程中的"黑箱"打开,考虑输入到输出的中间环节,对生产过程中的各个环节分别评价。传统网络DEA方法获得的是相对于有效决策单元评价的结果,但有时可能要相对于非有效决策单元或者非决策单元进行评价,传统网络DEA方法无法解决该类问题。为此给出相对于非有效决策单元或者非决策单元进行评价的基于C~2R模型的广义链式网络DEA模型,并探讨相关性质.     

Collaborative distributed decision making for large scale disaster relief operations: Drawing analogies from robust natural systems

One of the most ignored, but urgent and vital challenges confronting society today is the vulnerability of urban areas to extreme events. Current organization of response systems, predominantly based on a command and control model, limits their effectiveness and efficiency. Particularly, in decision‐making processes where a large number of actors may be involved. In this article, a new distributed collaborative decision‐making model is proposed to overcome command and control limitations encountered in stressful, hostile, chaotic, and large‐scale settings. This model was derived by borrowing concepts from the collective decision making of honeybees foraging, a successful process in solving complex tasks within complex settings. The model introduced in this article was evaluated through differential equations, i.e., continuous analysis, and difference equations, i.e., discrete analysis. The most important result found is that the best available option in any large‐scale decision‐making problem can be configured as an attractor, in a distributed and timely manner. We suggest that the proposed model has the potential to facilitate decision‐making processes in large‐scale settings. © 2005 Wiley Periodicals, Inc. Complexity 11:28–38, 2005     

投资决策分析方法的缺陷及改进

本文指出了传统投资决策方法的缺陷 ,提出了将期权理论应用于投资决策的总体思路 ,突破了传统决策分析的局限性 ,使决策更加科学和合理     

Successive Proportional Additive Numeration Using Fuzzy Linguistic Labels (Fuzzy Linguistic SPAN)

The SPAN (Successive Proportional Additive Numeration or Social Participatory Allocation Network) is a procedure that converts individual judgments into a group decision. The procedure is based on a voting design by which individual experts allocate their votes iteratively between their preferred options and other experts. The process ends when all the votes are allocated to options, and the one with the highest number of votes is selected. The method requires the experts to specify an exact allocation of votes to both options and other experts. The Fuzzy Linguistic SPAN allows experts to allocate their votes using linguistic labels such as “most of” or “a few”, and determine the preferred option. This method is demonstrated using the Max–Min aggregation function used to develop a proportional representation of the option and member voting schemes. The method is also demonstrated using the LOWA aggregation function. The Fuzzy Linguistic SPAN method is beneficial since the linguistic voting process is easier for the experts and significantly reduces the computational process compared to the traditional SPAN. The paper presents the method and two examples with comparisons to the numerical SPAN method.     

Multivariate Gaussian criteria in SMAA

We consider stochastic multicriteria decision-making problems with multiple decision makers. In such problems, the uncertainty or inaccuracy of the criteria measurements and the partial or missing preference information can be represented through probability distributions. In many real-life problems the uncertainties of criteria measurements may be dependent. However, it is often difficult to quantify these dependencies. Also, most of the existing methods are unable to handle such dependency information.In this paper, we develop a method for handling dependent uncertainties in stochastic multicriteria group decision-making problems. We measure the criteria, their uncertainties and dependencies using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. Based on the simulation results, we determine for the criteria measurements a joint probability distribution that quantifies the uncertainties and their dependencies. We then use the SMAA-2 stochastic multicriteria acceptability analysis method for comparing the alternatives based on the criteria distributions. We demonstrate the use of the method in the context of a strategic decision support model for a retailer operating in the liberated European electricity market.