Toward a decision informatics paradigm: a real-time, information-based approach to decision making

The focus of this paper is on decision making; more specifically, on what decision making requirements are needed in the future. We augur for a decision informatics paradigm; it is a real-time, information-based approach to decision making. The paradigm is supported by two sets of technologies (i.e., information and decision technologies) and underpinned by three disciplines (i.e., data fusion/analysis, decision modeling, and systems engineering). We begin by considering the context - and needs - for decision making as the economies of the world change and evolve, especially in regard to emerging services; then our proposed decision informatics paradigm is detailed and illustrated, together with an in-depth review of a critical, underpinning research area (dealing with real-time fusion and analysis of multiple nonhomogeneous data sources), followed by several concluding remarks.     

一种基于辩论的决策方法研究

为了解决动态开放环境下决策方案可行性论证的难题,将辩论理论与实用推理结合提出了一种新的适用于多属性决策的辩论框架,定义了论据的类型和论据之间可能存在的攻击关系。将基于辩论的多属性决策过程分为用户需求获取、方案可行性论证和可行方案选优3个阶段,通过论据及其攻击关系对方案的可行性进行辩论论证。最后采用一个医疗决策问题对该框架的应用进行了示例说明,结果表明,该框架能够较好的处理信息不完整、不一致条件下的决策问题。     

Network selection based on multiple attribute decision making and group decision making for heterogeneous wireless networks

One of the key issues for radio resources management is network selection strategy in heterogeneous scenarios.In order to provide ubiquitous service,the paper puts forward a network selection algorithm based on multiple attribute decision making(MADM) and group decision making(GDM).Firstly,the proposed algorithm acquires attribute weights’ vectors of the subjective and objective decision makers based on MADM,and then the two attribute weights’ vectors are synthesized to be a new attribute weights’ vector by using GDM.Considering that the results of GDM should be reasonable and convincible,the criterion of consistency is adopted for judging the compatibility of group judgments.More specifically,the algorithm takes into account not only objective attributes of networks but also the preference of subscribers and traffic class.Hence it guarantees that the subscribers can not select the networks with poor performance depending on their preference.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide subscribers with satisfactory quality of service(QoS).     

Intelligent wireless mesh path selection algorithm using fuzzy decision making

Wireless mesh networks (WMNs) are expected to be widely deployed due to their ability to provide ubiquity, convenience, cost-efficiency, and simplicity for both service providers and end-users. Recently, the IEEE 802.11s standard introduces the hybrid wireless mesh protocol (HWMP) which is inspired by a combination of on-demand and tree-based pro-active routing algorithms. In this paper, we argue that the proposed unimetric path selection algorithm in the standard is not reliable. We introduce and examine a novel multimetric wireless mesh path selection algorithm using fuzzy decision making under realistic wireless channel conditions. The proposed path selection algorithm is designed to improve the performance of both re-active and pro-active routing protocols of HWMP for not only single-channel but also multi-channel WMNs. The reported results show the superior performance of the proposed path selection algorithm in terms of delay and packet delivery ratio without increasing overhead significantly. Although some fuzzy-based routing algorithms have been defined in literature recently, to the best of our knowledge, this paper is the first one to introduce and examine the use of fuzzy logic in the path selection of single- and multi-channel wireless local area network-based WMNs under realistic wireless channel conditions.     

Can a GSS stimulate group polarization? an empirical study

This study investigates the impact of the electronic communication and anonymity capabilities of a group support system (GSS) on group polarization. Three support levels were studied: verbal support, identified GSS support, and anonymous GSS support. Information exposure was varied at two levels: exposure to positions without arguments and exposure to positions with arguments. The dependent variables were preference change and choice shift. Anonymous GSS groups produced higher choice shift than verbal groups. Anonymous GSS groups who were exposed to positions with arguments also had higher preference change than groups in other treatments. These results indicate that the anonymity and electronic communication capabilities of a GSS can be used conjointly to stimulate group polarization, especially when groups exchange mutual positions and arguments     

Vertical handoff decision in 4G wireless networks using multi attribute decision making approach

Multi-interface terminal in heterogeneous wireless networks will have network access from diverse access technologies. Multiple attribute for decision making including user preference will increase the complexity of handoff process. Various approaches have been proposed to solve the complexity problem of handoff decision. In this paper, multi attribute decision making algorithms Analytic Hierarchy Process (AHP) method, Simple Additive Weighting (SAW), Total Order Preference by Similarity to the Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA) methods have been proposed for handoff decision in a WiMAX–WLAN environment to facilitate user with better quality of service. AHP has been used for calculation of weights of decision parameters. Numerical results show that SAW, TOPSIS and GRA provide almost similar performance. SAW is very simple to implement but probability of error is less in GRA because values of decision attribute are directly used for ranking of alternatives. TOPSIS is sensitive to the attribute with high score.     

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

Sustainable decision making: the role of decision support systems

Sustainable decision making stands for decision making which contributes to the transition to a sustainable society. It raises a number of challenging problems for which existing decision support systems (DSS) may not be equipped. The role of DSS in sustainable decision making is considered. The different models of decision making and their appropriateness in sustainable decision making are discussed. Examples from the areas of water resources and energy planning and management are presented to illustrate some of the issues in sustainable decision making and the role of DSS. Conclusions include a suggested research program for further development of models of decision making and the development of DSS for use in sustainable decision making     

权重信息不完全的混合型多属性决策方法研究

针对属性权重信息不完全且属性取值为精确数、语言标度、区间数、直觉模糊数、区间直觉模糊数的混合型多属性决策问题,根据五种不同形式的属性值规范化定义,建立每种形式下属性值之间的相关系数。基于偏爱程度的加权平均(PDWA)算子和逼近理想解(TOPSIS)法构建单目标最优化模型求出各属性的权重,通过对属性值和属性权重的线性集结,得到排序结果。最后通过数值算例,验证了该方法的可行性和操作性。     

A Dempster-Shafer-based approach to compromise decision making withmultiattributes applied to product selection

The Dempster-Shafer theory of evidence is applied to a multiattribute decision-making problem whereby the decision maker (DM) must compromise with available alternatives, none of which exactly satisfies his ideal. The decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each attribute element and the classification of existing alternatives. The classification of alternatives is addressed through expert evaluation of the degree to which each element is contained in each available alternative. The relative importance of each attribute element is determined through pairwise comparisons of the elements by the DM and implementation of a ratio-scale quantification method. The belief and plausibility that an alternative will satisfy the DM's ideal are then calculated and combined to rank order the available alternatives. Application to the problem of selecting computer software is given     

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user’s signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.     

A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks

Mobile terminals can typically connect to multiple wireless networks which offer varying levels of suitability for different classes of service. Due to the changing dynamics of network attributes and mobile users’ traffic needs, vertical handovers across heterogeneous networks become highly desirable. Multiple attribute decision making (MADM) techniques offer an efficient approach for ranking competing networks and selecting the best one according to specific quality of service parameters. In this paper, a genetic algorithm (GA) is applied to optimize network attributes’ weighting by emphasizing ranking differences among candidate networks, thereby aiding correct decision making by reducing unnecessary handovers and ranking abnormalities. The performance of the proposed GA-based vertical handover is investigated with typical MADM techniques including Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show that the proposed GA-based weight determination approach reduces the abnormality observed in the conventional SAW and TOPSIS techniques substantially. The results of this paper will help ensuring the application of MADM methods to more dynamic and challenging decision making problems encountered in wireless network.     

Rational decision making: structuring of design meetings

The design process is discussed from the viewpoint of decision-making. In sorting design problems the following stages of strategy, tactics, and execution (called the decision-making cycle), are assumed to always occur. Particular design meetings to obtain improved structuring of the design process are included in this decision-making cycle. The design meetings include decision analysis (DA), potential problem analysis (PPA), failure-mode and effect analysis (FMEA), and design for production (DFP). Progress controls in the decision-making cycle are included to ensure faster feedback about the progress of a project involving checks of the management aspects quality, throughput time, and costs. The necessity of this approach is illustrated by means of data gathered from an industrial automation department     

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.     

A tradeoff analysis for rough terrain cargo handlers using the AHP:an example of group decision making

The results of a case study designed to select the next generation of rough-terrain cargo handlers for the US Army are presented. Three alternatives were identified and ultimately ranked using the analytic hierarchy process (AHP). This framework permits decision makers to conduct tradeoffs among incommensurate criteria without having to rely on a single measure. The evaluation team consisted of five program managers and engineers. Special attention is paid to the efforts at data collection and the accompanying group dynamics. The objective hierarchy contained 12 attributes. In general, the AHP was found to be quite accessible and conducive to concensus building. Once the attributes were defined, the decision makers had little difficulty in furnishing the necessary data and discussing the intermediate results     

Multiobjective decision making for traffic assignment

The use of traffic assignment methods with multiobjective decision making to remedy the shortcomings of conventional traffic assignment methods is discussed. The optimal flow patterns are determined using three objectives: total travel time for road users; air pollution for nonusers; and travel distance for government. By using multiobjective decision making and nonlinear programming techniques, a series of noninferior solutions is generated. By combining an eigenvector weighting method with pairwise comparison, a compromise solution for the flow pattern is obtained. As an application example the Taipei network system is discussed. The results show that if other nontraffic-related factors are taken into account, the multiobjective traffic assignment approach is more reasonable and suitable than conventional approaches     

Knowledge engineering for medical decision making: A review of computer-based clinical decision aids

Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques.     

Pattern discovery: a data driven approach to decision support

Decision support nowadays is more and more targeted to large scale complicated systems and domains. The success of a decision support system relies mainly on its capability of processing large amounts of data and efficiently extracting useful knowledge from the data, especially knowledge which is previously unknown to the decision makers. With a large scale system, traditional knowledge acquisition models become inefficient and/or more biased, due to the subjectivity of the experts or the pre-assumptions of certain ideas or algorithmic procedures. Today, with the rapid development of computer technologies, the capability of collecting data has been greatly advanced. Data becomes the most valuable resource for an organization. We present a fundamental framework toward intelligent decision support by analyzing a large amount of mixed-mode data (data with a mixture of continuous and categorical values) in order to bridge the subjectivity and objectivity of a decision support process. By considering significant associations of artifacts (events) inherent in the data as patterns, we define patterns as statistically significant associations among feature values represented by joint events or hypercells in the feature space. We then present an algorithm which automatically discovers statistically significant hypercells (patterns) based on: 1) a residual analysis, which tests the significance of the deviation when the occurrence of a hypercell differs from its expectation, and 2) an optimization formulation to enable recursive discovery. By discovering patterns from data sets based on such an objective measure, the nature of the problem domain will be revealed. The patterns can then be applied to solve specific problems as being interpreted or inferred with.