An incident information management framework based on data integration, data mining, and multi-criteria decision making

An effective incident information management system needs to deal with several challenges. It must support heterogeneous distributed incident data, allow decision makers (DMs) to detect anomalies and extract useful knowledge, assist DMs in evaluating the risks and selecting an appropriate alternative during an incident, and provide differentiated services to satisfy the requirements of different incident management phases. To address these challenges, this paper proposes an incident information management framework that consists of three major components. The first component is a high-level data integration module in which heterogeneous data sources are integrated and presented in a uniform format. The second component is a data mining module that uses data mining methods to identify useful patterns and presents a process to provide differentiated services for pre-incident and post-incident information management. The third component is a multi-criteria decision-making (MCDM) module that utilizes MCDM methods to assess the current situation, find the satisfactory solutions, and take appropriate responses in a timely manner. To validate the proposed framework, this paper conducts a case study on agrometeorological disasters that occurred in China between 1997 and 2001. The case study demonstrates that the combination of data mining and MCDM methods can provide objective and comprehensive assessments of incident risks.     

Decision making support system for house purchase by using fuzzy logic

In this paper, we suggest a decision making support system for house purchasers, using fuzzy inference and hierarchic structure of evaluation. Main part of this system consist of macro and micro evaluation. Essential factors are taken into account in macro evaluation, and unessential detailed factors are considered later in micro evaluation. By adopting this structure, many decision makers could get their most suitable result.     

Managing emergencies and abnormal situations in air traffic control (part II): Teamwork strategies

Team performance has been studied in many safety-critical organizations including aviation, nuclear power plant, offshore oil platforms and health organizations. This study looks into teamwork strategies that air traffic controllers employ to manage emergencies and abnormal situations. Two field studies were carried out in the form of observations of simulator training in emergency and unusual scenarios of novices and experienced controllers. Teamwork strategies covered aspects of team orientation and coordination, information exchange, change management and error handling. Several performance metrics were used to rate the efficiency of teamwork and test the construct validity of a prototype model of teamwork. This is a companion study to an earlier investigation of taskwork strategies in the same field (part I) and contributes to the development of a generic model for Taskwork and Teamwork strategies in Emergencies in Air traffic Management (T²EAM). Suggestions are made on how to use T²EAM to develop training programs, assess team performance and improve mishap investigations.     

Managing emergencies and abnormal situations in air traffic control (part I): Taskwork strategies

A lot of research in Air Traffic Control (ATC) has focused on human errors in decision making whilst little attention has been paid to the cognitive strategies employed by controllers in managing abnormal situations. This study looks into cognitive strategies in taskwork that enable controllers to become resilient decision-makers. Two field studies were carried out where novice and experienced controllers were observed in simulator training in emergency and unusual scenarios. A prototype model of taskwork strategies in air traffic management was developed and its construct validity was tested in the context of the field studies. A companion study (part II), follows that investigates aspects of teamwork in the same field and contributes to the development of a generic model of Taskwork & Teamwork strategies in Emergencies in Air traffic Management (T²EAM). The final section addresses the difficulties experienced by novice controllers and explains taskwork strategies employed by experts to manage uncertainty and balance workload in simulator emergencies.     

Decision making in fuzzy discrete event systems

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert’s knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.     

Cognitive model based fashion style decision making

The decision of the style of a garment was affected by not only the physical attributes of the components of the garment but also the decision context. Lacking consideration of the decision context leads to the prediction failure of many models in intelligent fashion design systems. This paper proposed to integrate the cognitive model - Multi-alternative Decision Field Theory (MDFT) with Genetic Algorithm to tackle the context problem, specifically, the choice set when a decision was made. A unified S expression and its sign deciding mechanism was given out. Experiments showed that the proposed model gives out a better prediction of human fashion style decision than GA only model.     

On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach

Motivated by the converse Lyapunov technique for investigating converse results of semistable switched systems in control theory,this paper utilizes a constructive induction method to identify a cost function for performance gauge of an average,multi-cue multi-choice(MCMC),cognitive decision making model over a switching time interval.It shows that such a constructive cost function can be evaluated through an abstract energy called Lyapunov function at initial conditions.Hence,the performance gauge problem for the average MCMC model becomes the issue of finding such a Lyapunov function,leading to a possible way for designing corresponding computational algorithms via iterative methods such as adaptive dynamic programming.In order to reach this goal,a series of technical results are presented for the construction of such a Lyapunov function and its mathematical properties are discussed in details.Finally,a major result of guaranteeing the existence of such a Lyapunov function is rigorously proved.     

Decision making policy for RF energy harvesting enabled cognitive radios in decentralized wireless networks

Cognitive radio (CR) paradigm with radio frequency energy harvesting (RFEH) have significant potential to improve the network throughput by utilizing vacant spectrum using battery-operated self-sustainable radio terminals. Research efforts relevant to these paradigms are focused on the mode selection policies which decide when to switch from CR mode (i.e., opportunistic vacant spectrum access mode) to the RFEH mode (i.e., battery charging using ambient RF energy) and vice-versa. So far, very little attention has been paid to the dual but competing task of frequency band selection in CR and RFEH modes under partially observable environment in the decentralized wireless networks. Furthermore, the need of tunable bandwidth frequency band access for CRs and lower subband switching cost (SSC) for energy efficient implementation have made the design of the decision making policy (DMP) more challenging. In this paper, a new CR-RFEH DMP has been proposed for RFEH enabled CR terminals in the decentralized wireless networks. The proposed DMP consists of three sub-units: 1) Bayesian approach based tunable Thompson sampling algorithm for subband statistics estimation, 2) Thompson sampling algorithm based subband access scheme exploiting the past collision events to minimize collisions among CRs, and 3) Mode selection scheme. Simulation results show that the proposed DMP offers 10–35% improvement in the throughput of the decentralized network and 40–90% reduction in the number of subband switchings compared to existing DMPs. The simulation results are then validated using real radio signals on the proposed USRP testbed.     

Decision making with an interval-valued fuzzy preference relation and admissible orders

In this paper we analyze under which conditions we must use interval-valued fuzzy relations in decision making problems. We propose an algorithm to select the best alternative from a set of solutions which have been calculated with the nondominance algorithm using intervals and different linear orders among them. Based on the study made by Orlovsky in his work about nondominance, we study a characterization of weak transitive and 0.5-transitive interval-valued fuzzy relations, as well as the conditions under which transitivity is preserved by some operators on those relations. Next, we study the case of interval-valued reciprocal relations. In particular, we describe the preservation of reciprocity by different operators and analyze the transitivity properties for these interval-valued reciprocal relations. Finally, we propose to use, in the nondominance algorithm, linear interval orders generated by means of operators which preserve transitivity.     

Improved Decision Neural Network (IDNN) based consensus method to solve a multi-objective group decision making problem

Multi-criterion frameworks involving several subjective and quantitative factors that allow the complexity of Group Decision Making (GDM) to get worsen, especially for those problems which are having strategic dimensions. Recently, integration of multi-attribute utility theory (MAUT) and feed-forward neural network have been studied with a view to facilitate the automation of GDM. In this paper Improved Decision Neural Network (IDNN) based methodology has been developed to solve the multi-criterion decision problem in GDM. Reductions in the training data set, exploitation of indirect methods like multiplicative preference relation during the training process, and reduced number of iterations to map the MAUF are the advantages of this novel methodology. In this research, a soft consensus based group decision making methodology under linguistic assessments have been adopted for consensus forming among the groups.     

基于可能性理论的案例决策方法研究

提出基于可能性理论与基于案仍推理相结合的双层CBDT决策方法。第1层：决策者应用基于案例推理方法，通过建立在连续集合上问题的可能性分布，结合不同的决策方法对决策问题的不确定性进行定性分析；第2层：利用期望效用理论在与问题的可能性分布相关联的方案中选择具体方案。该方法可避免以往基于案例决策方法中所蕴含的一些技术难题。     

Adaptation in IMMPS as a decision making process

Intelligent Multimedia Presentation Systems (IMMPS) are characterised by their capability to dynamically adapt at run-time, based on the state of several aspects of user-computer interaction, such as the user profile, task and information characteristics, etc. In this paper, we treat adaptation as a decision making process, and propose the employment of established techniques from the domain of Decision Making to assist this process. It is argued that, following this approach, the adaptation process can be easily modified, customised and re-used in different application domains and for different user interaction requirements. The designer is enabled to incorporate different design strategies, which can be dynamically and automatically modified, according to the run-time requirements of the system. The application of the proposed approach is exemplified in the media/modalities allocation process.     

An effectiveness analysis of information channeling in choice decision making with attribute importance weights

In multi-alternative, multi-attribute choice decision tasks, decision makers use either alternative-based or attribute-based information processing patterns. Evidence suggests that channeling of access patterns may be effective. Restricted search of only key information attributes may be further encouraged when importance weights for attributes are predetermined and provided to the decision makers. This study examines the effectiveness of alternative-based channeling and attribute-based channeling with or without the provision of attribute importance weights. Both alternative-based and attribute-based channeling improves the decision accuracy when attribute weights are provided. In addition, the results indicate statistically significant effects on decision accuracy for the type of information display.     

诱导连续区间有序加权平均算子及其在区间数群决策中的应用

基于诱导有序加权平均(IOWA)算子和连续区间有序加权平均(C-OWA)算子,提出一种诱导连续区间有序加权平均(IC-OWA)算子,并讨论了该算子的优良性质.针对区间数互补判断矩阵提出了连续偏好矩阵的概念,定义了基于专家评判水平偏差的诱导连续区间有序加权平均(DIC-OWA)算子,并给出一种基于该算子的区间数群决策方法.最后通过算例说明了该方法的可行性.     

A method using weight restrictions in data envelopment analysis for ranking and validity issues in decision making

In this article we introduce a comprehensive yet efficient approach based on data envelopment analysis (DEA) with restricted multipliers for accountable and understandable multiple attribute decision making (MADM). Information system (IS) appraisals are motivated and used for illustrating the proposed methodology. Results show that the given DEA based approach can easily and significantly increase the information frame of the decision maker by identifying disparate rankings and by affirming the stability and validity of ranking outcomes. The given validity concept is contrary to the directions given in the main body of research and can also be used to question ranking outcomes of classic MADM methods.     

Do men and women use feedback provided by their Decision Support Systems (DSS) differently

This study investigates the effect gender has on the use of computer-based feedback and the impact that this feedback has on mood. The decision making process of men and women are investigated via a laboratory experiment using a previously validated Decision Support System (DSS) and a commonly used and negatively framed feedback. Grounded in human-computer interaction theories highlighting the strong social component of computers and social feedback theories showing that men and women react to negative feedback differently, we argue that the commonly used outcome feedback in DSS studies will influence both the decision accuracy of male and female users and their moods differently. The results, which support our basic theoretical argument, indicate that outcome feedback (in particular the more negative outcome feedback) improved the decision accuracy of the female users compared to their male counterparts. The results also indicate that the outcome feedback affect the overall mood of men and women differently as well. The overall moods of the female subjects were significantly less positive before and after completing the task (receiving this commonly used negative form of feedback), the moods of the male subjects before and after completing the task (receiving the same negative feedback) did not change. These results not only extend prior DSS feedback studies but also highlight the need and provide support for examining gender differences in such investigations.     

Aggregating information and ranking alternatives in decision making with intuitionistic multiplicative preference relations

The intuitionistic multiplicative preference relation (IMPR), whose all elements are measured by an unsymmetrical scale (Saaty's 1–9 scale) instead of the symmetrical scale in the intuitionistic fuzzy preference relation (IFPR), is suitable for describing the asymmetric preference information. In decision making process, one of the most crucial issues is how to rank alternatives from the given preference relation constructed by the decision maker. In this paper, two approaches are proposed for deriving the ranking orders of the alternatives from two different angles. To do it, a transformation mechanism is developed to transform an IMPR to a corresponding IFPR, and then all alternatives depicted by the given IMPR can be ranked via solving a familiar IFPR. In addition, the generalized intuitionistic multiplicative ordered weighted averaging (GIMOWA) and the geometric (GIMOWG) operators are given by taking fully account of the different weights associated with the particular ordered positions and their desirable properties are also discussed. After that, through a practical example, the proposed approaches are compared with the previous work and a numerical analysis of the results is also given.     

Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure

We study the decision-making problem with Dempster-Shafer theory of evidence. We analyze how to deal with this model when the available information is uncertain and it can be represented with fuzzy numbers. We use different types of aggregation operators that aggregate fuzzy numbers such as the fuzzy weighted average (FWA), the fuzzy ordered weighted averaging (FOWA) operator and the fuzzy hybrid averaging (FHA) operator. As a result, we get the belief structure fuzzy weighted average (BS-FWA), the belief structure fuzzy ordered weighted averaging (BS-FOWA) operator and the belief structure fuzzy hybrid averaging (BS-FHA) operator. We further generalize this new approach by using generalized and quasi-arithmetic means. We also develop an illustrative example regarding the selection of investments where we can see the different results obtained by using different types of fuzzy aggregation operators.     

The fusion process with heterogeneous preference structures in group decision making: A survey

In group decision making (GDM), decision makers who have different experiential, cultural and educational backgrounds will naturally provide their preference information by heterogeneous preference structures (e.g., utility values, preference orderings, numerical preference relations and multigranular linguistic preference relations). To date, many studies have discussed GDM problems with heterogeneous preference structures. To provide a clear perspective on the fusion process with heterogeneous preference structures in GDM, this paper presents a review of three types of fusion approaches: the indirect approach, the optimization-based approach and the direct approach. Moreover, with respect to insights gained from prior researches, several open problems are proposed for the future research.