Scenario analysis using Bayesian networks: A case study in energy sector

This paper provides a general overview of creating scenarios for energy policies using Bayesian Network (BN) models. BN is a useful tool to analyze the complex structures, which allows observation of the current structure and basic consequences of any strategic change. This research will propose a decision model that will support the researchers in forecasting and scenario analysis fields. The proposed model will be implemented in a case study for Turkey. The choice of the case is based on complexities of a renewable energy resource rich country. Turkey is a heavy energy importer discussing new investments. Domestic resources could be evaluated under different scenarios aiming the sustainability. Achievements of this study will open a new vision for the decision makers in energy sector.     

A research framework for Web-based open decision support systems

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). In this paper, we propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS and creates a uniform research framework for various decision support systems. The conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also analyze the basic functions and develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS function efficiently. Finally, an illustrative example is studied to support the proposed ideas.     

A systematic literature review on the applications of Bayesian networks to predict software quality

Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual and methodological details regarding BN construction are reported in the studies. We conduct a systematic literature review on the applications of BNs to predict software quality. We focus on more detailed questions regarding (1) dataset characteristics, (2) techniques used for parameter learning, (3) techniques used for structure learning, (4) use of tools, and (5) model validation techniques. Results on ten primary studies show that BNs are mostly built based on expert knowledge, i.e. structure and prior distributions are defined by experts, whereas authors benefit from BN tools and quantitative data to validate their models. In most of the papers, authors do not clearly explain their justification for choosing a specific technique, and they do not compare their proposed BNs with other machine learning approaches. There is also a lack of consensus on the performance measures to validate the proposed BNs. Compared to other domains, the use of BNs is still very limited and current publications do not report enough details to replicate the studies. We propose a framework that provides a set of guidelines for reporting the essential contextual and methodological details of BNs. We believe such a framework would be useful to replicate and extend the work on BNs.     

Integrating model management with data management in decision support systems

Because of the increasing complexity of the problems faced by decision makers and the nature of decision support systems, decision models are becoming more and more crucial to their success. In this paper, a general framework for model management, which can integrate model management and data management and handle issues in model management such as model creation, model modification and model use is proposed.     

An ecological risk assessment for managing and predicting trophic shifts in estuarine ecosystems using a Bayesian network

Estuaries are dynamic systems at the transition between freshwater and marine ecosystems. In this study, a spatially and temporally explicit Bayesian network (BN) was developed for a tidally connected estuary in southeastern Australia. The BN provides an environmental risk assessment (ERA) for the probability of a shift to a eutrophied state based on markers of pelagic and benthic primary production. The model was created to provide an initial framework of system knowledge based on empirical data, with the intention that the model and its linkages be iteratively developed as more information becomes available. The BN was investigated for its potential to predict trophic shifts and provide a framework for evidence-based decision making. Model assessment was conducted through both sensitivity analysis and scenario tests. Through evaluation and updating, the BN can provide information on the key nutrients and bio-physical mechanisms regulating changes in trophic state in estuarine ecosystems.     

A design for a common‐sense knowledge‐enhanced decision‐support system: Integration of high‐frequency market data and real‐time news

According to efficient markets theory, information is an important factor that affects market performance and serves as a source of first‐hand evidence in decision making, in particular with the rapid rise of Internet technologies in recent years. However, a lack of knowledge and inference ability prevents current decision support systems from processing the wide range of available information. In this paper, we propose a common‐sense knowledge‐supported news model. Compared with previous work, our model is the first to incorporate broad common‐sense knowledge into a decision support system, thereby improving the news analysis process through the application of a graphic random‐walk framework. Prototype and experiments based on Hong Kong stock market data have demonstrated that common‐sense knowledge is an important factor in building financial decision models that incorporate news information.     

An appropriateness framework for the Dutch Meuse decision support system

Models are essential in a decision support system for river basin management. In a decision support system for integrated planning and management, the use of appropriate models is important to avoid models being either too simple or too complex. In this paper, appropriate models refer to models that are good-enough-but-not-more-than-that to obtain an acceptable ranking of river engineering measures under uncertainty. A systematic approach called ‘appropriateness framework’ is proposed to determine appropriate models that can be used in a decision support system. The approach is applied to a decision support system for the Dutch Meuse River. One important component of this decision support system, flood safety, is used in this paper to demonstrate how this approach works. The results show that the approach is very useful in helping to determine appropriate models. Potential applications of the approach in other decision support systems are discussed. The approach presented in this paper is designed as a tool to stimulate the communication between decision makers and modelers and to promote the use of models in decision-making for river basin management.     

Predicting software defects in varying development lifecycles using Bayesian nets

An important decision in software projects is when to stop testing. Decision support tools for this have been built using causal models represented by Bayesian Networks (BNs), incorporating empirical data and expert judgement. Previously, this required a custom BN for each development lifecycle. We describe a more general approach that allows causal models to be applied to any lifecycle. The approach evolved through collaborative projects and captures significant commercial input. For projects within the range of the models, defect predictions are very accurate. This approach enables decision-makers to reason in a way that is not possible with regression-based models.     

Decision-enabled dynamic process management for networked enterprises

Enterprises in today’s networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A service-oriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed.     

An overview of methods to evaluate uncertainty of deterministic models in decision support

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller.     

Consensus under a fuzzy context: Taxonomy,analysis framework AFRYCA and experimental case of study

Consensus reaching processes play an increasingly important role in the resolution of group decision making problems: a solution acceptable to all the experts participating in a problem is necessary in many real-life contexts. A large number of consensus approaches have been proposed to support groups in such processes, each one with its own characteristics, such as the methods utilized for the fusion of information regarding the preferences of experts. Given this variety of existing approaches in the literature to support consensus reaching processes, this paper considers two main objectives. Firstly, we propose a taxonomy that provides an overview and categorization of some existing consensus models for group decision making problems defined in a fuzzy context, taking into account the main features of each model. Secondly, the paper presents AFRYCA, a simulation-based analysis framework for the resolution of group decision making problems by means of different consensus models. The framework is aimed at facilitating a study of the performance of each consensus model, as well as determining the most suitable model/s for the resolution of a specific problem. An experimental study is carried out to show the usefulness of the framework.     

Learned student models with item to item knowledge structures

Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, Partial Order Knowledge Structures (POKS), relies on a naive Bayes framework whereas the other is based on the Bayesian network (BN) learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in different experimental simulations. The results from simulations over three data sets show that they both can effectively perform accurate predictions, but POKS generally displays higher predictive power than the BN. Moreover, the simplicity of POKS translates to a time efficiency between one to three orders of magnitude greater than the BN runs. We further explore the use of the item to item approach for handling concepts mastery assessment. The approach investigated consist in augmenting an initial set of observations, based on inferences with the item to item structure, and feed the augmented set to a BN containing a number of concepts. The results show that augmented set can effectively improve predictive power of a BN for item outcome, but that improvement does not transfer to the concept assessment in this particular experiment. We discuss different explanations for the results and outline future research avenues.     

Decision support analysis for safety control in complex project environments based on Bayesian Networks

This paper presents a novel and systemic decision support model based on Bayesian Networks (BN) for safety control in dynamic complex project environments, which should go through the following three sections. At first, priori expert knowledge is integrated with training data in model design, aiming to improve the adaptability and practicability of model outcome. Then two indicators, Model Bias and Model Accuracy, are proposed to assess the effectiveness of BN in model validation, ensuring the model predictions are not significantly different from the actual observations. Finally we extend the safety control process to the entire life cycle of risk-prone events in model application, rather than restricted to pre-accident control, but during-construction continuous and post-accident control are included. Adapting its reasoning features, including forward reasoning, importance analysis and background reasoning, decision makers are provided with systematic and effective support for safety control in the overall work process. A frequent safety problem, ground settlement during Wuhan Changjiang Metro Shield Tunnel Construction (WCMSTC), is taken as a case study. Results demonstrate the feasibility of BN model, as well as its application potential. The proposed model can be used by practitioners in the industry as a decision support tool to increase the likelihood of a successful project in complex environments.     

Generic causal probabilistic networks: a solution to a problem of transferability in medical decision support

Causal probabilistic networks provide a natural framework for representation of medical knowledge, allowing clinical experts to encode assumptions about causal dependencies between stochastic variables. Application in medical decision support has produced promising results. However, model features and parameters may vary geo- or demographically. Therefore methods are needed that allow for easy adjustment of the model to a change in conditions. We present a method to represent causal probabilistic networks generically that maximizes the transferability of a models relevance and completeness, when moved from one environment to another, and illustrate application of the method with an example from a medical decision support system.     

Group decision making problems in a linguistic and dynamic context

The aim of this paper is to present a new model of decision support system for group decision making problems based on a linguistic approach and dynamic sets of alternatives. The model incorporates a mechanism that allows to manage dynamic decision situations in which some information about the problem is not constant in time. We assume that the set of alternatives can change during the decision making process. The model is presented in a mobile and dynamic context where the experts’ preferences can be incomplete. The linguistic approach is used to represent both the experts’ preferences about the alternatives and the agreement degrees to manage the change of some alternatives. A prototype of such mobile decision support system in which the experts use mobile devices to provide their linguistic preferences at anytime and anywhere has been implemented. In such a way, we provide a new linguistic group decision making framework that is mobile and dynamic.     

广域组织内开放决策支持网格框架

针对政府机构和企业缩减规模的发展趋势,为地理上分布的管理人员进行更高效的跨应用领域决策支持提出一个5层决策支持框架,重点实现组织决策支持（Organization Decision Support,ODS）、提高决策质量,并提供一个公共操作环境以使组织在决策制定的整个生命周期中有效地进行信息协作．该框架以决策任务为主导,扩展传统的ODS框架,把不同的资源规范到一体的参考模型中,引入更多的与决策支持有关的协作模型,可为组织的辅助决策系统建设提供参考．     

基于PMML的移动通信客户流失模型交换

移动通信领域迫切需要在地理分布的经营分析系统之间交换标准的数据挖掘模型。尽管预测模型标记语言已经成为数据挖掘模型交换格式的业界标准,但并没形成可用的框架来指导标准交换模型的生产过程。该文提出了支持挖掘模型交换和移动通信客户流失分析的决策树算法框架。利用该框架构建了流失预警系统,并使用模拟客户数据验证了其有效性。对标准交换模型进行了适当扩展,以支持对移动通信数据更加有效的流失分析。     

Stochastic programming and scenario generation within a simulation framework: An information systems perspective

Stochastic programming brings together models of optimum resource allocation and models of randomness to create a robust decision-making framework. The models of randomness with their finite, discrete realisations are called scenario generators. In this paper, we investigate the role of such a tool within the context of a combined information and decision support system. We explain how two well-developed modelling paradigms, decision models and simulation models can be combined to create “business analytics” which is based on ex-ante decision and ex-post evaluation. We also examine how these models can be integrated with data marts of analytic organisational data and decision data. Recent developments in on-line analytical processing (OLAP) tools and multidimensional data viewing are taken into consideration. We finally introduce illustrative examples of optimisation, simulation models and results analysis to explain our multifaceted view of modelling. In this paper, our main objective is to explain to the information systems (IS) community how advanced models and their software realisations can be integrated with advanced IS and DSS tools.     

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA results are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the results of basic DEA models. The paper formally shows how the results of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides DEA results which are consistent with the framework and are ready-to-analyze with data mining tools, thanks to their specially designed table-based structures. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.