An Overview of Bayesian Network Applications in Uncertain Domains <s> VI. DECISION MAKING AND BAYESIAN NETWORKS <s> This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VI. DECISION MAKING AND BAYESIAN NETWORKS <s> Summary: Multi-Criteria Decision Making (MCDM) involves the selection of the best actions from a set of alternatives, each of which is evaluated against multiple, and often conflicting, criteria. Most of the existing MCDM methods only focus on decisions under certainty. The criteria were evaluated separately as if they were independent of each other. Complex, often uncertain interactions between criteria, and between criteria and other factors are not modeled in a coherent and systematic manner. To address these issues, we propose in this paper a decision framework based on Bayesian networks (BN) and influence diagram (ID) to structure and manage MCDM problems with explicit modeling of uncertain interactions among entities of interest. In this framework, a decision problem is represented by an ID where each decision node represents the set of alternatives for a decision, a utility node represents the set of objectives (decision maker’s preferences), decision criteria and internal or external factors that may affect the criteria are represented by chance nodes. Interdependencies among these nodes are qualitatively modeled by the links in the diagram and quantitatively by conditional probability tables (CPT) associated with each of the chance nodes and the utility node. The joint probability distribution, which is compactly captured by the network structure and CPT, encodes the domain expert’s knowledge of interdependency between variables. The decision problem is then treated as an optimization problem: recommend the decision alternative which optimizes the expected utility, given observations of some external factors and preferences made by the decision maker. Various algorithms developed for BN and ID can be employed to automatically solve this problem. The steps that need to be taken to model a MCDM problem as an ID is presented, illustrated with a running example. Other related issues are also discussed. Our preliminary work indicates that this framework is of great potential as a modeling tool to support MCDM decision making in an uncertain environment. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VI. DECISION MAKING AND BAYESIAN NETWORKS <s> Bayesian Belief Networks (BBNs) are graphical models that provide a compact and simple representation of probabilistic data. BBNs depict the relationships among several variables and include conditional probability distributions that make probabilistic statements about those variables. This paper demonstrates how to create a BBN from real-world data on Information Technology implementations. The paper also displays the resulting BBN and describes how it can be incorporated into a DSS to support "what-if' analyses about Information Technology implementations. The paper combines techniques originating from artificial intelligence, statistics, and computer-based decision making. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VI. DECISION MAKING AND BAYESIAN NETWORKS <s> Integrated Testing Strategies (ITSs) are considered tools for guiding resource efficient decision-making on chemical hazard and risk management. Originating in the mid-nineties from research initiatives on minimizing animal use in toxicity testing, ITS development still lacks a methodologically consistent framework for incorporating all relevant information, for updating and reducing uncertainty across testing stages, and for handling conditionally dependent evidence. This paper presents a conceptual and methodological proposal for improving ITS development. We discuss methodological shortcomings of current ITS approaches, and we identify conceptual requirements for ITS development and optimization. First, ITS development should be based on probabilistic methods in order to quantify and update various uncertainties across testing stages. Second, reasoning should reflect a set of logic rules for consistently combining probabilities of related events. Third, inference should be hypothesis-driven and should reflect causal relationships in order to coherently guide decision-making across testing stages. To meet these requirements, we propose an information-theoretic approach to ITS development, the "ITS inference framework", which can be made operational by using Bayesian networks. As an illustration, we examine a simple two-test battery for assessing rodent carcinogenicity. Finally, we demonstrate how running the Bayesian network reveals a quantitative measure of Weight-of-Evidence. <s> BIB004
BN support decision making by finding high-quality strategies with the use of most probable configurations of attributes. The following literature presents several BN applications for better decision making. Lauria et al. BIB003 presented BN for IT implementation to support decision making by integrating BN into Decision Support System (DSS). This method has emerged as classification tool to make decision in uncertain situations. The classification tool has limited number of settings for illustrating relationships among variables of a real-world dataset on IT implementation. Another BN model, proposed by Pendharkar et al.  , integrates risks to predict software development effort. This model establishes probabilistic bounds for managers to use it as a benchmark. However, the proposed BN-based model is constructed over readily available data with small number of instances. Additionally, this model ignores complex variables and claims to address uncertainty with assumptions. Besides this, Jaworska et al. BIB004 suggested BN approach for Integrated Testing Strategies (ITS) to recognize constraints. The main focus of identifying constraints in ITS realization is to provide a guiding resource for making decisions on management risks and a chemical hazard. Nevertheless, BN-based ITS realization can be used to track quality with constraint based decisions about risks and hazards. Therefore, ITS cannot be declared as a guiding resource as a ready-made managerial decisions. Watthayu et al. BIB002 presented Multi-Criteria Decision Making (MCDM) framework which focused uncertainty in making decisions based on BN and Influence Diagram (ID) (an extended version of BN). This approach has the explicit probabilistic independence in the entire set of elements. However, the explicit independent elements in MCDM framework can produce biased or invalid results and increases computational complexity. Nadkarni et al. BIB001 transformed causal maps into BN to provide a systematic procedure, named as Causal BN, by combining exploratory and confirmatory methods. Causal BN quantified wide-ranging relationships between variables with no prior assumption with strong probabilistic inference using Netica tool. However, causal BN approach is restricted to normal decision with the employment of ID framework. Baesens et al.  presented a BN classifier for discovering the slope of life cycle of loyal customers. The key purpose is to predict the increase or decrease in expenditure of a new customer. Consequently, unrestricted BN classifier showed better performance. But, Markov Blanket concept is used without stressing the way of selecting effective attributes. Zorilla et al.  used BN in participatory water resource management for integrating understanding, significance and standards in decision making. In this way, public involvement is boosted up over a complex dilemma, but BN generalization is confined in terms of updated verdict generation.