The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network

Jiang, Meizhi; Lu, Jing

doi:10.1016/j.tre.2020.101965

Cited by 50 publications

(23 citation statements)

References 36 publications

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“…While CLDs (Williams, 2016) and SD (Loosemore & Cheung, 2015) can provide useful insights into the dynamics of multiple factors associated with country risk, these techniques rely on expert judgment in that experts are involved in developing the causal structure of a model, whereas our study aims to gain insights into the data‐driven model. Considering the aforementioned limitations of the available techniques, we utilize BBNs that provide an effective framework for developing a data‐driven model and generating different scenarios, which can be used for visualizing propagation patterns across multiple risks and uncertain variables (Cao, 2019; Ekici & Ekici, 2019; Jiang & Lu, 2020).…”

Section: Proposed Methodologymentioning

confidence: 99%

Exploring Probabilistic Network‐Based Modeling of Multidimensional Factors Associated with Country Risk

Qazi

Khan

2020

Risk Analysis

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Assessment of country risk provides a vital source of information to organizations for expanding and globalizing their operations. Various rating agencies are involved in developing models for assessing country risk, which utilize different statistical techniques for establishing the overall impact of individual factors on country risk. The main limitation of existing studies on country risk is their limited focus on exploring the relative contribution of individual factors to country risk in a probabilistic network setting. Utilizing real data, we develop a probabilistic network model that captures dependencies among multidimensional factors associated with country risk. Further, we assess the network‐wide vulnerability and resilience potential of individual factors to identify critical factors. The findings of this study provide policy‐makers with some unique insights into prioritizing strategies to mitigate country risk. Further, this study provides the context for multinational enterprises to utilize the proposed methodology for prioritizing key factors associated with the relative variables of interest such as regional stability and business environment among others.

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Section: Proposed Methodologymentioning

confidence: 99%

Exploring Probabilistic Network‐Based Modeling of Multidimensional Factors Associated with Country Risk

Qazi

Khan

2020

Risk Analysis

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“…The network topology of a BN is represented by a directed acyclic graph (DAG), which is a pair G = (V, E). A set of nodes that represents variables or attributes is denoted by V, and a set of directed edges, represented by E, connects the nodes representing causal relations [50]. The DAG is a graphical representation of a set of nodes and directed edges.…”

Section: Bayesian Network Modelingmentioning

confidence: 99%

“…Typically, the structure of a BN can be learned by expert knowledge and data learning. However, the expert judgments cannot confirm the objectivity and accuracy of the results, which may lead to spurious relationships, while it is difficult for the simple data-driven technique to learn the order among the nodes and critical information concealed in the investigation reports [50]. This study applies the integrated approach to address these weaknesses in structure learning.…”

Section: Bayesian Network 421 Bayesian Network Constructionmentioning

confidence: 99%

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Effect of Cross-Departmental Collaboration on Performance: Evidence from the Federal Highway Administration

2021

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Cross-departmental collaboration, one of the most salient administrative reforms, has been promoted to resolve cross-jurisdictional administration issues over the previous three decades. Nearly all previous empirical studies have examined the direct impact of cross-departmental collaboration on organizational performance without accounting for the indirect effects of managerial practices. Using data from the Federal Highway Administration, this study develops an integrated structural equation modeling and Bayesian network model used to examine both direct and indirect impacts of cross-departmental collaboration on organizational performance. The structural model indicates that cross-departmental collaboration has a direct effect on organizational performance and indirect effects through its influence on resource acquisition and knowledge creation. The scenario-based simulation suggests the optimal integration of managerial actions to improve agency performance, which is achieved by encouraging cross-departmental collaboration and supporting the knowledge creation process. Finally, implications are provided to present practical managerial actions from the Federal Highway Administration as an exemplar for other highway agencies.

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“…The BN and GT play a significant role in predicting and unintentionally diagnosing failures and targeted risks by using numerous tools and models, based on the information collected from the system expert's knowledge (EK) and/or from empirical data (ED). EK represents the opinions collected by interviewing the system or domain expert, and ED is the historical or experimental data gathered by real-time scenarios or the literature [50][51][52][53][54]. It is revealed in existing studies that a reliable strategy can be attained for the developed model by applying collective EK and ED.…”

Section: Data Sources and Number Of Nodes Used To Construct Bn/gtmentioning

confidence: 99%

Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review

Pirbhulal

Gkioulos

Katsikas

2021

Signals

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In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented

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The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network

Cited by 50 publications

References 36 publications

Exploring Probabilistic Network‐Based Modeling of Multidimensional Factors Associated with Country Risk

Exploring Probabilistic Network‐Based Modeling of Multidimensional Factors Associated with Country Risk

Effect of Cross-Departmental Collaboration on Performance: Evidence from the Federal Highway Administration

Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review

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