Estimating nuclear proliferation and security risks in emerging markets using Bayesian Belief Networks

Carless, Travis S.; Redus, Kenneth; Dryden, Rachel

doi:10.1016/j.enpol.2021.112549

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…BN is the core method in NRAM because of its powerful reasoning ability. It has attracted much attention in safety and risk analysis fields, such as nuclear proliferation and security risks (Carless et al., 2021), risk reduction for recycled water (Beaudequin et al., 2016), and safety management of urban gas pipeline network (Deng et al., 2022; F. Li et al., 2019). In addition, BN can represent the interdependence well between confounding factors based on a large amount of data.…”

Section: Methodsmentioning

confidence: 99%

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

et al. 2023

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COVID‐19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision‐making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision‐making in the context of COVID‐19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID‐19 at the national level.

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Section: Methodsmentioning

confidence: 99%

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

et al. 2023

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“…Factors using belief structures synthesize individual belief structures into group trust structures and rank expected risk results using the minimax regret approach (MRA). Risk analysis in energy is a popular topic and is of great importance, especially when it comes to the security of supply and safety through the use of nuclear, thermal, or renewable sources [57][58][59].…”

Section: Implementing the Mcdm And Other Methods To Rpnmentioning

confidence: 99%

Risk Evaluation: Brief Review and Innovation Model Based on Fuzzy Logic and MCDM

et al. 2022

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The risk assessment of engineering systems represents an important part of the quality of service and dependability. The existing methods for risk evaluation use crisp sets for rating partial indicators’ proposition and their cumulative products as an overall indicator. In this paper, existing FMEA and FMECA methods have been improved using the fuzzy expert system for calculating the risk priority number. The application of fuzzy logic allows the use of linguistic descriptions for risk analysis. In this way, the state of the system in terms of risks and consequences is better described. The settings of the fuzzy systems are based on the application of two multi-criteria decision-making methods. The AHP method was used to define the mutual relationship of the impact of partial indicators (occurrence, severity, and detectability) on risk. In this way, subjectivity in risk assessment is reduced. In the composition of the fuzzy model, the TOPSIS method is introduced to reduce the dissipation of results, which contributes to the accuracy of the outcome. This contributes to the accuracy of the results. The results were verified through a case study of a complex engineering system—bucket-wheel excavators. The risk was observed from the aspect of the danger of damage and the danger of downtime. The initial information for weak points of ES is defined according to historical damage events and statistics of downtime. Expert knowledge was used for weak points grading in the model. Additional model verification was performed using similar methods, using the same input data. The innovative model, presented in the paper, shows that it is possible to correct different weights of risk indicators. The obtained results show less dispersion compared with other existing methods. Weak points with increased risk have been located, and an algorithm has been proposed for risk-based maintenance application and implementation.

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“…To be sure, social science work on slow‐motion variables in world politics is not as rooted in rigorous science as structural engineering. But it offers grounds for optimism that experts could do something analogous: (a) identify chronic risk‐amplifiers and attenuators for nuclear proliferation (Bas & Coe, 2016; Bell, 2015; Carless et al, 2021; Cimbala, 2017; Kaplow & Gartzke, 2016; Solingen, 2007, 2018), secession (Black, 2004; Levinson, 2017; Siroky & Abbasov, 2021) and interstate border disputes (Carter & Poast, 2016; Goemans & Schultz, 2016; Huth, 1996); (b) synthesize evidence into causal‐propensity assessments for the initial 5 years and convert those assessments into probability judgments on a bounded 0–1.0 scale; (c) adjust their initial probability judgments as time widens to 10 and 25 years; (d) average individual forecasts into composites to further enhance accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…To be sure, social science work on slowmotion variables in world politics is not as rooted in rigorous science as structural engineering. But it offers grounds for optimism that experts could do something analogous: (a) identify chronic riskamplifiers and attenuators for nuclear proliferation (Bas & Coe, 2016;Bell, 2015;Carless et al, 2021;Cimbala, 2017;Kaplow & Gartzke, 2016;Solingen, 2007Solingen, , 2018, secession (Black, 2004;Levinson, 2017;Siroky & Abbasov, 2021) and interstate border disputes (Carter & Poast, 2016;Goemans & Schultz, 2016;Huth, 1996) viewpoint-variable hypotheses bearing on the connections among ideology, forecasts, and forecasting accuracy. Each set of hypotheses is purely correlational-and rests on the working assumption that the present contains clues to the future that observers with the requisite skills and mindsets are better at spotting.…”

mentioning

confidence: 99%

Long‐range subjective‐probability forecasts of slow‐motion variables in world politics: Exploring limits on expert judgment

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Karvetski²,

Satopää

et al. 2023

Futures & Foresight Science

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Skeptics see long‐range geopolitical forecasting as quixotic. A more nuanced view is that although predictability tends to decline over time, its rate of descent is variable. The current study gives geopolitical forecasters a sporting chance by focusing on slow‐motion variables with low base rates of change. Analyses of 5, 10, and 25‐year cumulative‐risk judgments made in 1988 and 1997 revealed: (a) specialists beat generalists at predicting nuclear proliferation but not shifting nation‐state boundaries; (b) some counterfactual interventions—for example, Iran gets the bomb before 2022—boosted experts’ edge but others—for example, nuclear war before 2022—eliminated it; (c) accuracy fell faster on topics where expertise conferred no edge in shorter‐range forecasts. To accelerate scientific progress, we propose adversarial collaborations in which clashing schools of thought negotiate Bayesian reputational bets on divisive issues and use Lakatosian scorecards to incentivize the honoring of bets.

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Estimating nuclear proliferation and security risks in emerging markets using Bayesian Belief Networks

Cited by 13 publications

References 25 publications

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

Risk Evaluation: Brief Review and Innovation Model Based on Fuzzy Logic and MCDM

Long‐range subjective‐probability forecasts of slow‐motion variables in world politics: Exploring limits on expert judgment

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