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In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts. [ABSTRACT FROM AUTHOR]

This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95. [ABSTRACT FROM AUTHOR]

Environmental flow (e-flow) decision making relies on flow-ecology models to predict ecological outcomes under different flow regimes. While expert knowledge has traditionally informed these models, there is increasing use of data-driven approaches. We investigated data integration for Bayesian conditional probability networks (CPNs) developed through expert elicitation in an e-flows assessment in Victoria, Australia. Using synthetic datasets based on monitoring data, we assessed the impact of varying data characteristics on model outcomes. Incorporating 10 years of data had the greatest influence on model predictions compared to the expert-based models, with diminishing additional effect for longer records. Notably, the expert model based on the most non-conforming expert opinion was more sensitive to data integration than a model based on harnessing the opinion of all experts, highlighting the importance of considering diverse experiential knowledge. We provide recommendations on leveraging limited datasets and models to guide efficient monitoring and management. • Exploration of a method to incorporate monitoring data into expert-opinion built Bayesian CPNs. • Synthetic data records of varying characteristics were used to assess impact on model outcomes. • Demonstrate that complementary use of expert opinion and empirical data can enrich modeling approaches. • Monitoring data can complement expert elicitation in models to assess uncertainty and direct management efforts. [ABSTRACT FROM AUTHOR]

• A new dependency assessment method was developed for human reliability analysis. • The method mathematically quantifies the conditional probability of a subsequent event. • The method analyzes six features of dependency between two events. • Quantitative bases for estimating the conditional probability are provided. • Two application studies demonstrate the feasibility of the proposed method. Dependency assessment is an aspect of human reliability analysis that identifies the causal relationship between two human events and quantifies the conditional probability of the successor event when two or more events exist in an accident sequence. Despite broad recognition of the impact of dependency on the overall system risk, many experts have been concerned that most current methods are rooted in the THERP method without a sufficient theoretical and empirical basis for dependency models. In this study, we propose a method that calculates the conditional failure probability of a successor event based on quantitative evidence of the dependency between two human events. Quantitative assessment is performed by evaluating six features and integrating the failure probabilities due to the features into the assessment based on an arithmetic equation. The estimates obtained from this empirical data analysis, a statistical function for time insufficiency, and a sequence alignment algorithm were employed to support the basis of the calculation with several assumptions. Two case studies are presented to show the feasibility of the study and the result differences between the proposed and existing methods. Since this study presents a new approach to dependency assessment, additional issues to be tackled regarding the assumptions and technical bases used are discussed with further research directions. [ABSTRACT FROM AUTHOR]

We suggest a normative model for the evaluation of explanations B because A based on probabilistic conditional reasoning and compare it with empirical data. According to the modus ponens model of explanations, the probability of B because A should equal the joint probability of the conditional if A then B and the explanans A. We argue that B because A expresses the conjunction of A and B as well as positive relevance of A for B. In Study 1, participants (N = 80) judged the subjective probabilities of 20 sets of statements with a focus on belief-based reasoning under uncertainty. In Study 2, participants (N = 376) were assigned to one of six item sets for which we varied the inferential relevance of A for B to explore boundary conditions of our model. We assessed the performance of our model across a range of analyses and report results on the Equation, a fundamental model in research on probabilistic reasoning concerning the evaluation of conditionals. In both studies, results indicate that participants' belief in statements B because A followed model predictions systematically. However, a sizeable proportion of sets of beliefs contained at least one incoherence, indicating deviations from the norms of rationality suggested by our model. In addition, results of Study 2 lend support to the idea that inferential relevance may be relevant for the evaluation of both conditionals and explanations. [ABSTRACT FROM AUTHOR]