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Team assembly is an important issue when collaborative or cooperative activities between people are carried out. In the educational context, it seems relevant to us to consider in the assembly process how students interact with others. Therefore, it is appropriate to carry out a process to model social behavior. In turn, the experts will be able to use the modelling information to establish the appropriate criteria to differentiate between assembly strategies according to the educational contexts. Our social behavior modelling process proposal is based on the information that can be extracted from the interactions and is guided by experts. We propose an approach divided into steps. Firstly, social network analysis metrics are used to study students'social interactions. Secondly, a set of social interactions attributes are offered so that experts can organize them hierarchically. Thirdly, quantitative information on the students' relationship can be inferred using an influence diagram and hierarchically ordered attributes. Once the modelling process is out, the qualitative information may be used to improve the team assembly. In this paper we describe two experiences where we have put our approach into practice. The students made a peer review task. However, the pedagogical strategies that experts followed were different for each experience. [ABSTRACT FROM AUTHOR]

A statistical process control (SPC) model is introduced that incorporates sample data on the number of defectives and allows the probability that an assignable cause of variation in each time interval of a finite production process to be nonconstant. A Limited Memory Influence Diagram (LIMID) model is implemented to facilitate the decision in each time interval on whether or not to investigate the potential presence of the assignable cause of variation and restore the process to working condition. The method determines control limits that minimize the costs of inspection, repair, sampling, and operating under the assignable cause of variation. Sample size and sampling intervals can also be adjusted to further reduce the costs of maintaining quality control. This method expands on the capabilities of models that assume that the conditional probability of an assignable cause occurring remains constant throughout the production horizon. [ABSTRACT FROM AUTHOR]

Abstract Social networks (SNs) have become popular as a medium for disseminating information and connecting like-minded people. They play a central role in decision-making by correlating the behaviors and preferences of connected agents. However, it is difficult to identify social influence effects in decision-making. In this article, we propose a framework of how to describe the uncertain nature of the social network group decision-making (SN-GDM) process. Social networks analysis (SNA) and quantum probability theory (QPT) are combined to construct a decision framework considering superposition and interference effects in SN-GDM scenarios. For the first time, we divide interference effects into symmetry and asymmetry. We construct an influence diagram, which is a quantum-like Bayesian network (QLBN), to model group decisions with interactions. We identify symmetry interference terms from Shapley value and asymmetry interference terms from trust value, respectively. The probability of an alternative is calculated through quantum probability theory in our influence diagram. The combination of QLBN model and social network could gain an understanding of how the group preferences evolve within SN-GDM scenarios, and provide new insights into SNA. Finally, an overall comparative analysis is performed with traditional SNA and other quantum decision models.

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The special geographical environment and functions of seaports make them extremely vulnerable to natural threats, notably tropical cyclones (TCs). Existing methods to quantify disruption to seaports may not offer a holistic understanding of relationship between the disaster data and the complex composition of the seaport. To resolve this issue, this study introduces a new framework combining physics-based modeling and influence diagrams to analyze the potential disruptions faced by seaports during TCs. To assess the validity of the proposed framework, the Haikou Port is used as a testbed. Six different historical typhoon scenarios are used to compare between the real operation state and the produced results. Findings reveal that the new framework is effective in capturing the decline in port operational capacity during TCs, particularly for seaports like Haikou that demonstrate high resilience to TCs. Overall, this study presents a valuable perspective for assessing and forecasting seaport disruptions facing TCs. [ABSTRACT FROM AUTHOR]

Systems models of the Food–Water–Energy (FWE) nexus face a conceptual difficulty: the systematic integration of local stakeholder perspectives into a coherent framework for analysis. We present a novel procedure to co-produce and systematize the real-life complexity of stakeholder knowledge and forge it into a clear-cut set of challenges. These are clustered into the Pressure–State–Response (PSIR) framework, which ultimately guides the development of a conceptual systems model closely attuned to the needs of local stakeholders. We apply this approach to the case of the emerging megacity Pune and the Bhima basin in India. Through stakeholder workshops, involving 75 resource users and experts, we identified 22 individual challenges. They include exogenous pressures, such as climate change and urbanization, and endogenous pressures, such as agricultural groundwater over-abstraction and land use change. These pressures alter the Bhima basin's system state, characterized by inefficient water and energy supply systems and regional scarcity. The consequent impacts on society encompass the inadequate provision with food, water, and energy and livelihood challenges for farmers in the basin. An evaluation of policy responses within the conceptual systems model shows the complex cause–effect interactions between nexus subsystems. One single response action, such as the promotion of solar farming, can affect multiple challenges. The resulting concise picture of the regional FWE system serves resource users, policymakers, and researchers to evaluate long-term policies within the context of the urban FWE system. While the presented results are specific to the case study, the approach can be transferred to any other FWE nexus system. [ABSTRACT FROM AUTHOR]

A limited memory influence diagram model is utilized for statistical process control in a system where qualitative data on the number of defectives in a sample is available in each period of a finite production horizon. Based on the number of defective units observed, a decision is made on whether to stop the process and repair an assignable cause of variation. The model has no requirement to maintain the history of sample results and corrective actions taken in previous sampling periods. Despite the limited memory of process history, the nature of the decision rule and solution algorithm result in quality-related costs that are comparable to those of existing methods for Bayesian quality control with information on number of defectives. When sampling interval and sample size can be selected as design parameters for a system with a fixed time between scheduled maintenance, the limited memory influence diagram model can be constructed to provide similar costs with reduced computational requirement as compared with a partially observed Markov decision process technique. [ABSTRACT FROM AUTHOR]

The aim of this paper is to answer the following questions: What is meant by models of decision analysis, philosophy, and components? What is the role of decision models in analyzing the decision problem to reach the expected value? What is the logical relationship between the two models of decision analysis: the influence diagram, the decision tree, in solving the decision problem? In order to answer the research questions, the steps for building an influence diagram and how to draw it, as well as steps for building a decision tree and how to draw it, were presented. This research also clarified how to turn the tree back and reach the best option using the expected financial value base, and the importance of the role of sensitivity analysis in choosing assumptions and structured alternatives in decision analysis as one of the many advantages of using typical decision analysis in decision-making and decision-making. Based on decision analysis and the use of the two models, the best option is to have a math lab with an expert and pay eight thousand dollars, and to achieve material value (conceptual structures, facts and skills) and moral value (appreciation and respect). And use the evaluation process by conducting achievement tests. Refer to studies and research that address controversial issues of the use of the Mathematics and Computer Laboratory in education. [ABSTRACT FROM AUTHOR]

Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy among people, processes, and ML, this synergy is not often realized because ML algorithms do not yet connect the dots on fully understanding and strategically fostering student success. Transitioning from risk to impact prediction is a catalyst for institutional transformation, which can lead to continuous learning and student-success process innovation. This paper explores how ML can complement and facilitate organizational transformation in promoting a culture of care and innovation through virtuous full-cycle learning.

Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy among people, processes, and ML, this synergy is not often realized because ML algorithms do not yet connect the dots on fully understanding and strategically fostering student success. Transitioning from risk to impact prediction is a catalyst for institutional transformation, which can lead to continuous learning and student-success process innovation. This paper explores how ML can complement and facilitate organizational transformation in promoting a culture of care and innovation through virtuous full-cycle learning. [ABSTRACT FROM AUTHOR]

Abstract Ecological understanding of host–pathogen dynamics is the basis for managing wildlife diseases. Since 2008, federal, state, and provincial agencies and tribal and private organizations have collaborated on bat and white‐nose syndrome (WNS) surveillance and monitoring, research, and management programs. Accordingly, scientists and managers have learned a lot about the hosts, pathogen, and dynamics of WNS. However, effective mitigation measures to combat WNS remain elusive. Host–pathogen systems are complex, and identifying ecological research priorities to improve management, choosing among various actions, and deciding when to implement those actions can be challenging. Through a cross‐disciplinary approach, a group of diverse subject matter experts created an influence diagram used to identify uncertainties and prioritize research needs for WNS management. Critical knowledge gaps were identified, particularly with respect to how WNS dynamics and impacts may differ among bat species. We highlight critical uncertainties and identify targets for WNS research. This tool can be used to maximize the likelihood of achieving bat conservation goals within the context and limitations of specific real‐world scenarios.

Ecological understanding of host–pathogen dynamics is the basis for managing wildlife diseases. Since 2008, federal, state, and provincial agencies and tribal and private organizations have collaborated on bat and white‐nose syndrome (WNS) surveillance and monitoring, research, and management programs. Accordingly, scientists and managers have learned a lot about the hosts, pathogen, and dynamics of WNS. However, effective mitigation measures to combat WNS remain elusive. Host–pathogen systems are complex, and identifying ecological research priorities to improve management, choosing among various actions, and deciding when to implement those actions can be challenging. Through a cross‐disciplinary approach, a group of diverse subject matter experts created an influence diagram used to identify uncertainties and prioritize research needs for WNS management. Critical knowledge gaps were identified, particularly with respect to how WNS dynamics and impacts may differ among bat species. We highlight critical uncertainties and identify targets for WNS research. This tool can be used to maximize the likelihood of achieving bat conservation goals within the context and limitations of specific real‐world scenarios. [ABSTRACT FROM AUTHOR]

Risk management is deemed as a critical process toward achieving the objectives of any project. A number of tools and techniques have been developed to help project managers reduce the risk involved in complex projects as risk is generally perceived as a negative event. However, uncertainty, which may lead to risk events may well result in opportunities. With the main aim of managing both risk and opportunity within an interdependent setting of interacting project uncertainties, we present a new thinking, called 'uncertainty thinking', a process model, a Bayesian Belief Network and Influence Diagram based modeling approach to implement uncertainty thinking in practice. We demonstrate the implementation of the developed uncertainty modeling approach through an illustrative case. We introduce new loss and opportunity metrics to help project managers prioritize uncertain variables within a network setting taking into account interdependencies and develop an optimization scheme for selecting loss reduction and opportunity exploitation strategies in accordance with the decision maker's loss averse/gain seeking preferences. The results reveal the importance of modeling both risk and opportunity in managing project uncertainties as focusing only on the negative connotation of risk may result in selecting sub-optimal decisions. Furthermore, we also establish that minimization of the expected loss across a risk network does not necessarily maximize the expected gain. [ABSTRACT FROM AUTHOR]

Extended abstract So far, the changes have been done in human resources planning practices (Van Buren et al, 2011: 211). In the process of strategic planning, fast performance is required. But the complexity and extreme environmental uncertainty and the need to solve them, is equal to use some methods in which the weaknesses of strategic planning as much as possible would cover (Chatfields, 2005: 19; Porter, 2004: 288; Frechtling, 2001: 21). In conditions of extreme uncertainty, using the methods and models of strategic planning is fraught with many problems (Glenn & Gordon, 2003: 2). Futures studies should be one of our key defenses against the challenges of uncertainty and it should make it possible to predict dangerous trends and recognize desirable futures and respond to it properly (Hejazi, 2011:84). Case of the study The research was conducted amongst the internal and external stakeholders of Esfahan Technical and Vocational Training Organization. The study population included seventeen participants of the organization stakeholders who were identified during the futures studies workshop in the preparation step of our model. Materials and Methods In this study, in order to answer the research questions, the fuzzy DEMATEL technique and customized hybrid Roland Berger - HHL Leipzig approach have been used. In this approach, the modified Framing checklist, 360° stakeholder feedback, the two-step questionnaire of impact/uncertainty identification, Impact/Uncertainty Grid, scenario matrix, Fuzzy DEMATEL technique and Influence Diagram were used. In this study, futuristic preparation step includes the steps of determining the purpose, methods, participants, stakeholders, resource requirements, time horizon, level of analysis and communication. Also the meaning-making future step includes enviroment review, analysis, interpretation and prospection. The study population included seventeen participants of the vocational Isfahan stakeholders who were identified during the futures studies workshop in the preparation step of our model. According to the features that each participant should have, these members were selected purposefully and judgmentally. The study aims to design Influence Diagram of strategic human resource planning considering futures studies approach in Esfahan Technical and Vocational Training Organization. Main question Q1: how is the Influence Diagram of strategic human resource planning considering futures studies approach in Esfahan Technical and Vocational Training Organization? Secondary questions: Q1a: What are the weak signals and blind spots related to influence factors and uncertainties of the model of strategic human resource planning considering futures studies approach? Q1b: How much is the importance level of influnce diagram factors? in Esfahan Technical and Vocational Training Organization by using of customized hybrid Roland Berger - HHL Leipzig approach, after preparation of Framing checklist and 360° stakeholder feedback and identification of influence factors and uncertainties and weak signals and blind spots, we used the Impact/Uncertainty grid to identify the trends, less important factors and critical uncertainties. Then, by using of the critical uncertainties, two general classifications to identify key uncertainties have created and through positive and negative development of them on the matrix axes and during a brain storming session, we have developed four differenet scenarios titled “organization fortress”, “organization under siege”, “safe environment” and “organization decline”. Finally, while making use of trends and uncertainties and by using of fuzzy DEMATEL technique, causal relations chain and strategic leverage that exists behind every scenario clarified. Eventually by using the influence diagram, some developments are shown that if they happen untill the year 1400 (2021), it can develop the key uncertainties of human resource planning of Esfahan Technical and Vocational Training Organization in positive or negative direction. Discussion and Conclusion The employment rate, increasing organization budget, policies to attract competitive human resources, the realization of the 1404 Development Plan particularly in the field of human resources, different rules of insurance and pension, equal employment opportunity programs, job promotion programs were identified as influence factors. Blind spots related to influence factors include Barjam change (Joint Comprehensive Plan of Action), increasing organization budget, employment and unemployment rates, stable political situation and etc. Blind spots related to unsectainty include Brjam change (Joint Comprehensive Plan of Action), removing the sanctions, professional trainings, jobs specialization and etc. Absence of weak signals shows that all participants have a common understanding of influencing factors and uncertainties. Then, by using of impact/uncertainty grid, it became clear that the development of small businesses, governmental strict rules on human resource planning, different insurance and pension laws are the less important factors that even can be negleted during the scenarios development. Different trends were recognized in which employment and unemployment, professional trainings, equal employment opportunity programs, global developments have high influence potential and unsertainty. Five critical uncertainties such as budget increasing, Brjam change (Joint Comprehensive Plan of Action), the realization of the 1404 development plan, new approaches of public administration and new combinations of stackholders groups that have the highest impact and uncertainty were identified. By putting these factors in two different groups in titled organization related laws and economical look of organization, four dimentions of scenario matrix were developed and finally four scenarios were developed and by using of influence diagram, the details of the relations and their importance level were recognized. The results of the fuzzy DEMATEL technique clarified that some factors affect the others and also their level of importance. Realization or failure to achieve the goals of the 1404 document has highest rank. Realization of it could lead to increased funding and support the organizations to increase the recruitment and use of new approaches of public administration that has been mentioned in Iran 1404 document. Changes in budget especially budget increasing, has second position because it can directly provide the prerequisites and requirements on the one hand, and the implementation process of strategic planning of human resources considering the futures studies concepts. The organization should try to influence the governors through lobbying and working closely with them to increase the organization's budget. New approaches in public administration, such as outsourcing, downsizing, privatization, merger, partnership or joint management are in next places in terms of importance. Respectively the next places in terms of importance belong to political support for increasing employment, new combination of internal and external stakeholders, Brjam change (Joint Comprehensive Plan of Action), new directions of organizational strategies, professional training, changes in inflation, employment and unemployment rates, changing in number and scope of activities of institutions, management changing especially senior managers, the diverse organization's objectives.

Processing facilities handling large amounts of hazardous substances are attractive targets for terrorists. Thus, these work sites are exposed not only to accidents but also to intentional threats. Some research has separately studied risk caused by either potential accidental events or terrorist acts. However, studies focusing on integrated risk assessment and management (dealing with both safety and security issues) are lacking. This paper proposes an approach to assess and manage integrated risks. This method is based on an influence diagram which incorporates safety and security‐related factors into one framework. It considers the effects of management actions on both accidental and intentional risks. This method can help to detect hidden risk (i.e., the risk not recognized during design and operation stages) and ensure to reduce the real risk to an acceptable level by guiding the selection of management actions. The effectiveness of the proposed method is demonstrated using the overfilling risk management of an oil tank. © 2018 American Institute of Chemical Engineers Process Process Saf Prog 38: 42–51, 2019 [ABSTRACT FROM AUTHOR]

Most existing risk analysis methods focus on analysing risks that a system might face throughout its life. However, there is no explicit method for risk analysis during incidents. Approaches such as bow-ties and attack trees provide reliable information about triggers and escalation of incidents, but do not cover risk evaluation. Risk matrices include the entire risk analysis process; however, their risk evaluation approach is oversimplified. This paper presents a General Model for Incident Risk Analysis, which formalises the incident risk analysis process through an influence diagram. Our aim is to provide a decision support model that generates reliable risk information and enhances incident risk evaluation. [ABSTRACT FROM AUTHOR]

Highlights • Analysing the Process Variables (PVs) given perturbations in time series. • Uncertainty related to parameters, models and historical data was investigated. • Maintenance time schedule optimization using Markov degradation model. • Characterizing the reliability of inspection by one-dimensional exponential model. • The model was examined on Natural Gas Regulating and Metering Stations (NGRMS). Abstract In this paper, a risk-based optimization methodology for a maintenance schedule considering Process Variables (PVs), is developed within the framework of asset integrity assessment. To this end, an integration of Dynamic Bayesian Network, Damage Modelling and sensitivity analysis are implemented to clarify the behaviour of failure probability, considering the exogenous undisciplinable perturbations. Discrete time case is considered through measuring or observing the PVs. Decision configurations and utility nodes are defined inside the network to represent maintenance activities and their associated costs. The regression analysis is considered to model the impact of perturbations on PVs for future applications. The developed methodology is applied to a case study of Chemical Plant (Natural Gas Regulating and Metering Stations). To demonstrate the applicability of the methodology, three cases of seasonal observations of specific PV (pressure) are considered. The proposed methodology could either analyse the failure based on precursor data of PVs or obtain the optimum maintenance schedule based on actual condition of the systems. [ABSTRACT FROM AUTHOR]