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Presents information sources and methodologies for modeling and simulating banking system stability Combining both academic and institutional knowledge and experience, Banking Systems Simulation: Theory, Practice, and Application of Modeling Shocks, Losses, and Contagion presents banking system risk modeling clearly within a theoretical framework. Written from the global financial perspective, the book explores single bank risk, common bank exposures, and contagion, and how these apply on a systemic level. Zedda approaches these simulation methods logically by providing the basic building blocks of modeling and simulation, and then delving further into the individual techniques that make up a systems model. In addition, the author provides clear and detailed explanations of the foundational research into the mathematical and legal concepts used to analyze banking risk problems, measures and data for representing the main banking risk sources, and the major problems researchers are likely to encounter. There are numerous software descriptions throughout, with references and tools to help readers gain a proper understanding of the presented techniques and possibly develop new applications and research. The book concludes with an appendix that features real-world datasets and models. In addition, this book: - Provides a comprehensive overview of methods for analyzing models and simulating risk for banking and financial systems - Provides a clear presentation of the technical and legal concepts used in banking regulation - Presents unique insights from an expert's perspective, with specific coverage of assessing risks and developing what-if analyses at the systems level - Concludes with a discussion of applications, including banking systems regulation what-if tests, cost-benefit analysis, evaluations of banking systems stability effects on public finances, dimensioning, and risk-based contributions for Deposit Guarantee Schemes (DGS) and Resolution Funds Banking Systems Simulation: Theory, Practice, and Application of Modeling Shocks, Losses, and Contagion is ideal for banking researchers focusing on computational methods of analysis as well as an appropriate reference for graduate-level students in banking, finance, and computational methods. Stefano Zedda is Researcher in Financial Mathematics at the University of Cagliari in Italy and qualified as associate professor in banking and corporate finance. His research is mainly focused on quantitative analyses for banking and finance, with a particular focus on banking systems modeling and simulation. In 2008, Zedda developed the mathematical modeling and software implementation of the Systemic Model for Banking Originated Losses (SYMBOL), further developed during his activity at the European Commission. The Commission subsequently adopted it as a standard tool for testing banking regulation proposals. Stefano Zedda's research interests include banking, financial mathematics, and statistics, specifically simulation of banking and financial systems stability, banking regulation impact assessment, and interactive agent simulation.

The complete guide to the principles and practice of risk quantification for business applications.  The assessment and quantification of risk provide an indispensable part of robust decision-making; to be effective, many professionals need a firm grasp of both the fundamental concepts and of the tools of the trade.  Business Risk and Simulation Modelling in Practice  is a comprehensive, in–depth, and practical guide that aims to help business risk managers, modelling analysts and general management to understand, conduct and use quantitative risk assessment and uncertainty modelling in their own situations. Key content areas include: * Detailed descriptions of risk assessment processes, their objectives and uses, possible approaches to risk quantification, and their associated decision-benefits and organisational challenges. * Principles and techniques in the design of risk models, including the similarities and differences with traditional financial models, and the enhancements that risk modelling can provide. * In depth coverage of the principles and concepts in simulation methods, the statistical measurement of risk, the use and selection of probability distributions, the creation of dependency relationships, the alignment of risk modelling activities with general risk assessment processes, and a range of Excel modelling techniques. * The implementation of simulation techniques using both Excel/VBA macros and the @RISK Excel add-in. Each platform may be appropriate depending on the context, whereas the core modelling concepts and risk assessment contexts are largely the same in each case. Some additional features and key benefits of using @RISK are also covered.  Business Risk and Simulation Modelling in Practice  reflects the author?s many years in training and consultancy in these areas. It provides clear and complete guidance, enhanced with an expert perspective. It uses approximately one hundred practical and real-life models to demonstrate all key concepts and techniques; these are accessible on the companion website.

Intro -- Business Risk and Simulation Modelling in Practice -- Contents -- Preface -- About the Author -- About the Website -- PART I An Introduction to Risk Assessment - Its Uses, Processes, Approaches, Benefits and Challenges -- 1 The Context and Uses of Risk Assessment -- 1.1 Risk Assessment Examples -- 1.1.1 Everyday Examples of Risk Management -- 1.1.2 Prominent Risk Management Failures -- 1.2 General Challenges in Decision-Making Processes -- 1.2.1 Balancing Intuition with Rationality -- 1.2.2 The Presence of Biases -- 1.3 Key Drivers of the Need for Formalised Risk Assessment in Business Contexts -- 1.3.1 Complexity -- 1.3.2 Scale -- 1.3.3 Authority and Responsibility to Identify and Execute Risk-Response Measures -- 1.3.4 Corporate Governance Guidelines -- 1.3.5 General Organisational Effectiveness and the Creation of Competitive Advantage -- 1.3.6 Quantification Requirements -- 1.3.7 Reflecting Risk Tolerances in Decisions and in Business Design -- 1.4 The Objectives and Uses of General Risk Assessment -- 1.4.1 Adapt and Improve the Design and Structure of Plans and Projects -- 1.4.2 Achieve Optimal Risk Mitigation within Revised Plans -- 1.4.3 Evaluate Projects, Set Targets and Reflect Risk Tolerances in Decision-Making -- 1.4.4 Manage Projects Effectively -- 1.4.5 Construct, Select and Optimise Business and Project Portfolios -- 1.4.6 Support the Creation of Strategic Options and Corporate Planning -- 2 Key Stages of the General Risk Assessment Process -- 2.1 Overview of the Process Stages -- 2.2 Process Iterations -- 2.3 Risk Identification -- 2.3.1 The Importance of a Robust Risk Identification Step -- 2.3.2 Bringing Structure into the Process -- 2.3.3 Distinguishing Variability from Decision Risks -- 2.3.4 Distinguishing Business Issues from Risks -- 2.3.5 Risk Identification in Quantitative Approaches: Additional Considerations.

Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions. Bill & Melinda Gates Foundation.

Purpose The purpose of this paper is to investigate models and methods for managing supply chain risks and delays in construction projects. Design/methodology/approach The study mainly employs quantitative analysis in order to identify disruptions in construction supply chains. It also uses paradigms of simulation modeling, which are suitable for risk assessment and management. Both qualitative and quantitative data were collected through a literature review and details of specific construction projects, respectively. A dynamic modeling method was used, and the model was provided with an event-based simulation. Simulation modeling was used to measure the performance of the system. Findings The study shows the benefits of applying the dynamic modeling method to a construction project. Using event-based simulation, it was found that construction delays influence both the magnitude and the probability of disruption. This method contributes to the existing theoretical foundations of risk management practices, since it also considers the time factor. This method supplements the Monte Carlo statistical simulation method, which has no time representation. Using empirical analysis, the study proposes increasing the safety stock of construction materials at the distribution center, so as to mitigate risks in the construction supply chain. Research limitations/implications The research considers a single case of a hypothetical construction project. The simulation models represent a simple supply chain with only one supplier. The calculations are based on the current economic scenario, which will of course change over time. Practical implications The outcomes of the study show that the introduction of a safety stock of construction materials at the distribution center can prevent supply chain disruption. Since the consideration of risks at all stages of construction supply chain is essential to investors, entrepreneurs and regulatory bodies, the adoption of new approaches for their management during strategic planning of the investment projects is essential. Originality/value This dynamic modeling method is used in combination with the Monte Carlo simulation, thus, providing an explicit cause-and-effect dependency over time, as well as a distributed value of outcomes.

Strategies to prevent or delay Alzheimer’s disease and related dementias (AD/ADRD) are urgently needed, and blood pressure (BP) management is a promising strategy. Yet the effects of different BP control strategies across the life course on AD/ADRD are unknown. Randomized trials may be infeasible due to prolonged follow-up and large sample sizes. Simulation analysis is a practical approach to estimating these effects using the best available existing data. However, existing simulation frameworks cannot estimate the effects of BP control on both dementia and cardiovascular disease. This manuscript describes the design principles, implementation details, and population-level validation of a novel population-health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP-COG) study of the effect of BP levels over the life course on dementia and cardiovascular disease. MICROSIM is an agent-based Monte Carlo simulation designed using computer programming best practices. MICROSIM estimates annual vascular risk factor levels and transition probabilities in all-cause dementia, stroke, myocardial infarction, and mortality in a nationally representative sample of US adults 18+ using the National Health and Nutrition Examination Survey (NHANES). MICROSIM models changes in risk factors over time, cognition and dementia using changes from a pooled dataset of individual participant data from 6 US prospective cardiovascular cohort studies. Cardiovascular risks were estimated using a widely used risk model and BP treatment effects were derived from meta-analyses of randomized trials. MICROSIM is an extensible, open-source framework designed to estimate the population-level impact of different BP management strategies and reproduces US population-level estimates of BP and other vascular risk factors levels, their change over time, and incident all-cause dementia, stroke, myocardial infarction, and mortality.

A large number of studies have applied simulation to a multitude of issues relating to healthcare. These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources. In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high-quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies' results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied to healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques in solving diverse healthcare problems.

The Yangtze River is one of the most important rivers in China due to its large basin size, the large population along the river, and the numerous large dams and reservoirs on the river. The Jinsha River, the upper reach of the Yangtze River, was dammed twice recently at Baige, Tibet, one on 10 October 2018 and the other on 3 November 2018 (UTC + 8). Accordingly, two large landslide dams, 61 m and 96 m in height to the lowest dam crest, were formed in a 3-week interval. Due to the large inflow rates at the time of damming, the barrier lake level rose rapidly, posing huge risks to the downstream residents and properties. In managing the landslide dam risk, one of the important tasks is to predict the dam breaching flood beforehand. This paper focuses on rapid prediction of the dam breaching hydrograph and breach geometric parameters of the two landslide dams. The predictions were made timely before the breaching of the two landslide dams using both erosion-based empirical equations and numerical simulation and were refined based on detailed field investigation at the site after breaching. Comprehensive field investigations were conducted to determine the geological structures of the landslide dams, characterize the erodibility of dam materials, and measure the final beach dimensions. The simulated dam breaching processes, outflow hydrographs, lake water level changes, and final breach dimensions were validated by field observations. Compared with the hypothetical scenario without a diversion channel on the second landslide dam, a diversion channel 15 m in depth successfully lowered the peak flood discharge by about one third and helped to mitigate the flood risk significantly. The analysis outcome serves as basis for warning and evacuation of the downstream residents and making appropriate engineering risk mitigation plans.

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.