Large-Scale Evacuation introduces the reader to the steps involved in evacuation modelling for towns and cities, from understanding the hazards that can require large-scale evacuations, through understanding how local officials decide to issue evacuation advisories and households decide whether to comply, to transportation simulation and traffic management strategies. The author team has been recognized internationally for their research and consulting experience in the field of evacuations. Collectively, they have 125 years of experience in evacuation, including more than 140 projects for federal and state agencies. The text explains how to model evacuations that use the road transportation network by combining perspectives from social scientists and transportation engineers, fields that have commonly approached evacuation modelling from distinctly different perspectives. In doing so, it offers a step-by-step guide through the key questions needed to model an evacuation and its impacts to the evacuation route system as well as evacuation management strategies for influencing demand and expanding capacity. The authors also demonstrate how to simulate the resulting traffic and evacuation management strategies that can be used to facilitate evacuee movement and reduce unnecessary demand. Case studies, which identify key points to analyze in an evacuation plan, discuss evacuation termination and re-entry, and highlight challenges that someone developing an evacuation plan or model should expect, are also included. This textbook will be of interest to researchers, practitioners, and advanced students.

The purpose of this book is to share with teachers a successful coaching model that has been researched, designed, piloted, evaluated and used across a range of schools. It is a peer coaching model which teachers use with teachers. It is a model which, as a coach or coachee, both parties will learn from. While the model is directed to teachers, it is equally applicable and transferable to other professions.The book is clear and concise with relevant background information, a step-by-step process, and includes case studies.

In today's world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.

Enterprise Risk Management: Advances on its Foundation and Practice relates the fundamental enterprise risk management (ERM) concepts and current generic risk assessment and management principles that have been influential in redefining the risk field over the last decade. It defines ERM with a particular focus on understanding the nexus between risk, uncertainty, knowledge and performance. The book argues that there is critical need for ERM concepts, principles and methods to adapt to the latest and most influential risk management developments, as there are several issues with outdated ERM theories and practices; problems include the inability to effectively and systematically balance both opportunity and downside performance, or relying too much on narrow probability-based perspectives for risk assessment and decision-making. It expands traditional loss-based risk principles into new and innovative performance-risk frameworks, and presents fundamental risk principles that have recently been developed by the Society for Risk Analysis (SRA). All relevant statistical and risk concepts are clearly explained and interpreted using minimal mathematical notation. The focus of the book is centered around ideas and principles, more than technicalities. The book is primarily intended for risk professionals, researchers and graduate students in the fields of engineering and business, and should also be of interest to executive managers and policy makers with some background in quantitative methods such as statistics.

"Financial Risk Forecasting" is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques.

Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use - that risk is exogenous - and what happens when those assumptions are violated.

Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book.

The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing.

The book is accompanied by a website - www.financialriskforecasting.com - which features downloadable code as used in the book.

Prospect Theory: For Risk and Ambiguity provides the first comprehensive and accessible textbook treatment of the way decisions are made both when we have the statistical probabilities associated with uncertain future events (risk) and when we lack them (ambiguity). The book presents models, primarily prospect theory, that are both tractable and psychologically realistic. A method of presentation is chosen that makes the empirical meaning of each theoretical model completely transparent. Prospect theory has many applications in a wide variety of disciplines. The material in the book has been carefully organized to allow readers to select pathways through the book relevant to their own interests. With numerous exercises and worked examples, the book is ideally suited to the needs of students taking courses in decision theory in economics, mathematics, finance, psychology, management science, health, computer science, Bayesian statistics, and engineering.

This new edition of Risk Management: Concepts and Guidance supplies a look at risk in light of current information, yet remains grounded in the history of risk practice. Taking a holistic approach, it examines risk as a blend of environmental, programmatic, and situational concerns. Supplying comprehensive coverage of risk management tools, practices, and protocols, the book presents powerful techniques that can enhance organizational risk identification, assessment, and management-all within the project and program environments. Updated to reflect the Project Management Institute's A Guide to the Project Management Body of Knowledge (PMBOK (R) Guide), Fifth Edition, this edition is an ideal resource for those seeking Project Management Professional and Risk Management Professional certification. Emphasizing greater clarity on risk practice, this edition maintains a focus on the ability to apply "planned clairvoyance" to peer into the future. The book begins by analyzing the various systems that can be used to apply risk management. It provides a fundamental introduction to the basics associated with particular techniques, clarifying the essential concepts of risk and how they apply in projects. The second part of the book presents the specific techniques necessary to successfully implement the systems described in Part I. The text addresses project risk management from the project manager's perspective. It adopts PMI's perspective that risk is both a threat and an opportunity, and it acknowledges that any effective risk management practice must look at the potential positive events that may befall a project, as well as the negatives.Providing coverage of the concepts that many project management texts ignore, such as the risk response matrix and risk models, the book includes appendices filled with additional reference materials and supporting details that simplifying some of the most complex aspects of risk management.

This is an update and expansion upon PMI's popular reference, The Practice Standard for Project Risk Management. Risk Management addresses the fact that certain events or conditions may occur with impacts on project, program, and portfolio objectives. This standard will: identify the core principles for risk management; describe the fundamentals of risk management and the environment within which it is carried out; define the risk management life cycle; and apply risk management principles to the portfolio, program, and project domains within the context of an enterprise risk management approach It is primarily written for portfolio, program, and project managers, but is a useful tool for leaders and business consumers of risk management, and other stakeholders.

Enterprise Risk Management: Advances on its Foundation and Practice relates the fundamental enterprise risk management (ERM) concepts and current generic risk assessment and management principles that have been influential in redefining the risk field over the last decade. It defines ERM with a particular focus on understanding the nexus between risk, uncertainty, knowledge and performance. The book argues that there is critical need for ERM concepts, principles and methods to adapt to the latest and most influential risk management developments, as there are several issues with outdated ERM theories and practices; problems include the inability to effectively and systematically balance both opportunity and downside performance, or relying too much on narrow probability-based perspectives for risk assessment and decision-making. It expands traditional loss-based risk principles into new and innovative performance-risk frameworks, and presents fundamental risk principles that have recently been developed by the Society for Risk Analysis (SRA). All relevant statistical and risk concepts are clearly explained and interpreted using minimal mathematical notation. The focus of the book is centered around ideas and principles, more than technicalities. The book is primarily intended for risk professionals, researchers and graduate students in the fields of engineering and business, and should also be of interest to executive managers and policy makers with some background in quantitative methods such as statistics.

Given time, scientists reach consensus about the truth or falsity of a wide range of alleged hazards. Today, there is broad agreement that CFCs destroy stratospheric ozone. On the other hand, research does not support claims that electromagnetic fields from transmission lines cause a noticeable increase of leukemia. But new allegations continuously arise. Are manufactured chemicals in the environment distorting normal hormonal processes in our bodies? Are genetically modified foods a cause for concern? Addressing one of the most vexing problems in risk policy, Allan Mazur asks how we can tell, at an early stage, how seriously we should take a new warning. To identify hallmarks that could help predict the truth or falsity of an alleged hazard, Mazur analyzes 31 health warnings raised during the 1950s and 1960s about diverse technologies, including fluoridation, DDT, cyclamate, nuclear weapons testing, and birth control pills. Among his considerations are the initial source of an alarm, the biases held by its primary 'sponsors, ' and the type of media coverage it receives. With 30 to 50 years of hindsight, he identifies characteristics - apparent from the outset of a controversy - that most effectively distinguish true warnings from false alarms. Early recognition and a timely response to a genuine hazard are important to protect our environment, health, and economic well-being. But if we act quickly and a warning turns out to be false, money is wasted, people are needlessly frightened, regulators lose credibility, and our ability to appropriately handle the next set of risks is compromised. Mazur's findings do not provide certainty about which of today's warnings will prove true and which will prove false. But they do help us to make informed judgments about where it is best and most reasonable to direct our worries and resources.

This book provides a comprehensive demonstration of risk analysis as a distinct science covering risk understanding, assessment, perception, communication, management, governance and policy. It presents and discusses the key pillars of this science, and provides guidance on how to conduct high-quality risk analysis. The Science of Risk Analysis seeks to strengthen risk analysis as a field and science by summarizing and extending current work on the topic. It presents the foundation for a distinct risk field and science based on recent research, and explains the difference between applied risk analysis (to provide risk knowledge and tackle risk problems in relation to for example medicine, engineering, business or climate change) and generic risk analysis (on concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterise, communicate, manage and govern risk). The book clarifies and describes key risk science concepts, and builds on recent foundational work conducted by the Society for Risk Analysis in order to provide new perspectives on science and risk analysis. The topics covered are accompanied by cases and examples relating to current issues throughout. This book is essential reading for risk analysis professionals, scientists, students and practitioners, and will also be of interest to scientists and practitioners from other fields who apply risk analysis in their work.

This book provides a comprehensive demonstration of risk analysis as a distinct science covering risk understanding, assessment, perception, communication, management, governance and policy. It presents and discusses the key pillars of this science, and provides guidance on how to conduct high-quality risk analysis. The Science of Risk Analysis seeks to strengthen risk analysis as a field and science by summarizing and extending current work on the topic. It presents the foundation for a distinct risk field and science based on recent research, and explains the difference between applied risk analysis (to provide risk knowledge and tackle risk problems in relation to for example medicine, engineering, business or climate change) and generic risk analysis (on concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterise, communicate, manage and govern risk). The book clarifies and describes key risk science concepts, and builds on recent foundational work conducted by the Society for Risk Analysis in order to provide new perspectives on science and risk analysis. The topics covered are accompanied by cases and examples relating to current issues throughout. This book is essential reading for risk analysis professionals, scientists, students and practitioners, and will also be of interest to scientists and practitioners from other fields who apply risk analysis in their work.

Christian Hugo Hoffmann undermines the citadel of risk assessment and management, arguing that classical probability theory is not an adequate foundation for modeling systemic and extreme risk in complex financial systems. He proposes a new class of models which focus on the knowledge dimension by precisely describing market participants' own positions and their propensity to react to outside changes. The author closes his thesis by a synthetical reflection on methods and elaborates on the meaning of decision-making competency in a risk management context in banking. By choosing this poly-dimensional approach, the purpose of his work is to explore shortcomings of risk management approaches of financial institutions and to point out how they might be overcome.

Richard Pettigrew offers an extended investigation into a particular way of justifying the rational principles that govern our credences (or degrees of belief). The main principles that he justifies are the central tenets of Bayesian epistemology, though many other related principles are discussed along the way. These are: Probabilism, the claims that credences should obey the laws of probability; the Principal Principle, which says how credences in hypotheses about the objective chances should relate to credences in other propositions; the Principle of Indifference, which says that, in the absence of evidence, we should distribute our credences equally over all possibilities we entertain; and Conditionalization, the Bayesian account of how we should plan to respond when we receive new evidence. Ultimately, then, this book is a study in the foundations of Bayesianism. To justify these principles, Pettigrew looks to decision theory. He treats an agent's credences as if they were a choice she makes between different options, gives an account of the purely epistemic utility enjoyed by different sets of credences, and then appeals to the principles of decision theory to show that, when epistemic utility is measured in this way, the credences that violate the principles listed above are ruled out as irrational. The account of epistemic utility set out here is the veritist's: the sole fundamental source of epistemic utility for credences is their accuracy. Thus, Pettigrew conducts an investigation in the version of Iepistemic utility theory known as accuracy-first epistemology. The book can also be read as an extended reply on behalf of the veritist to the evidentialist's objection that veritism cannot account for certain evidential principles of credal rationality, such as the Principal Principle, the Principle of Indifference, and Conditionalization.

Lara Buchak sets out an original account of the principles that govern rational decision-making in the face of risk. A distinctive feature of these decisions is that individuals are forced to consider how their choices will turn out under various circumstances, and decide how to trade off the possibility that a choice will turn out well against the possibility that it will turn out poorly. The orthodox view is that there is only one acceptable way to do this: rational individuals must maximize expected utility. Buchak's contention, however, is that the orthodox theory (expected utility theory) dictates an overly narrow way in which considerations about risk can play a role in an individual's choices. Combining research from economics and philosophy, she argues for an alternative, more permissive, theory of decision-making: one that allows individuals to pay special attention to the worst-case or best-case scenario (among other 'global features' of gambles). This theory, risk-weighted expected utility theory, better captures the preferences of actual decision-makers. Furthermore, it isolates the distinct roles that beliefs, desires, and risk-attitudes play in decision-making. Finally, contra the orthodox view, Buchak argues that decision-makers whose preferences can be captured by risk-weighted expected utility theory are rational. Thus, Risk and Rationality is in many ways a vindication of the ordinary decision-maker-particularly his or her attitude towards risk-from the point of view of even ideal rationality.

Whilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today's social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears - who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progress?

A challenge to the conventional wisdom surrounding financial risk, providing insight into why easy solutions to control the financial system are doomed to fail Finance plays a key role in the prosperity of the modern world-but it also brings grave dangers. We seek to manage those threats with a vast array of sophisticated mathematical tools and techniques of financial risk management. Too often, though, we fail to address the greatest risk-the peril posed by our own behavior. Jon Danielsson argues that critical risk is generated from within, through the interactions of individuals and perpetuated by their beliefs, objectives, abilities, and prejudices. He asserts that the widespread belief that risk originates outside the financial system frustrates our ability to measure and manage it, and the likely consequences of new regulations will help alleviate small-scale risks but, perversely, encourage excessive risk taking. Danielsson uses lessons from past and recent crises to show that diversity is the best way to safeguard our financial system.

Risk analysis is not a narrowly defined set of applications. Rather, it is widely used to assess and manage a plethora of hazards that threaten dire implications. However, too few people actually understand what risk analysis can help us accomplish and, even among experts, knowledge is often limited to one or two applications. Explaining Risk Analysis frames risk analysis as a holistic planning process aimed at making better risk-informed decisions and emphasizing the connections between the parts. This framework requires an understanding of basic terms, including explanations of why there is no universal agreement about what risk means, much less risk assessment, risk management and risk analysis. Drawing on a wide range of case studies, the book illustrates the ways in which risk analysis can help lead to better decisions in a variety of scenarios, including the destruction of chemical weapons, management of nuclear waste and the response to passenger rail threats. The book demonstrates how the risk analysis process and the data, models and processes used in risk analysis will clarify, rather than obfuscate, decision-makers' options. This book will be of great interest to students and scholars of risk assessment, risk management, public health, environmental science, environmental economics and environmental psychology.

When making decisions, people naturally face uncertainty about the potential consequences of their actions due in part to limits in their capacity to represent, evaluate or deliberate. Nonetheless, they aim to make the best decisions possible. In Decision Theory with a Human Face, Richard Bradley develops new theories of agency and rational decision-making, offering guidance on how 'real' agents who are aware of their bounds should represent the uncertainty they face, how they should revise their opinions as a result of experience and how they should make decisions when lacking full awareness of, or precise opinions on relevant contingencies. He engages with the strengths and flaws of Bayesian reasoning, and presents clear and comprehensive explorations of key issues in decision theory, from belief and desire to semantics and learning. His book draws on philosophy, economics, decision science and psychology, and will appeal to readers in all of these disciplines.

The role of designers has traditionally been to design a building so that it conforms to accepted local building codes. The safety of workers is left up to the contractor building the designs. Research shows, however, that designers can have an especially strong influence on construction safety during the concept, preliminary and detailed design phases. This book establishes the new knowledge and conceptual frameworks necessary to develop a mobile computing-enabled knowledge management system that can help reduce the high rate of construction falls. There are three main objectives of this book: 1. To create a new Prevention through Design (PtD) knowledge base to model the relationships between fall risks and design decisions; 2. To develop a PtD mobile App to assist building designers in fall prevention through design; 3. To evaluate the practical implications of the PtD mobile App for the construction industry, especially for building designers and workers. The cutting edge technologies explored in this book have the potential to significantly reduce the rate of serious injuries that occur in the global construction industry. This is essential reading for researchers and advanced students of construction management with an interest in safety or mobile technologies.

Today's instantaneous and ever-present news stream frequently presents a sensationalized or otherwise distorted view of the world, demanding constant critical engagement on the part of everyday citizens. The Critical Thinker's Guide to Bias, Lies, and Politics in the News reveals the power of critical thinking to make sense of overwhelming and often subjective media by detecting ideology, slant, and spin at work. Building off the Richard Paul and Linda Elder framework for critical thinking, Elder focuses on the internal logic of the news as well as societal influences on the media while illustrating essential elements of trustworthy journalism. With up-to-date discussions of social media, digital journalism, and political maneuvering inside and outside the fourth estate, Fact or Fake is an essential handbook for those who want to stay informed but not influenced by our modern news reporting systems.