Peter C. Young

This casebook extends Strategic Risk Leadership: Engaging a World of Risk, Uncertainty and the Unknown, bringing theory and practice grounded in the first book to life with an array of applicable, real-world examples. The book enables critical thinking about the current state of risk management and ERM, demonstrating contemporary shortcomings and challenges from real-life cases drawn from a global selection of well-known organizations. It confronts modern risk management practices and discusses what leaders should do to deal with unpredictable environments. Providing a basis for developing more effective risk management approaches, the book identifies shortcomings of contemporary approaches to risk management and specifies how to deal with the major risks we face today, illuminated by a variety of comprehensive global examples. It also provides valuable insights on these approaches for managers and leaders in general—including risk executives and chief risk officers—as well as advanced risk management students. End-of-chapter cases illustrate both good and bad risk management approaches as useful inspiration for reflective risk leaders.This book will be a hugely valuable resource for those studying or teaching risk management.

This casebook extends Strategic Risk Leadership: Engaging a World of Risk, Uncertainty and the Unknown, bringing theory and practice grounded in the first book to life with an array of applicable, real-world examples. The book enables critical thinking about the current state of risk management and ERM, demonstrating contemporary shortcomings and challenges from real-life cases drawn from a global selection of well-known organizations. It confronts modern risk management practices and discusses what leaders should do to deal with unpredictable environments. Providing a basis for developing more effective risk management approaches, the book identifies shortcomings of contemporary approaches to risk management and specifies how to deal with the major risks we face today, illuminated by a variety of comprehensive global examples. It also provides valuable insights on these approaches for managers and leaders in general—including risk executives and chief risk officers—as well as advanced risk management students. End-of-chapter cases illustrate both good and bad risk management approaches as useful inspiration for reflective risk leaders.This book will be a hugely valuable resource for those studying or teaching risk management.

Modern risk management as practiced today faces significant obstacles—we argue—primarily due to the fundamental premise of the concept itself. It asserts that we are mainly dealing with measurable, quantifiable risks and that we can manage the uncontrollable by relying on formal control-based systems, which has produced a general view that (enterprise) risk management is a technical-scientific discipline. Strategic Risk Leadership offers a critique of the status quo, and encourages leaders, executives, and chief risk officers to find fresh approaches that can help them deal more proactively with what the future may hold. The book provides an overview of the history of risk management and current risk governance approaches as prescribed by leading risk management standards, such as COSO and ISO31000. This enables practitioners to challenge the frameworks and improve their adoption in practice introducing sustainable resilience as a (more) meaningful response to uncertain and unknowable conditions. The book shows how traditional thinking downplays the significance of human behavior and judgmental biases as key elements of major organizational exposures illustrated and explained through numerous case examples and studies. This book is essential reading for strategic risk managers to understand the requirements for effective risk governance practices in the contemporary and rapidly changing global risk landscape. Indeed, it is a valuable resource for all risk executives, leaders, and chief risk officers, as well as advanced students of risk management.

This is a revised version of the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century. Also over this time, the CAPTAIN Toolbox for recursive estimation and time series analysis has been developed at Lancaster, for use in the MatlabTM software environment (see Appendix G). Consequently, the present version of the book is able to exploit the many computational routines that are contained in this widely available Toolbox, as well as some of the other routines in MatlabTM and its other toolboxes.The book is an introductory one on the topic of recursive estimation and it demonstrates how this approach to estimation, in its various forms, can be an impressive aid to the modelling of stochastic, dynamic systems. It is intended for undergraduate or Masters students who wish to obtain a grounding in this subject; or for practitioners in industry who may have heard of topics dealt with in this book and, while they want to know more about them, may have been deterred by the rather esoteric nature of some books in this challenging area of study.

True Digital Control: Statistical Modelling and Non–Minimal State Space Designdevelops a true digital control design philosophy that encompasses data–based model identification, through to control algorithm design, robustness evaluation and implementation. With a heritage from both classical and modern control system synthesis, this book is supported by detailed practical examples based on the authors’ research into environmental, mechatronic and robotic systems. Treatment of both statistical modelling and control design under one cover is unusual and highlights the important connections between these disciplines. Starting from the ubiquitous proportional–integral controller, and with essential concepts such as pole assignment introduced using straightforward algebra and block diagrams, this book addresses the needs of those students, researchers and engineers, who would like to advance their knowledge of control theory and practice into the state space domain; and academics who are interested to learn more about non–minimal state variable feedback control systems. Such non–minimal state feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner. Key features: Covers both system identification and control system design in a unified mannerIncludes practical design case studies and simulation examplesConsiders recent research into time–variable and state–dependent parameter modelling and control, essential elements of adaptive and nonlinear control system design, and the delta–operator (the discrete–time equivalent of the differential operator) systemsAccompanied by a website hosting MATLAB examples True Digital Control: Statistical Modelling and Non–Minimal State Space Design is a comprehensive and practical guide for students and professionals who wish to further their knowledge in the areas of modern control and system identification.

This is a revised version of the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century. Also over this time, the CAPTAIN Toolbox for recursive estimation and time series analysis has been developed at Lancaster, for use in the MatlabTM software environment (see Appendix G). Consequently, the present version of the book is able to exploit the many computational routines that are contained in this widely available Toolbox, as well as some of the other routines in MatlabTM and its other toolboxes.The book is an introductory one on the topic of recursive estimation and it demonstrates how this approach to estimation, in its various forms, can be an impressive aid to the modelling of stochastic, dynamic systems. It is intended for undergraduate or Masters students who wish to obtain a grounding in this subject; or for practitioners in industry who may have heard of topics dealt with in this book and, while they want to know more about them, may have been deterred by the rather esoteric nature of some books in this challenging area of study.