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Risk Bandits: Rescuing Risk Management from Tokenism provides directors and executives with a unique yet highly warranted insight into poorly understood organisational risk management practices. As respected business practitioners with extensive experience in meaningful risk management, authors Rob Hogarth and Tony Pooley, have teamed up to turn a clear and unblinking eye upon typical, contemporary organisational risk management and present a take-no-prisoners critique of its often shaky processes. This book offers directors and executives a must-read critique of typical organisational risk management and proposes an alternative for grounding organisational risk management practices on a solid foundation that protects and creates value. It is not often that I read a book on risk and find myself saying here, here as I turn the pagesJean Cross, Emeritus Prof. in Risk, University of NSW I think this is an excellent book and industry is long overdue for the truth, I cant wait to get my risk managers reading it. Shayne Arthur, General Manager Risk at Orica This is a ripping yarn, I was keen to provide feedback before boarding in case I was the victim of a low probability event over the Atlantic.Norman W Ritchie, vPSI Director It is an easy read, written in a journalistic style and certainly comprehensively and competently covering the topic Barry J Cooper, Prof. and Associate Dean at Deakin University Business School
Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools—Bayesian and frequentist—of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.
One Armed Bandit is an action drama set in the future, in a world divided, where all traces of history have been lost. Where superhuman abilities are created by the wealthy, fueled by their greed, their lust for power and their thirst for everlasting life. One man, Jayden Wright, fights to restore balance to the world, whilst others fight to destroy it. In the Genesis Arc, Jayden was one of many Arclights, training to become an Elite. Until all that was destroyed. Torn between justice or revenge, he is destined to face many challenges along his journey. But before Jayden can bring balance to the world, he will have to conquer the demons within - or be taken over by them. See which path Jayden will follow in One Armed Bandit. In Chapter 6, The Risk of a Lifetime, amidst the raging battle, Asic reflects back on her and Jayden's shared past, where one key event changed everything and altered the path of the rest of their lives.
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
The National Science Foundation, The National Institute of Occupational Safety and Health, and the Center for Technology and Humanities at Georgia State University sponsored a two-day national conference on Moral Issues and Public Policy Issues in the Use of the Method of Quantitative Risk Assessment ( QRA) on September 26 and 27, 1985, in Atlanta, Georgia. The purpose of the conference was to promote discussion among practicing risk assessors, senior government health officials extensively involved in the practice of QRA, and moral philosophers familiar with the method. The conference was motivated by the disturbing fact that distinguished scientists ostensibly employing the same method of quantitative risk assessment to the same substances conclude to widely varying and mutually exclusive assessments of safety, depending on which of the various assumptions they employ when using the method. In short, the conference was motivated by widespread concern over the fact that QRA often yields results that are quite controversial and frequently contested by some who, in professedly using the same method, manage to arrive at significantly different estimates of risk.
Tired of lying in wait, the exiled Noble Bandit seizes his chance at revenge. Meanwhile, as the birth of the Royal Twins nears, Peasant General Guarding Bear is repatriated by the Emperor. Fearing for the safety of his heirs, the emperor orders the general to lay siege to his enemy’s fortress. In preparation, the general recruits the aid of a powerful wizard and a skilled young healer – but none of them suspects a traitor in their midst. ​​​​​​​As loyalties are tested and new alliances made, who will rise above and claim victory as their own?
A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1991.
Cause; a murder in Los Angeles. Effect; A summons from the Grand Regent. Jake Striker felt he needed to return to Mongolia to tell his in-laws how their daughter died. His wife, Chandaa, was killed by a drunk driver who ran a red light. The 911 Porsche was broadsided and his wife died instantly at an intersection in Beverly Hills. She had been with him when he finally located the mysterious Chang Jai Lamasery, high on Mt. Bayaskhulangtu. They had been searching for a child that was born at the exact instant the ancient Chang Lai Lama died. The Lamas believed the child was the reincarnation of their revered Grand Lama and were returning him to his rightful home. The parents of the child saw it as kidnapping. Chandaa’s parents live far out on the rolling steppes, where only personal communication is possible. Jake is tall, six feet, three inches, handsome with light brown hair and storm gray eyes. He had originally been hired by the parents to locate their child. Now he is back in Mongolia, comforting grieving in-laws. Chanda’s sister, Mei, a stunningly beautiful Mongolian woman, with Chinese ancestors, is with Jake in her parent’s ger when Lama Namsray arrives and tells Jake that the Grand Regent is in need of his services. Contacting the Grand Regent will require an arduous trek by horseback up into the sacred mountains, where only a privileged few are permitted. A steppe soldier is traveling with Lama Namsray, to protect him; armed with an AK47. Jake is a policeman turned lawyer and has the ear of the Grand Regent. One of the young men who was born in the mysterious valley has gone rogue and become a drug dealer and murderer in Southern California. He needs to be stopped and the Grand Regent is about to give Jake the assignment. Mei, who is twenty-five, with almond eyes and raven hair that hangs to her waist, informs Jake and Lama Namsray that she is going with them. The Lama tells her that will be impossible, outsiders are not allowed into the secret Lamasery. She informs him, “I’m going.” Lama Namsray, the second most powerful lama in the sacred dzong, explains that the Grand Regent would never permit it. Mei says, “I know about the gold mine, I know about the child Grand Lama and I know about the secret entrance into the valley. I’m going.” Jake sides with Mei and she accompanies him on the dangerous journey into the mountains. She also accompanies him to Southern California where they do battle with the drug lord and his thugs. Events become so dangerous they have to call upon the ancient Order of the Tu Tung. A mysterious Lamasery, a handsome lawyer, a beautiful Mongolian woman and a clandestine order of assassins, wrapped tightly together with dragon-emblazoned fabric from the Great Silk Road.
In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning from less feedback. The multiarmed bandit field is currently experiencing a renaissance, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This book aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.