Adversarial risk analysis

Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA) A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations. The book shows decision makers how to build Bayesian models for the strategic calculation of their opponents, enabling decision makers to maximize their expected utility or minimize their expected loss. This new approach to risk analysis asserts that analysts should use Bayesian thinking to describe their beliefs about an opponents goals, resources, optimism, and type of strategic calculation, such as minimax and level-k thinking. Within that framework, analysts then solve the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent and enables analysts to maximize their expected utilities. Descripción del editor: "A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations.Focuses on the recent subfield of decision analysis, ARACompares ideas from decision theory and game theoryUses multi-agent influence diagrams (MAIDs) throughout to help readers visualize complex information structuresApplies the ARA approach to simultaneous games, auctions, sequential games, and defend-attack gamesContains an extended case study based on a real application in railway security, which provides a blueprint for how to perform ARA in similar security situationsIncludes exercises at the end of most chapters, with selected solutions at the back of the bookThe book shows decision makers how to build Bayesian models for the strategic calculation of their opponents, enabling decision makers to maximize their expected utility or minimize their expected loss. This new approach to risk analysis asserts that analysts should use Bayesian thinking to describe their beliefs about an opponent's goals, resources, optimism, and type of strategic calculation, such as minimax and level-k thinking. Within that framework, analysts then solve the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent and enables analysts to maximize their expected utilities." (CRC Press) Flexible Models to Analyze Opponent Behavior A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations. The book shows decision makers how to build Bayesian models for the strategic calculation of their opponents, enabling decision makers to maximize their expected utility or minimize their expected loss. This new approach to risk analysis asserts that analysts should use Bayesian thinking to describe their beliefs about an opponent's goals, resources, optimism, and type of strategic calculation, such as minimax and level-k thinking. Within that framework, analysts then solve the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent and enables analysts to maximize their expected utilities Content: Games and Decisions Game Theory: A Review Decision Analysis: An Introduction Influence Diagrams Problems Simultaneous Games Discrete Simultaneous Games: The Basics Modeling Opponents Comparison of ARA Models Problems Auctions Non-Strategic Play Minimax Perspectives Bayes Nash Equilibrium Level-k Thinking Mirror Equilibria Three Bidders Problems Sequential Games Sequential Games: The Basics ARA for Sequential Games Case Study: Somali Pirates Case Study: La Relance Problems Variations on Sequential Defend-Attack Games The Sequential Defend-Attack Model Multiple Attackers Multiple Defenders Multiple Targets Defend-Attack-Defend Games Learning A Security Case Study Casual Fare Evaders Collusion Pickpockets Evaders and Pickpockets Multiple Stations Terrorism Other Issues Complex Systems Applications Solutions to Selected Exercises References Index Front Cover; Contents; Preface; 1. Games and Decisions; 2. Simultaneous Games; 3. Auctions; 4. Sequential Games; 5. Variations on Sequential Defend-Attack Games; 6. A Security Case Study; 7. Other Issues; Solutions to Selected Exercises; References