|aAdversarial risk analysis /|cDavid L. Banks (Duke University, Durham, North Carolina, USA), Jesus Rios (IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA) David Ríos Insua (Institute of Mathematical Sciences, ICMAT-CSIC, Madrid, Spain).
|aBoca Raton :|bCRC Press, Taylor & Francis Group,|cc2016.
|aBoca Raton :|bCRC Press, Taylor & Francis Group,|c
|ax, 214 pages :|billustrations ;|c24 cm
|a"A CRC title."
|aIncludes bibliographical references (pages 199-207) and index.
|a1. 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.
內容簡介top Adversarial Risk Analysis 簡介 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 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.