Adversarial risk analysis
- David L. Banks, Duke University, Durham, North Carolina, USA, Jesus Rios, IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA, David Rios Insua, Institute of Mathemathical Sciences, ICMAT-CSIC, Madrid, Spain.
- Boca Raton : Taylor & Francis, 2015.
- Copyright notice
- Physical description
- x, 214 pages : illustrations ; 24 cm
At the library
Science Library (Li and Ma)
|QA269 .B25 2015||Unknown|
- Includes bibliographical references and index.
- 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.
- (source: Nielsen Book Data)
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.
(source: Nielsen Book Data)
- Publication date
- "A CRC title."
- 9781498712392 (hardcover : alk. paper)
- 1498712398 (hardcover : alk. paper)
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