Neuware - This 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. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations. A case study based on a real application in railway security provides a blueprint for how to perform ARA in similar security situations. In addition, multi-agent influence diagrams help readers visualize complex information structures.

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Detalles bibliogr�ficos

T�tulo

Adversarial Risk Analysis

Editor

CRC Press Jun 2015

Fecha de publicaci�n

2015

Idioma

Ingl�s

ISBN 10

1498712398

ISBN 13

9781498712392

Encuadernaci�n

Buch

Estado

Neu

Cat�logos de vendedores

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Sinopsis

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.

Focuses on the recent subfield of decision analysis, ARA

Compares ideas from decision theory and game theory

Uses multi-agent influence diagrams (MAIDs) throughout to help readers visualize complex information structures

Applies the ARA approach to simultaneous games, auctions, sequential games, and defend-attack games

Contains an extended case study based on a real application in railway security, which provides a blueprint for how to perform ARA in similar security situations

Includes exercises at the end of most chapters, with selected solutions at the back of the book

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.

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David L. Banks is a professor in the Department of Statistical Science at Duke University. His research interests include data mining and risk analysis.

Jesus Rios is a researcher in risk and decision analytics for the Cognitive Computing Department at the IBM Research Division. His research focuses on applying risk and decision analysis to solve complex business problems.

David R�os Insua is the AXA-ICMAT Chair in Adversarial Risk Analysis at the Institute of Mathematical Sciences ICMAT-CSIC and a member of the Spanish Royal Academy of Sciences. His research interests include risk analysis, decision analysis, Bayesian statistics, security, aviation safety, and social robotics.

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