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Productivity Press
07 July 2015
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.
By:   David L. Banks (Duke University USA), Jesus M. Rios Aliaga (Business Analytics and Mathematical Sciences, IBM, New York, USA), David Rios Insua (Institute of Mathematical Sciences ICMAT-CSIC, Spain)
Imprint:   Productivity Press
Country of Publication:   United States
Dimensions:   Height: 234mm,  Width: 156mm,  Spine: 18mm
Weight:   454g
ISBN:   9781498712392
ISBN 10:   1498712398
Pages:   224
Publication Date:   07 July 2015
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
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

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 Rios 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.

Reviews for Adversarial Risk Analysis

Sun Tzu's The Art of War exhorts the aspiring warrior to 'know your enemy,' but the mathematics of conflict (whether von Neumann-Morgenstern game theory or Bayesian decision analysis) takes very little account of the complexion of the adversary. Indeed, when Morgenstern, reading Conan Doyle, saw paradox from the infinite regress implied by the infinitely rational agents Holmes and Moriarty attempting to outthink each other, von Neumann introduced randomization precisely to excise from such analysis any need to model such outthinking. Both game theory and decision analysis offer 'optimal' strategies based on the situation alone, the two methods diverging as they optimize for different quantities. But in real-world situations in which one faces less-than-perfectly-rational adversaries, outthinking can confer an advantage. Here, Banks (Duke Univ.), Rios (IBM), and Insua (ICMAT-CSIC, Spain) identify three categories of uncertainty for the strategist: aleatory uncertainty-nondeterminism of outcomes even after players make choices; epistemic uncertainty-hidden information concerning opponents' preferences, beliefs, and capabilities; and concept uncertainty-hidden information concerning opponents' strategies. Adversarial risk analysis, a new field with roots in modern efforts to defeat terrorism, provides a framework, in principle, to cope with these uncertainties. Solving the models seems generally intractable, but the heart of the book, the first of its kind, offers exemplary case studies. Summing up: Recommended. Lower-division undergraduates and above; informed general audiences. -D. V. Feldman, University of New Hampshire, Durham, USA, for CHOICE, March 2016


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