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E-raamat: Adversarial Risk Analysis

(Business Analytics and Mathematical Sciences, IBM, New York, USA), (Duke Univ), (Institute of Mathematical Sciences ICMAT-CSIC, Spain)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 30-Jun-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040073865
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 30-Jun-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040073865

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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 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.

Arvustused

"This well-written and concise text is an introduction to the field of adversarial risk analysis (ARA), which is a form of decision and risk analysis which incorporates uncertainty and game theory to model strategies of an adversaryThere is an appropriate amount of detail throughout the book, making it suitable for a reference text as well as a book which may be read cover to cover and it is both thought provoking and enlightening." Matthew Craven, Plymouth University, Journal of the Royal Statistical Society, Series A, January 2017

"Here, Banks (Duke Univ.), Rios (IBM), and Insua (ICMAT-CSIC, Spain) identify three categories of uncertainty for the strategist: aleatory uncertaintynondeterminism of outcomes even after players make choices; epistemic uncertaintyhidden information concerning opponents' preferences, beliefs, and capabilities; and concept uncertaintyhidden 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

Preface ix
1 Games and Decisions
1(30)
1.1 Game Theory: A Review
2(8)
1.2 Decision Analysis: An Introduction
10(9)
1.3 Influence Diagrams
19(12)
Exercises
27(4)
2 Simultaneous Games
31(36)
2.1 Discrete Simultaneous Games: The Basics
32(4)
2.2 Modeling Opponents
36(20)
2.2.1 Non-Strategic Analysis
36(3)
2.2.2 Nash Equilibrium
39(2)
2.2.3 Level-k Thinking
41(5)
2.2.4 Mirror Equilibria
46(10)
2.3 Comparison of ARA Models
56(11)
Exercises
63(4)
3 Auctions
67(22)
3.1 Non-Strategic Play
68(3)
3.2 Minimax Perspectives
71(2)
3.3 Bayes Nash Equilibrium
73(4)
3.4 Level-k Thinking
77(4)
3.5 Mirror Equilibria
81(1)
3.6 Three Bidders
81(8)
3.6.1 Level-A: Thinking
82(3)
3.6.2 Mirror Equilibrium
85(1)
Exercises
86(3)
4 Sequential Games
89(34)
4.1 Sequential Games: The Basics
89(5)
4.2 ARA for Sequential Games
94(3)
4.3 Case Study: Somali Pirates
97(10)
4.4 Case Study: La Relance
107(16)
4.4.1 Continuous Bets
113(4)
4.4.2 Generalizations of La Relance
117(5)
Exercises
122(1)
5 Variations on Sequential Defend-Attack Games
123(32)
5.1 The Sequential Defend-Attack Model
123(5)
5.2 Multiple Attackers
128(5)
5.3 Multiple Defenders
133(4)
5.4 Multiple Targets
137(2)
5.5 Defend-Attack-Defend Games
139(11)
5.6 Learning
150(5)
6 A Security Case Study
155(22)
6.1 Casual Fare Evaders
157(4)
6.2 Collusion
161(4)
6.3 Pickpockets
165(5)
6.4 Evaders and Pickpockets
170(2)
6.5 Multiple Stations
172(4)
6.6 Terrorism
176(1)
7 Other Issues
177(8)
7.1 Complex Systems
177(4)
7.2 Applications
181(4)
Solutions to Selected Exercises 185(14)
References 199(10)
Index 209
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 Ríos 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.