Banks / Aliaga / Rios Insua Adversarial Risk Analysis

Erscheinungsjahr 2015
ISBN: 978-1-4987-1240-8
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 224 Seiten

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.

Autoren/Hrsg.

Banks, David L.

Aliaga, Jesus M. Rios

Rios Insua, David

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

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

Über Autor(innen)

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.