Abstract
Applications in counterterrorism and corporate competition have led to the development of new methods for the analysis of decision making when there are intelligent opponents and uncertain outcomes. This field represents a combination of statistical risk analysis and game theory, and is sometimes called adversarial risk analysis. In this article, we describe several formulations of adversarial risk problems, and provide a framework that extends traditional risk analysis tools, such as influence diagrams and probabilistic reasoning, to adversarial problems. We also discuss the research challenges that arise when dealing with these models, illustrate the ideas with examples from business, and point out relevance to national defense.
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