For decades, game theory and other group decision-making paradigms have been considered of little use in practical risk management problems. However, this viewpoint has recently become less dogmatic because: a) High-profile terrorist attacks have demanded significant national investment in protective responses, and there is public concern that not all of these investments are prudent and/or effective; b) Key business sectors have become more mathematically sophisticated, and now use this expertise to shape corporate strategy for auction bidding, lobbying efforts, and other decisions; c) Regulatory legislation must balance competing interests (growth, environmental impact, safety) in a way that is credible and transparent; and d) The on-going arms race in cyber-security means that the financial penalties for myopic protection are large and random. Solution strategies for such diverse applications must employ tools from many fields (statistics, economics, operations research, sociology, psychology, political science, etc.).

All of these problems are characterized by the fact that there are two or more intelligent opponents who make decisions for which the outcome is uncertain. Collectively, we refer to this problem area as Adversarial Risk Analysis (ARA). Traditional statistical risk analysis grew in the context of nuclear reactor safety, insurance, and other applications in which loss was governed by chance, rather than by the malicious (or self-interested) actions of intelligent actors. But in ARA, one needs to have some model for the decision-making of all participants. This model might be classically game-theoretic, with (noncooperative) Nash equilibria as the core concept, or it might be psychological, reflecting either a decision
analytic formulation or empirical studies of strategic behavior.

In counterterrorism, appropriate security measures represent one of the key challenges for states in this century. After recent large-scale terrorist attacks, multi-billion euro investments are being made to increase public safety. This has stirred debate about the cost-effectiveness of such measures. In turn, this has prompted research on modeling issues in counterterrorism, drawing upon tools from reliability analysis, data mining, and complex dynamic systems, among many others.

Adversarial Risk Analysis (ARA) is an attempt to combine strategic reasoning about opponents and probabilistic treatment of aleatory outcomes. It is an emerging perspective that has attractive features in counterterrorism applications.