Adversarial risk analysis under partial information

Abstract

Adversarial risk analysis provides one-sided decision support to decision makers faced with risks due to the actions of other parties who act in their own interest. It is therefore relevant for the management of security risks, because the likely actions of the adversary can, to some extent, be forecast by formulating and solving decision models which explicitly capture the adversary's objectives, actions, and beliefs. Yet, while the development of these decision models sets adversarial risk analysis apart from other approaches, the exact specification of the adversary's decision model can pose challenges. In response to this recognition, and with the aim of facilitating the use of adversarial risk analysis when the parameters of the decision model are not completely known, we develop methods for characterizing the adversary's likely actions based on concepts of partial information, stochastic dominance and decision rules. Furthermore, we consider situations in which information about the beliefs and preferences of all parties may be incomplete. We illustrate our contributions with a realistic case study of military planning in which the Defender seeks to protect a supply company from the Attacker who uses unmanned aerial vehicles for surveillance and the acquisition of artillery targets.