Robust allocation of a defensive budget considering an attacker’s private information

Attackers ’ private information is one of the main issues in defensive resource allocation games in homeland security. The outcome of a defense resource allocation decision criti-cally depends on the accuracy of estimations about the attacker’s attributes. However, terror-ists ’ goals may be unknown to the defender, necessitating robust decisions by the defender. This article develops a robust-optimization game-theoretical model for identifying optimal defense resource allocation strategies for a rational defender facing a strategic attacker while the attacker’s valuation of targets, being the most critical attribute of the attacker, is un-known but belongs to bounded distribution-free intervals. To our best knowledge, no previ-ous research has applied robust optimization in homeland security resource allocation when uncertainty is defined in bounded distribution-free intervals. The key features of our model include (1) modeling uncertainty in attackers ’ attributes, where uncertainty is characterized by bounded intervals; (2) finding the robust-optimization equilibrium for the defender using concepts dealing with budget of uncertainty and price of robustness; and (3) applying the proposed model to real data. KEY WORDS: Defender-attacker game; defense resource allocation; private information; robust optimizatio