This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support all major theoretical predictions. In the auction treatment, where winning a...

This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support all major theoretical predictions. In the auction treatment, where winning a...

This paper experimentally examines behavior in a two-player game of attack and defense of a weakest-link network of targets, in which the attacker's objective is to successfully attack at least one target and the defender's objective is diametrically opposed. We apply two benchmark contest...

This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support all major theoretical predictions. In the auction treatment, where winning a...

This paper experimentally examines behavior in a two-player game of attack and defense of a weakest-link network of targets, in which the attacker’s objective is to successfully attack at least one target and the defender’s objective is diametrically opposed .We apply two benchmark contest...

This paper experimentally examines behavior in a two-player game of attack and defense of a weakest-link network of targets, in which the attacker's objective is to successfully attack at least one target and the defender's objective is diametrically opposed. We apply two benchmark contest...

This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support the main qualitative predictions of the theory. In the auction treatment, where winning...