Simulating attacker and defender strategies within a dynamic game on network topology

Abstract

Successful military counterinsurgency operations increasingly rely upon an advanced understanding of relevant networks and their topologies. This paper evaluates, via simulations, various attacker and defender strategies within a dynamic game on network topology. The simulation is designed to provide insight into the effectiveness of offensive targeting strategies as determined by various centrality measures, given limited states of information and varying network topologies. Improved modeling of complex social behaviors is accomplished through incorporation of a distance-based utility function. Moreover, insights into effective defensive strategies are gained through incorporation of a hybrid model of network regeneration. Two designed experiments investigate the impact of game features on the superlative offensive and defensive strategies. Results indicate that degree centrality, proximal target centrality, and closeness centrality outperform other measures as targeting criteria given varying network topologies and defensive regeneration methods. Furthermore, the attacker state of information is only significant given a topology conducive to defense. The costs of direct relationships significantly impact effective regeneration methods, whereas restructuring methods are insignificant. These results offer preliminary insight into practical attack and defense strategies utilizing a simulation that can be easily adapted for operational applications.

Keywords:

• Military
• simulation
• dynamic game
• social networks

Acknowledgements

The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Army, the United States Air Force, the Department of Defense, or the United States Government. The authors wish to thank the associate editor and two anonymous referees for their helpful comments and suggestions.

Notes

Please note this paper has been re-typeset by Taylor & Francis from the manuscript originally provided to the previous publisher.