Allocating multiple defensive resources in a zero-sum game setting

This paper investigates the problem of allocating multiple defensive resources to protect multiple sites against possible attacks by an adversary. The effectiveness of the resources in reducing potential damage to the sites is assumed to vary across the resources and across the sites and their availability is constrained. The problem is formulated as a two-person zero-sum game with piecewise linear utility functions and polyhedral action sets. Linearization of the utility functions is applied in order to reduce the computation of the game’s Nash equilibria to the solution of a pair of linear programs (LPs). The reduction facilitates revelation of structure of Nash equilibrium allocations, in particular, of monotonicity properties of these allocations with respect to the amounts of available resources. Finally, allocation problems in non-competitive settings are examined (i.e., situations where the attacker chooses its targets independently of actions taken by the defender) and the structure of solutions in such settings is compared to that of Nash equilibria.

[1]  A Charnes,et al.  Constrained Games and Linear Programming. , 1953, Proceedings of the National Academy of Sciences of the United States of America.

[2]  D. W. Blackett,et al.  Some blotto games , 1954 .

[3]  A. Tucker,et al.  Linear Inequalities And Related Systems , 1956 .

[4]  P. Wolfe 9 . Determinateness of Polyhedral Games , 1957 .

[5]  G. Dantzig Discrete-Variable Extremum Problems , 1957 .

[6]  D. H. Martin On the continuity of the maximum in parametric linear programming , 1975 .

[7]  H. Luss Minimax resource allocation problems: Optimization and parametric analysis , 1992 .

[8]  S. Newell,et al.  Winner Takes all , 1996 .

[9]  Naoki Katoh,et al.  Resource Allocation Problems , 1998 .

[10]  P. Pardalos,et al.  Handbook of Combinatorial Optimization , 1998 .

[11]  Hanan Luss,et al.  On Equitable Resource Allocation Problems: A Lexicographic Minimax Approach , 1999, Oper. Res..

[12]  Christos H. Papadimitriou,et al.  Algorithms, games, and the internet , 2001, STOC '01.

[13]  L. Berkovitz Convexity and Optimization in Rn , 2001 .

[14]  Christos H. Papadimitriou,et al.  Algorithms, Games, and the Internet (Extended Abstract) , 2001 .

[15]  David P. Morton,et al.  George B Dantzig, 1914–2005 , 2005, J. Oper. Res. Soc..

[16]  Richard W. Cottle,et al.  George B. Dantzig: Operations Research Icon , 2005, Oper. Res..

[17]  B. Roberson The Colonel Blotto game , 2006 .

[18]  R. Powell Defending against Terrorist Attacks with Limited Resources , 2007, American Political Science Review.

[19]  Rae Zimmerman,et al.  Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  Gregory Levitin,et al.  Intelligence and impact contests in systems with redundancy, false targets, and partial protection , 2009, Reliab. Eng. Syst. Saf..

[21]  Uriel G. Rothblum,et al.  Nature plays with dice - terrorists do not: Allocating resources to counter strategic versus probabilistic risks , 2009, Eur. J. Oper. Res..

[22]  Hanan Luss Equitable Resource Allocation: Models, Algorithms and Applications , 2012 .

[23]  Uriel G. Rothblum,et al.  A Stochastic Competitive R&D Race Where "Winner Takes All" , 2012, Oper. Res..

[24]  Å. Lindahl Convexity and Optimization , 2015 .