Relevant bibliographies by topics / Colonel Blotto Game

Academic literature on the topic 'Colonel Blotto Game'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Colonel Blotto Game"

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Roberson, Brian. "The Colonel Blotto game." Economic Theory 29, no. 1 (January 18, 2006): 1–24. http://dx.doi.org/10.1007/s00199-005-0071-5.

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Boix-Adserà, Enric, Benjamin L. Edelman, and Siddhartha Jayanti. "The multiplayer Colonel Blotto game." Games and Economic Behavior 129 (September 2021): 15–31. http://dx.doi.org/10.1016/j.geb.2021.05.002.

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Rinott, Yosef, Marco Scarsini, and Yaming Yu. "A Colonel Blotto Gladiator Game." Mathematics of Operations Research 37, no. 4 (November 2012): 574–90. http://dx.doi.org/10.1287/moor.1120.0550.

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Homburg, Stefan. "Colonel Blotto und seine ökonomischen Anwendungen." Perspektiven der Wirtschaftspolitik 12, no. 1 (February 2011): 1–11. http://dx.doi.org/10.1111/j.1468-2516.2010.00347.x.

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AbstractRedistributional issues are important in contemporary welfare states. These issues cannot be analyzed using the median voter theorem because preferences fail singlepeakedness: Collective preferences are intransitive, giving rise to cyclical preferences. A suitable instrument for analyzing redistributional issues is the Colonel Blotto game. This game is older than the more familiar prisoner’s dilemma, but it has been solved only recently. The article introduces the Colonel Blotto Game as well as the general structure of its solutions. Thereafter, the game’s logic is illustrated using several policy examples. The two most fascinating results state that, in a political contest, it is never optimal to use pure strategies, and that the political process itself induces remarkable inequalities.
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Roberson, Brian, and Dmitriy Kvasov. "The non-constant-sum Colonel Blotto game." Economic Theory 51, no. 2 (October 28, 2011): 397–433. http://dx.doi.org/10.1007/s00199-011-0673-z.

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Dehghani, Sina, Hamed Saleh, Saeed Seddighin, and Shang-Hua Teng. "Computational Analyses of the Electoral College: Campaigning Is Hard But Approximately Manageable." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5294–302. http://dx.doi.org/10.1609/aaai.v35i6.16668.

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In the classical discrete Colonel Blotto game—introduced by Borel in 1921—two colonels simultaneously distribute their troops across multiple battlefields. The winner of each battlefield is determined by a winner-take-all rule, independently of other battlefields. In the original formulation, each colonel’s goal is to win as many battlefields as possible. The Blotto game and its extensions have been used in a wide range of applications from political campaign—exemplified by the U.S presidential election—to marketing campaign, from (innovative) technology competition to sports competition. Despite persistent efforts, efficient methods for finding the optimal strategies in Blotto games have been elusive for almost a century—due to exponential explosion in the organic solution space—until Ahmadinejad, Dehghani, Hajiaghayi, Lucier, Mahini, and Seddighin developed the first polynomial-time algorithm for this fundamental gametheoretical problem in 2016. However, that breakthrough polynomial-time solution has some structural limitation. It applies only to the case where troops are homogeneous with respect to battlegruounds, as in Borel’s original formulation: For each battleground, the only factor that matters to the winner’s payoff is how many troops as opposed to which sets of troops are opposing one another in that battleground. In this paper, we consider a more general setting of the two-player-multi-battleground game, in which multifaceted resources (troops) may have different contributions to different battlegrounds. In the case of U.S presidential campaign, for example, one may interpret this as different types of resources—human, financial, political—that teams can invest in each state. We provide a complexity-theoretical evidence that, in contrast to Borel’s homogeneous setting, finding optimal strategies in multifaceted Colonel Blotto games is intractable. We complement this complexity result with a polynomial-time algorithm that finds approximately optimal strategies with provable guarantees. We also study a further generalization when two competitors do not have zerosum/ constant-sum payoffs. We show that optimal strategies in these two-player-multi-battleground games are as hard to compute and approximate as Nash equilibria in general noncooperative games and economic equilibria in exchange markets.
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Kharlamov, V. V. "On Asymptotic Strategies in the Stochastic Colonel Blotto Game." Theory of Probability & Its Applications 67, no. 2 (August 2022): 318–26. http://dx.doi.org/10.1137/s0040585x97t990952.

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HAUSKEN, KJELL. "ON THE IMPOSSIBILITY OF DETERRENCE IN SEQUENTIAL COLONEL BLOTTO GAMES." International Game Theory Review 14, no. 02 (June 2012): 1250011. http://dx.doi.org/10.1142/s0219198912500119.

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A sequential Colonel Blotto and rent seeking game with fixed and variable resources is analyzed. With fixed resources, which is the assumption in Colonel Blotto games, we show for the common ratio form contest success function that the second mover is never deterred. This stands in contrast to Powell's (Games and Economic Behavior67(2), 611–615) finding where the second mover can be deterred. With variable resources both players exert efforts in both sequential and simultaneous games, whereas fixed resources cause characteristics of all battlefields or rents to impact efforts for each battlefield. With variable resources only characteristics of a given battlefield impact efforts are to win that battlefield because of independence across battlefields. Fixed resources impact efforts and hence differences in unit effort costs are less important. In contrast, variable resources cause differences in unit effort costs to be important. The societal implication is that resource constrained opponents can be expected to engage in warfare, whereas an advantaged player with no resource constraints can prevent warfare.
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Hernández, Damián G., and Damián H. Zanette. "Evolutionary Dynamics of Resource Allocation in the Colonel Blotto Game." Journal of Statistical Physics 151, no. 3-4 (December 14, 2012): 623–36. http://dx.doi.org/10.1007/s10955-012-0659-7.

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Zhang, Long, Yao Wang, and Zhu Han. "Safeguarding UAV-Enabled Wireless Power Transfer Against Aerial Eavesdropper: A Colonel Blotto Game." IEEE Wireless Communications Letters 11, no. 3 (March 2022): 503–7. http://dx.doi.org/10.1109/lwc.2021.3133891.

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Dissertations / Theses on the topic "Colonel Blotto Game"

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Macdonell, Scott Taplin. "Strategic political environments : gerrymandering and campaign expenditures." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5442.

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My dissertation contains three chapters studying the strategic allocation of resources in political environments. Chapter 2 asks if redistricting is the result of partisan gerrymandering or apolitical considerations. I develop a statistical test for partisan gerrymandering and apply it to the U.S. Congressional districting plan chosen by the Republican legislature in Pennsylvania in 2001. First, I formally model the optimization problem faced by a strategic Republican redistricter and characterize the theoretically optimal solution. I then estimate the likelihood a district is represented by a Republican, conditional on district demographics. This estimate allows me to determine the value of the gerrymanderer's objective function under any districting plan. Next, I use a geographic representation of the state to randomly generate a sample of legally valid plans. Finally, I calculate the estimated value of a strategic Republican redistricter's objective function under each of the sample plans and under the actual plan chosen by Republicans. When controlling for incumbency the formal test shows that the Republicans' plan was a partisan gerrymander. In Chapter 3 I introduce a new and novel electoral reform that continues to allow redistricting but changes the incentives to do so. This reform ensures that parties earn seats proportional to their performance at the polls without substantially changing the electoral system in the U.S. In order to evaluate the reform's impacts, I model and solve a game that incorporates the redistricting decision, candidate choice, state legislative elections, and policy choice. Unsurprisingly, strategic redistricting biases policy in favor of the redistricting party. In the environments studied, the new reform never increases policy bias, and often reduces it. Political campaigns often require the strategic allocation of resources across multiple contests. In Chapter 4 I analyze these environments in terms of the canonical Colonel Blotto game, beginning with the most basic of Blotto games: Two officers simultaneously allocate their forces across two fields of battle. The larger force on each front wins that battle, and the payoff is the sum of the values of the battles won. I completely characterize the set of Nash equilibria to any such game and provide the unique equilibrium payoffs. This characterization comes from an intuitive graphical algorithm which I then apply to several generalizations of the game. I completely characterize the set of equilibria and provide the unique equilibrium payoffs to Blotto games with battlefield values that vary across players and games with general resource constraints. I also use my approach to solve the Blotto games on more than two battlefields with asymmetric battlefields and force endowments.
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Books on the topic "Colonel Blotto Game"

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Findings on Mosaic Warfare from a Colonel Blotto Game. RAND Corporation, 2021. http://dx.doi.org/10.7249/rr4397.

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Grana, Justin, Nicholas A. ODonoughue, Acquisition and Technology Policy Center Staff, Rand Corporation Staff, and Jonathan Lamb. Findings on Mosaic Warfare from a Colonel Blotto Game. RAND Corporation, The, 2021.

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Book chapters on the topic "Colonel Blotto Game"

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Dziubiński, Marcin. "The Spectrum of Equilibria for the Colonel Blotto and the Colonel Lotto Games." In Algorithmic Game Theory, 292–306. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66700-3_23.

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Heyman, Joseph L., and Abhishek Gupta. "Colonel Blotto Game with Coalition Formation for Sharing Resources." In Lecture Notes in Computer Science, 166–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01554-1_10.

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Gupta, Abhishek, Tamer Başar, and Galina A. Schwartz. "A Three-Stage Colonel Blotto Game: When to Provide More Information to an Adversary." In Lecture Notes in Computer Science, 216–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12601-2_12.

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Conference papers on the topic "Colonel Blotto Game"

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Ferdowsi, Aidin, Anibal Sanjab, Walid Saad, and Tamer Basar. "Generalized Colonel Blotto Game." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8431701.

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Boix-Adserà, Enric, Benjamin L. Edelman, and Siddhartha Jayanti. "The Multiplayer Colonel Blotto Game." In EC '20: The 21st ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3391403.3399555.

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Gaonkar, Akash, Divya Raghunathan, and S. Matthew Weinberg. "The Derby Game: An Ordering-based Colonel Blotto Game." In EC '22: The 23rd ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490486.3538367.

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Vu, Dong Quan, Patrick Loiseau, and Alonso Silva. "Efficient Computation of Approximate Equilibria in Discrete Colonel Blotto Games." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/72.

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The Colonel Blotto game is a famous game commonly used to model resource allocation problems in many domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the aggregate value of battlefields they win, each battlefield being won by the player who allocates more resources to it. The continuous version of the game---where players can choose any fractional allocation---has been extensively studied, albeit only with partial results to date. Recently, the discrete version---where allocations can only be integers---started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters. We also propose an efficient dynamic programming algorithm in order to compute for each game instance the actual value of the error. We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields
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Fuchs, Z. E., and P. P. Khargonekar. "A sequential Colonel Blotto game with a sensor network." In 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6315589.

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Shahrivar, Ebrahim Moradi, and Shreyas Sundaram. "Multi-layer network formation via a Colonel Blotto game." In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2014. http://dx.doi.org/10.1109/globalsip.2014.7032237.

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Min, Minghui, Liang Xiao, Caixia Xie, Mohammad Hajimirsadeghi, and Narayan B. Mandayam. "Defense against advanced persistent threats: A Colonel Blotto game approach." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997103.

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Hajimirsadeghi, Mohammad, Gokul Sridharan, Walid Saad, and Narayan B. Mandayam. "Inter-network dynamic spectrum allocation via a Colonel Blotto game." In 2016 Annual Conference on Information Science and Systems (CISS). IEEE, 2016. http://dx.doi.org/10.1109/ciss.2016.7460510.

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Guan, Sanghai, Jingjing Wang, Chunxiao Jiang, Zhu Han, Yong Ren, and Abderrahim Benslimane. "Colonel Blotto Game Aided Attack-Defense Analysis in Real-World Networks." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647886.

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Gupta, Abhishek, Galina Schwartz, Cedric Langbort, S. Shankar Sastry, and Tamer Basar. "A three-stage Colonel Blotto game with applications to cyberphysical security." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859164.

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Reports on the topic "Colonel Blotto Game"

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Gupta, Abhishek, Galina Schwartz, Cedric Langbort, S. S. Sastry, and Tamer Basar. A Three-Stage Colonel Blotto Game with Applications to Cyber-Physical Security. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada604916.

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