Game Theory in Defence Applications: A Review

Abstract

This paper presents a succinct review of attempts in the literature to use game theory to model decision-making scenarios relevant to defence applications. Game theory has been proven as a very effective tool in modelling the decision-making processes of intelligent agents, entities, and players. It has been used to model scenarios from diverse fields such as economics, evolutionary biology, and computer science. In defence applications, there is often a need to model and predict the actions of hostile actors, and players who try to evade or out-smart each other. Modelling how the actions of competitive players shape the decision making of each other is the forte of game theory. In past decades, there have been several studies that applied different branches of game theory to model a range of defence-related scenarios. This paper provides a structured review of such attempts, and classifies existing literature in terms of the kind of warfare modelled, the types of games used, and the players involved. After careful selection, a total of 29 directly relevant papers are discussed and classified. In terms of the warfares modelled, we recognise that most papers that apply game theory in defence settings are concerned with Command and Control Warfare, and can be further classified into papers dealing with (i) Resource Allocation Warfare (ii) Information Warfare (iii) Weapons Control Warfare, and (iv) Adversary Monitoring Warfare. We also observe that most of the reviewed papers are concerned with sensing, tracking, and large sensor networks, and the studied problems have parallels in sensor network analysis in the civilian domain. In terms of the games used, we classify the reviewed papers into papers that use non-cooperative or cooperative games, simultaneous or sequential games, discrete or continuous games, and non-zero-sum or zero-sum games. Similarly, papers are also classified into two-player, three-player or multi-player game based papers. We also explore the nature of players and the construction of payoff functions in each scenario. Finally, we also identify gaps in literature where game theory could be fruitfully applied in scenarios hitherto unexplored using game theory. The presented analysis provides a concise summary of the state-of-the-art with regards to the use of game theory in defence applications and highlights the benefits and limitations of game theory in the considered scenarios.

n-Player Stochastic Duel Game Model with Applied Deep Learning and Its Modern Implications

“…A review of the available literature on the use of game theory in defense applications is provided in Ref. [ 31 ]. It discusses previous papers on game theory in detail and classifies them based on different criteria.…”

Determination of a Promotional Strategy for E-Commerce Competition in Indonesia Using Game Theory

“…Game theory has basic concepts for completing a competition, including the number of players, game values, and game strategies (Ho, Rajagopalan, Skvortsov, Arulampalam, & Piraveenan, 2022). This theory provides a language for formulating, structuring, analyzing and understanding strategic scenarios and is used for strategy selection.…”

“…In this study, the concept of 2person games is used. So that the general form of the pay off matrix used is like matrix (1) below (Ho, Rajagopalan, Skvortsov, Arulampalam, & Piraveenan, 2022):…”

A Survey of Decision Making in Adversarial Games

“…As the competitive feature is ubiquitous in a large number of real-world applications, adversarial games have been extensively investigated until now [13]- [18]. For example, the authors in [13] provided a broad survey of technical advances in Stackelberg security games (SSG) in 2018, the authors in [14] reviewed some main Nash equilibrium (NE) computing algorithms for extensive-form games with imperfect information based on counterfactual regret minimization (CFR) methods, the authors in [15] reviewed a combined use of game theory and optimization algorithms along with a new categorization for researches conducted in this area, the authors in [16] reviewed distributed online optimization, federated optimization from the perspective of privacy-preserving mechanisms, and cooperative/non-cooperative games from two facets, i.e., minimizing global costs and minimizing individual costs, and the authors in [17] surveyed recent advances of decentralized online learning, including decentralized online optimization and online game, from the perspectives of problem classifications, performance metrics, state-of-the-art performance results, and potential research directions in future.…”

“…For example, the authors in [13] provided a broad survey of technical advances in Stackelberg security games (SSG) in 2018, the authors in [14] reviewed some main Nash equilibrium (NE) computing algorithms for extensive-form games with imperfect information based on counterfactual regret minimization (CFR) methods, the authors in [15] reviewed a combined use of game theory and optimization algorithms along with a new categorization for researches conducted in this area, the authors in [16] reviewed distributed online optimization, federated optimization from the perspective of privacy-preserving mechanisms, and cooperative/non-cooperative games from two facets, i.e., minimizing global costs and minimizing individual costs, and the authors in [17] surveyed recent advances of decentralized online learning, including decentralized online optimization and online game, from the perspectives of problem classifications, performance metrics, state-of-the-art performance results, and potential research directions in future. Additionally, in consideration of the importance of game theory in national defense, some reviews of game theory in defense applications were succinctly provided in [18], [19], and a survey of defensive deception based on game theory and machine learning (ML) approaches was presented in [20]. Nonetheless, a thorough overview for adversarial games from the perspectives of the basic models' knowledge, equilibrium concepts, optimal strategy seeking techniques, research frontiers, and prevailing algorithms is still lacking.…”