Relevance of Game Theory in the Context of Artificial Intelligence

A brief introduction to those unfamiliar with Game Theory should start from the assumption that it is not about games in the ordinary sense. Such theory suggests a mathematical approach to ensuring a response for more significant gain or lesser loss in situations involving two or more players. In essence, game theory studies the decision-making of agents.

A classic example is the game of tic-tac-toe, a scenario in which each player can win, lose, or draw. Depending on each player’s move, the other will not be able to win, and therefore, their best choice will be a draw.

According to Figueiredo (1994), Game Theory could be called the Theory of Interdependent Decisions, as it deals with the analysis of situations where no individual can conveniently decide without taking into account the possible decisions of others. This mathematical approach was initially aimed at identifying economic behaviors but soon became of interest in the social sphere. Even in the legal field, the theory is employed to identify better strategies for each player.

Generative Artificial Intelligence (AI) technology is based on the operation of algorithms in so-called artificial neural networks that learn from data through a system known as deep learning or deep neural networks.

The approach of game theory is also a tool by which AI can be used (or is already used in certain situations) to improve its degree of accuracy or to provide the best possible response to certain conditions. As an example, KUSYK et al. report the use of game theory algorithms for the control of swarms of UAVs (Unmanned Aerial Vehicles). FUGATE and DERGUSON-WALTER describe how the combination of AI algorithms and game theory can be extended to provide practical guidance for cyber defense scenarios.

HAZRA and ANJARIA (2022) highlight that AI systems use games for training learning algorithms, and considering that such structures need to face situations with imperfect information, game theory provides substantial assistance. According to RUSSEL (2021), game theory, which boils down to rational decision-making theory for multiple agents, produces many exciting behaviors. Finally, the relevance of game theory is observed in various contexts (economics, legal), especially in the evolutionary scenario of AI. However, there is a particular scarcity in the literature on the subject, which should help improve deep learning models.


FIGUEIREDO, Reginaldo Santana. Teoria dos jogos: conceitos, formalização matemática e aplicação à distribuição de custo conjunto. Gestão & produção, v. 1, n. 3, p. 273–289, 1994.
FUGATE, Sunny; FERGUSON-WALTER, Kimberly. Artificial intelligence and game theory models for defending critical networks with cyber deception. AI magazine, v. 40, n. 1, p. 49–62, 2019.
HAZRA, Tanmoy; ANJARIA, Kushal. Applications of game theory in deep learning: a survey. Multimedia tools and applications, v. 81, n. 6, p. 8963–8994, 2022.
KUSYK, Janusz. et al. Artificial intelligence and game theory controlled autonomous UAV swarms. Evolutionary intelligence, v. 14, n. 4, p. 1775–1792, 2021.
RUSSEL, Stuart. Inteligência artificial a nosso favor: Como manter o controle sobre a tecnologia. Tradução Berilo Vargas. 1ª ed. São Paulo: Companhia das Letras, 2021.

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