Game theory is a branch of applied mathematics that studies strategic interactions between multiple rational agents, each seeking to optimize their outcomes in relation to the choices of others. It models situations of competition or cooperation, where the decision of each participant affects the outcomes of the rest. Game theory is distinguished from other analytical approaches by its ability to formalize strategic behavior in contexts where decisions are interdependent.
Use cases and examples
In artificial intelligence, game theory is used to model interactions between autonomous agents, such as in multi-agent systems, cybersecurity (attack detection and prevention), automated negotiation, and shared resource management. It is also widely used in digital economics, for dynamic pricing, automated auctions, and the design of incentive mechanisms on digital platforms.
Main software tools, libraries, frameworks
Key tools include the Python library Gambit for computational analysis of classic and evolutionary games, DeepMind's OpenSpiel for experimentation on zero-sum and general-sum games, and Axelrod-Python for simulating iterated prisoner's dilemma scenarios. General-purpose tools like MATLAB and R also offer dedicated modules for game theory.
Latest developments, evolutions, and trends
Recent research focuses on algorithmic game theory, integration with machine learning (especially for training agents in competitive environments), and the study of games with incomplete information. Applications in generative AI and collaborative robotics are rapidly expanding, offering new frameworks for strategic and collective decision-making.