Algorithmic game theory in financial markets

Algorithmic game theory merges the precision of mathematical game theory with the computational power of algorithms to solve complex problems in financial markets. At its core, game theory studies strategic interactions among rational agents, where the outcome for any participant depends not only on their own decisions but also on the choices of others. This framework is particularly resonant in financial markets, a domain characterized by the strategic interplay of numerous actors, including traders, firms, regulators, and investors.

The widespread application of game theory in financial markets is attributed to its ability to model and predict outcomes in competitive and co-operative settings. It provides a structured way to analyze how market participants make decisions under uncertainty and in environments of mutual influence. For instance, game theory helps in understanding how traders strategize in high-frequency trading environments...