> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/deep-learning/game-theory.md). # Game Theory Some AI systems are not a single model minimizing a loss but players whose best move depends on what the others do. This page is about game theory as it shows up in computer science and AI, and it starts from one illustrated overview. [In computer science](https://www.analyticsvidhya.com/blog/2019/11/game-theory-ai/) is Sharoon Saxena's "Game (Theory) for AI? An Illustrated Guide for Everyone" on Analytics Vidhya. It calls game theory the oldest algorithm in artificial intelligence and walks through its types, the Nash equilibrium, and how it is used for AI. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/deep-learning/game-theory.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.