Imagine teaching a computer the rules of a game, not by coding, but by simply describing it in plain English. Researchers are exploring this exciting possibility by using large language models (LLMs) to automatically create formal game representations. This process, called "autoformalization," transforms natural language descriptions into executable logic that defines the rules, player roles, and potential outcomes of a game. Think of it like an AI game designer that learns from human language. Researchers tested this approach with various games, including classics like Prisoner's Dilemma, Stag Hunt, and Battle of the Sexes. They fed the LLM different descriptions of each game, and the LLM then generated code that represented the game’s logic. The results were promising, with high accuracy in converting natural language into formal game rules. These AI-generated games were then used in simulated tournaments to see how different strategies performed. Interestingly, the tournament showed how strategies like "tit-for-tat" and "best response" play out in different game scenarios. This research opens doors to a future where AI can not only play games, but also design and analyze them. This could have implications beyond entertainment, such as modeling complex real-world interactions in economics, politics, and even biology. However, challenges remain. LLMs aren't perfect and can sometimes misinterpret descriptions or generate faulty code. Improving the feedback loop between the LLM and the game simulator is key to refining this process. Imagine a future where AI can model complex negotiations, design economic policies, or even predict ecological dynamics, all starting from a simple natural language description. While still in its early stages, this research offers a glimpse into the exciting potential of LLMs to bridge the gap between human language and complex formal systems.