Charting the Shapes of Stories with Game Theory
This work addresses the challenge of extracting deeper insights from narratives for researchers in literature, AI, and interdisciplinary studies, though it appears incremental in applying existing AI methods to a new domain.
The authors tackled the problem of analyzing story structures by modeling them as game-theoretic objects using AI, enabling quantitative analysis of character decisions and counterfactual plot lines, as demonstrated on Shakespeare's Romeo and Juliet.
Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical tools to extract structure from stories and insights from structure. Historically, these tools have been restricted to one dimensional charts and dynamic social networks; however, modern AI offers the possibility of identifying more fully the plot structure, character incentives, and, importantly, counterfactual plot lines that the story could have taken but did not take. In this work, we use AI to model the structure of stories as game-theoretic objects, amenable to quantitative analysis. This allows us to not only interrogate each character's decision making, but also possibly peer into the original author's conception of the characters' world. We demonstrate our proposed technique on Shakespeare's famous Romeo and Juliet. We conclude with a discussion of how our analysis could be replicated in broader contexts, including real-life scenarios.