Charting the Shapes of Stories with Game Theory
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917129667739648 |
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| author | Daskalakis, Constantinos Gemp, Ian Jiang, Yanchen Leme, Renato Paes Papadimitriou, Christos Piliouras, Georgios |
| author_facet | Daskalakis, Constantinos Gemp, Ian Jiang, Yanchen Leme, Renato Paes Papadimitriou, Christos Piliouras, Georgios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Charting the Shapes of Stories with Game Theory Daskalakis, Constantinos Gemp, Ian Jiang, Yanchen Leme, Renato Paes Papadimitriou, Christos Piliouras, Georgios Computer Science and Game Theory Artificial Intelligence 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. |
| title | Charting the Shapes of Stories with Game Theory |
| topic | Computer Science and Game Theory Artificial Intelligence |
| url | https://arxiv.org/abs/2412.05747 |