Toward a digital twin of U.S. Congress
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910923874107392 |
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| author | Helm, Hayden Chen, Tianyi McGuinness, Harvey Lee, Paige Duderstadt, Brandon Priebe, Carey E. |
| author_facet | Helm, Hayden Chen, Tianyi McGuinness, Harvey Lee, Paige Duderstadt, Brandon Priebe, Carey E. |
| contents | In this paper we provide evidence that a virtual model of U.S. congresspersons based on a collection of language models satisfies the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data are capable of producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward a digital twin of U.S. Congress Helm, Hayden Chen, Tianyi McGuinness, Harvey Lee, Paige Duderstadt, Brandon Priebe, Carey E. Computation and Language Artificial Intelligence Computers and Society Social and Information Networks In this paper we provide evidence that a virtual model of U.S. congresspersons based on a collection of language models satisfies the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data are capable of producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis. |
| title | Toward a digital twin of U.S. Congress |
| topic | Computation and Language Artificial Intelligence Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2505.00006 |