Toward a digital twin of U.S. Congress

By Hayden S. Helm, Tianyi Chen, Harvey McGuinness, Paige Lee, Brandon Duderstadt, Carey E. Priebe. arXiv.org, 2025

1 citation recorded by the source corpus.

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.

Subfields: Political Science and International Relations, Large Language Models

View Paper: https://arxiv.org/abs/2505.00006

Citations in the corpus

• Detecting Perspective Shifts in Multi-agent Systems (2025, unknown): “System of Digital Congresspersons One of our motivating applications is a system of evolving digital congressper-sons introduced in (Helm et al., 2025b).”