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.

Are you ready for a world where super-intelligent robots faithfully impersonate people? To help see what that might look like, The Wall Street Journal deployed ChatGPT, a free (for now) Artificial Intelligence trained on a huge dataset researchers gathered through 2021, which recently became a viral hit. We asked it to compose tweets in the style of public figures and institutions to see if anyone could distinguish them from the real thing. We included specifics in our prompts to the AI: write a tweet by Neil deGrasse Tyson about the universe. The topics we picked were based on the author's previous tweets.