Toward a digital twin of U.S. Congress
This assists stakeholders in resource allocation and could impact legislative dynamics, though it appears incremental as an application of existing methods to new data.
The researchers created a digital twin of U.S. Congress by training language models on daily-updated Twitter data from congresspersons, producing tweets indistinguishable from real ones and using them to predict roll-call votes and party-line crossing likelihoods.
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.