January 30, 2020 | News | Computational network models
Partisanship in the US Congress has slowed down the decline of bill passage
The Rotunda U.S. Capitol Building in Washington D.C. © iStockphoto.com/HaizhanZheng
Party membership alone does not tell you everything about the collaboration behavior of politicians. Cooperation across party lines has always been the basis for political compromises. Finding these among US Congresspeople seems to have become more difficult in recent years.
With the help of computational network models, Samin Aref, research scientist at the Laboratory of Digital and Computational Demography, analyzes US political polarization and its impact on passing bills. His joint study with Zachary Neal, Associate Professor at Michigan State University, is published in Scientific Reports.
They analyzed collaborations among members of the US House of Representatives and US Senate over the period 1979-2016 and found that the opposing groups have become increasingly partisan. The animation shows two opposing coalitions in the US Senate and how they have become more ideologically unified. In these partisan coalitions, the legislators have increasingly collaborated with those from their own party, leading to a decrease in cooperation across party lines. This can be clearly seen in the animation showing predominantly blue names (senators of the Democratic Party) in one coalition and the predominantly red names (senators of the Republican Party) in the other coalition.
The study finds that although polarization has some undesirable effects, the dominance of a single polarized coalition can facilitate passing bills.
“Our models help us to find groups of legislators in networks of political collaborations”, says Samin Aref. He adds, “they allow us to find the best way to put all the legislators into two groups such that collaborations happen mostly within the groups, and not between the groups.”
Developing such models is noteworthy, because finding these groups with such accuracy was thought to be infeasible until now. “Our main technical challenge was exploring an incredibly large number of possibilities for grouping legislators. In our models for the US House of Representatives, there are more solutions than there are atoms in the universe!”, says Samin Aref. Now, these models allow a deeper analysis of political collaborations by looking beyond party affiliations.
Aref, A., Neal, Z.: Detecting coalitions by optimally partitioning signed networks of political collaboration. Sci Rep 10, 1506 (2020). DOI: doi.org/10.1038/s41598-020-58471-z