Uncovering hidden cooperation in democratic institutions

Project goal

Generalising the method of Vote-Trading Networks, previously developed to study hidden cooperation in the US Congress, to a wider set of democratic institutions, developing a research programme in the measurement and characterisation of hidden cooperation on a large scale.



Daniele Guariso and Raluca-Florica Popp


Omar A Guerrero, Turing Research Fellow, UCL
Ulrich Matter, University of St Gallen
Dong Nguyen, Turing Research Fellow, University of Edinburgh

Project detail

The project aims at improving our understanding of cooperation in democratic institutions. In particular, it will shed new light on cooperative behaviour that is intentionally ‘hidden’. An example of such hidden cooperation is when two legislators agree to support each other’s favourite bills, despite their ideological preferences, and/or despite such support being disapproved of by their respective voters or campaign donors.

This kind of behaviour is key to the passage or blockage of critical legislation; however, we know little about it due to its unobservable nature. The objective of this project is to exploit newly available big data on voting behaviour from different institutional contexts and state-of-the-art methods from data science, in order to develop two distinct research papers with clear policy implications for the design and evaluation of political institutions.

Political institutions, such as parliaments and congresses, shape the life of every democratic society. Hence, understanding how legislative decisions arise from hidden agreements has direct implications on the guidelines that governments follow when conducting policy interventions. Moreover, decision making by voting is common in other areas than legislative law-making. It is prevalent in courts, international organisations, as well as in board rooms of private enterprises.

The supervisors have collected comprehensive data sets on two institutions; the US Supreme Court and the United Nations General Assembly. Each intern will work on one institution, using the data provided by the supervisors and, sometimes, collecting complementary data (through web scraping). The work conducted on the two institutions will share a set of tools and methods, but also have unique requirements. In order to streamline the workflow, the internship will be structured in three phases. Every week, there will be a group meeting where each intern will give a presentation of his or her progress. This will be an opportunity to share ideas, questions, challenges and solutions that the interns have experienced. It will also serve to evaluate progress and adjust goals and objectives.