Professor Laurent Bouton named Provost’s Distinguished Associate Professor

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Laurent Bouton has been named a Provost’s Distinguished Associate Professor by Georgetown University. The Distinguished Associate Professorship is awarded annually to a select group of faculty members who are “pushing the envelope of knowledge in their field and transmitting their passion for such work to their students.”

Laurent received his PhD from the European Center for Advanced Research in Economics and Statistics (ECARES) at the Université Libre de Bruxelles in 2009. He then took a position as Assistant Professor at Boston University. Prof. Bouton joined the faculty at Georgetown in 2013.

In addition to his appointments at BU and Georgetown, Prof. Bouton has been a visiting faculty member at MIT and Harvard, a FNRS Research Associate at the Université Libre de Bruxelles and a Research Affiliate of the Centre for Economic Policy Research. Prof. Bouton is also a Faculty Research Fellow of the National Bureau of Economic Research.

Prof. Bouton’s work focuses primarily on incentives faced by voters under various electoral systems. Much of his work concerns the information aggregation properties of electoral systems; i.e., the way in which voters use their information and how their use of information determines electoral outcomes.  Particular emphasis is put on elections in which there are more than two alternatives (candidates, parties or policies) on the ballot.

Political economists have long recognized the critical importance of information aggregation in an age of increasingly decentralized and fragmented media coverage of the political process. Individual voters may bring a diffuse set of beliefs to the voting booth.  These beliefs are shaped by ideology and are modified by the receipt of new information.  The question that Prof. Bouton addresses is whether, given the fragmentation of information sources, modern elections can produce the “right” outcome when voters would agree on the outcome in principle if they had the objectively correct data.