[Colloquium] Thursday 1/24 | Adam Bonica at the Computational Social Science Workshop

[Nora Nickels via Colloquium]

THE COMPUTATIONAL SOCIAL SCIENCE WORKSHOP PRESENTSADAM BONICAASSOCIATE
PROFESSOR OF POLITICAL SCIENCESTANFORD UNIVERSITY



The Computational Social Science Workshop
<https://macss.uchicago.edu/content/computation-workshop>at the University
of Chicago cordially invites you to attend this week’s talk:


FORECASTING CONGRESSIONAL ELECTION OUTCOMES AND LEGISLATIVE VOTING BEHAVIOR
USING CAMPAIGN FINANCE DATA
<https://github.com/uchicago-computation-workshop/adam_bonica/blob/master/a17_bonica_ajps_2017.pdf>


Summary: This article develops a generalized supervised learning
methodology for inferring roll-call scores from campaign contribution data.
Rather than use unsupervised methods to recover a latent dimension that
best explains patterns in giving, donation patterns are instead mapped onto
a target measure of legislative voting behavior. Supervised models
significantly outperform alternative measures of ideology in predicting
legislative voting behavior. Fundraising prior to entering office provides
a highly informative signal about future voting behavior. Impressively,
forecasts based on fundraising as a nonincumbent predict future voting
behavior as accurately as in-sample forecasts based on votes cast during a
legislator’s first 2 years in Congress. The combined results demonstrate
campaign contributions are powerful predictors of roll-call voting behavior
and resolve an ongoing debate as to whether contribution data successfully
distinguish between members of the same party.


THURSDAY, 1/24/201911:00AM-12:20PMKENT 120


A light lunch will be provided by Cedars Mediterranean Kitchen.



Adam Bonica is an Associate Professor of Political Science at Stanford
University. His research is at the intersection of data science and
politics, with a focus on American Politics. Among his research
contributions is the development of quantitative methods for measuring
ideological preferences using campaign contributions. This provides a
unified approach to measuring political preferences for a wide array of
political actors, which are made available as part of the Database on
Ideology, Money in Politics, and Elections (DIME). His current research
examines how the challenges of early fundraising, and the advantages
provided by a candidate’s professional network, have been instrumental in
sustaining deep representational imbalances in Congress. His work has
appeared in the American Journal of Political Science, Political Analysis,
Journal of Economic Perspectives, Journal of Law, Economics, and
Organization, and JAMA Internal Medicine.




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The 2018-2019 Computational Social Science Workshop
<https://macss.uchicago.edu/content/computation-workshop>meets Thursdays
from 11 a.m. to 12:20 p.m. in Kent 120. All interested faculty and graduate
students are welcome.

Students in the Masters of Computational Social Science program are
expected to attend and join the discussion by posting a comment on the issues
page  <https://github.com/uchicago-computation-workshop/adam_bonica/issues>of
the workshop’s public repository on GitHub.
<https://github.com/uchicago-computation-workshop/adam_bonica> Further
instructions are documented in the Computational Social Science
Workshop’s README
on Github. <https://github.com/uchicago-computation-workshop/README>
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