University of Cambridge > Talks.cam > Machine Learning @ CUED > Culture wars, voting and polarization: divisions and unities in modern American politics

Culture wars, voting and polarization: divisions and unities in modern American politics

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general audience talk (non-technical) -- NOTE change of room to LR4!

On the night of the 2000 presidential election, Americans sat riveted in front of their televisions as polling results divided the nation’s map into red and blue states. Since then the color divide has become a symbol of a culture war that thrives on stereotypes—pickup-driving red-state Republicans who vote based on God, guns, and gays; and elitist, latte-sipping blue-state Democrats who are woefully out of touch with heartland values. But how does this fit into other ideas about America being divided between the haves and the have-nots? Is political polarization real, or is the real concern the perception of polarization? We address these questions using recent and historical research.

BIO: Andrew Gelman is one of the leading quantitative researchers at the interface of social science and statistics. He has received numerous honors for his work, including the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40.

Andrew has written several books on statistical methods, as well as ‘Red State, Blue State, Rich State, Poor State’, a book about U.S. voting patterns. He is also well known for his blog, ‘Statistical Modeling, Causal Inference, and Social Science’, which covers topics such as data analysis, statistical graphics, politics, social science and academics in general.

Andrew received his undergraduate degrees in math and physics at MIT and his PhD in statistics from Harvard. He is currently a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.

DIRECTIONS: Directions to the main building of the Department of Engineering can be found here . The room LR4 is on the ground floor, just to the right from the main entrance.

This talk is part of the Machine Learning @ CUED series.

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