Thurs 4 Jun, 3:30pm, Kane Hall 210 at the University of Washington. Part of the Math Across Campus series.
We shall consider two topics involving coalitions and voting. Each topic involves open questions both in mathematics (probability theory) and in political science.
(1) Individuals in a committee or election can increase their voting power by forming coalitions. This behavior yields a prisoner’s dilemma, in which a subset of voters can increase their power, while reducing average voting power for the electorate as a whole. This is an unusual form of the prisoner’s dilemma in that cooperation is the selfish act that hurts the larger group. The result should be an ever-changing pattern of coalitions, thus implying a potential theoretical explanation for political instability.
(2) In an electoral system with fixed coalition structure (such as the U.S. Electoral College, the United Nations, or the European Union), people in diferent states will have different voting power. We discuss some flawed models for voting power that have been used in the past, and consider the challenges of setting up more reasonable mathematical models involving stochastic processes on trees or networks.
Fri 5 Jun, 9:45am, Kane Hall 225 at the University of Washington. Part of the 10th anniversary celebration of the Center for Statistics and the Social Sciences.
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 a results from our own research and that of others.
Mon 8 Jun, 11am, Fairmont Lounge, St. John’s College, 2111 Lower Mall, University of British Columbia. Statistics Department seminar.
A challenge in statistics is to construct models that are structured enough to be able to learn from data but not be so strong as to overwhelm the data. We introduce the concept of “weakly informative priors” which contain important information but less than may be available for the given problem at hand. We also discuss some related problems in developing general models for taxonomies and deep interactions. We consider how these ideas apply to problems in social science and public health. If you don’t walk out of this talk a Bayesian, I’ll eat my hat.
Mon 8 Jun, 3pm, Fairmont Lounge, St. John’s College, 2111 Lower Mall, University of British Columbia. Statistics Department seminar.
If you come to any of these, please ask lots of questions!
P.S. I’ve never spoken at UBC, but I have given a couple of talks in the statistics department at UW. The first time was twenty years ago. The talk went OK, I think–it was on medical imaging–but I did a horrible thing by leading off with a joke. I could probably get away with that now, but it didn’t go over well then. In my defense, the joke was related to the topic of the talk. But it was a pretty bad joke. The second talk was about twelve years ago. The topic was model checking in spatial statistics. I think it went fine, but I recall that there was one spatial statistics expert in the audience who was disappointed at how simple my model was. It worked ok for what we were doing, though.