Presented By: Department of Political Science
Interdisciplinary Seminar on Social Science Methodology (I3SM)
Wendy K. Tam Cho, Professor of Political Science, Statistics, Mathematics, Computer Science, Asian American Studies, and the College of Law at the University of Illinois at Urbana-Champaign
Wendy K. Tam Cho, Professor of Political Science, Statistics, Mathematics, Computer Science, Asian American Studies, and the College of Law at the University of Illinois at Urbana-Champaign, will be delivering the Interdisciplinary Seminar in Social Science Methodology's virtual keynote this year on Friday, April 9 from 9:30 am to 11:00 am EDT. She will be presenting a talk titled "A Parallel Evolutionary Multiple-Try Metropolis Markov Chain Monte Carlo Algorithm for Redistricting Analysis."
Abstract:
Important insights into redistricting can be gained by formulating and analyzing the problem within a large-scale spatial optimization and sampling framework. Redistricting is an application of the graph-partitioning problem that is NP-Hard. We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large, complex, and constrained spatial state space. Our algorithm combines the advantages of evolutionary algorithms as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hastings ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel computing architecture through the integration of a parallel EA framework that guides Markov chains running in parallel. We experimentally demonstrate the effectiveness of our algorithm to utilize hundreds of thousands of processors for the redistricting problem. The massive computing power allows us to extract new substantive insights that closely mesh with the framework that the Supreme Court has elucidated for electoral reform.
--
The primary function of the Interdisciplinary Seminar in Social Science Methodology (I3SM) is to provide an interdisciplinary forum for students and faculty to present their current projects and to receive feedback on either the methodological component of their project or a methodology under development. Presenters can also present new research questions and ideas and receive ideas about which methodologies would work best to tackle such questions. We define methodology broadly as the approaches to which data is collected and/or organized to give empirical content to social science research. It includes both qualitative and quantitative methodologies.
To join the meeting via Zoom, email skuzushi@umich.edu for the meeting link.
Abstract:
Important insights into redistricting can be gained by formulating and analyzing the problem within a large-scale spatial optimization and sampling framework. Redistricting is an application of the graph-partitioning problem that is NP-Hard. We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large, complex, and constrained spatial state space. Our algorithm combines the advantages of evolutionary algorithms as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hastings ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel computing architecture through the integration of a parallel EA framework that guides Markov chains running in parallel. We experimentally demonstrate the effectiveness of our algorithm to utilize hundreds of thousands of processors for the redistricting problem. The massive computing power allows us to extract new substantive insights that closely mesh with the framework that the Supreme Court has elucidated for electoral reform.
--
The primary function of the Interdisciplinary Seminar in Social Science Methodology (I3SM) is to provide an interdisciplinary forum for students and faculty to present their current projects and to receive feedback on either the methodological component of their project or a methodology under development. Presenters can also present new research questions and ideas and receive ideas about which methodologies would work best to tackle such questions. We define methodology broadly as the approaches to which data is collected and/or organized to give empirical content to social science research. It includes both qualitative and quantitative methodologies.
To join the meeting via Zoom, email skuzushi@umich.edu for the meeting link.
Explore Similar Events
-
Loading Similar Events...
