Press Archive

John Q. Public: Modeling information processing and political opinion on the TeraGrid

Published 10/14/2005

By Trish Barker, NCSA

How do people assess political candidates? How do campaign events and new information change their views? While there are various theories that address these questions, Sung-youn Kim, a visiting assistant professor at the University of Iowa, and Milton Lodge and Charles Taber, professors at Stony Brook University in New York, saw gaps between existing models and empirical findings. Several theories seemed to partially explain actual political behavior and judgment, but none seemed complete on its own.

With support from the National Science Foundation, the researchers integrated both cognitive and affective information-processing theories into a computational model (cleverly dubbed John Q. Public) that simulates how voters' political opinions fluctuate during a campaign. Using data from the 2000 National Annenberg Election Survey (NAES), they constructed virtual voters, or agents, each with a unique mindset.

Campaign messages were gleaned from news accounts and reduced to simple sentences ("Bush said Gore is dishonest" or "Bush said Bush is anti-abortion," for example). The computational model parses each sentence, retrieves relevant concepts from the long-term memory of each agent, and updates each agent's knowledge and attitudes accordingly. Using 100 agents, this simulation was repeated 100 times, returning results that accord well with the actual 2000 polling data.

Because the simulation employed multiple independent agents, with each one representing a single voter, each agent had to be loaded in a separate thread. Because of this complexity and the sheer computational intensity of the simulation, the researchers relied on the computational power of the TeraGrid, employing Itanium II systems at both the National Center for Supercomputing Applications (NCSA) and the San Diego Supercomputer Center (SDSC). Using 10 processors, the simulation took about five hours and produced 10 GB of output.

Now that the simulation's accuracy has been demonstrated, it can be used as a platform to develop and test hypotheses. By incorporating new parameters, researchers can see how these changes affect candidate evaluations.

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