A Computational Model of the Citizen as Motivated Reasoner: Modeling the Dynamics of the 2000 Presidential Election

A Computational Model of the Citizen as Motivated Reasoner: Modeling the Dynamics of the 2000... A computational model of political attitudes and beliefs is developed that incorporates contemporary psychological theory with well-documented findings from electoral behavior. We compare this model, John Q. Public (JQP), to a Bayesian learning model via computer simulations of observed changes in candidate evaluations over the 2000 presidential campaign. In these simulations, JQP reproduces responsiveness, persistence, and polarization of political attitudes, while the Bayesian learning model has difficulty accounting for persistence and polarization. We conclude that “motivated reasoning”—the discounting of information that challenges priors along with the uncritical acceptance of attitude-consistent information—is the reason our model can better account for persistence and polarization in candidate evaluations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Political Behavior Springer Journals

A Computational Model of the Citizen as Motivated Reasoner: Modeling the Dynamics of the 2000 Presidential Election

Loading next page...
 
/lp/springer_journal/a-computational-model-of-the-citizen-as-motivated-reasoner-modeling-BwYKnHLOAm
Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Political Science and International Relations; Political Science; Sociology, general
ISSN
0190-9320
eISSN
1573-6687
D.O.I.
10.1007/s11109-009-9099-8
Publisher site
See Article on Publisher Site

Abstract

A computational model of political attitudes and beliefs is developed that incorporates contemporary psychological theory with well-documented findings from electoral behavior. We compare this model, John Q. Public (JQP), to a Bayesian learning model via computer simulations of observed changes in candidate evaluations over the 2000 presidential campaign. In these simulations, JQP reproduces responsiveness, persistence, and polarization of political attitudes, while the Bayesian learning model has difficulty accounting for persistence and polarization. We conclude that “motivated reasoning”—the discounting of information that challenges priors along with the uncritical acceptance of attitude-consistent information—is the reason our model can better account for persistence and polarization in candidate evaluations.

Journal

Political BehaviorSpringer Journals

Published: Oct 14, 2009

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off