Rational irrationality: modeling climate change belief polarization using bayesian networks

Cook, John and Lewandowsky, Stephan (2016) Rational irrationality: modeling climate change belief polarization using bayesian networks. Topics in Cognitive Science, 8 1: 160-179. doi:10.1111/tops.12186

Author Cook, John
Lewandowsky, Stephan
Title Rational irrationality: modeling climate change belief polarization using bayesian networks
Journal name Topics in Cognitive Science   Check publisher's open access policy
ISSN 1756-8765
Publication date 2016-01-01
Year available 2009
Sub-type Article (original research)
DOI 10.1111/tops.12186
Open Access Status Not Open Access
Volume 8
Issue 1
Start page 160
End page 179
Total pages 20
Place of publication Malden, MA, United States
Publisher Wiley-Blackwell Publishing
Language eng
Abstract Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can simulate rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming (AGW). The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group.
Keyword Bayes' theorem
Bayesian updating
Belief polarization
Climate change
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Global Change Institute Publications
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