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Topics in Cognitive Science
Article . 2016 . Peer-reviewed
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Rational Irrationality: Modeling Climate Change Belief Polarization Using Bayesian Networks

Authors: Stephan Lewandowsky; Stephan Lewandowsky; John Cook; John Cook;

Rational Irrationality: Modeling Climate Change Belief Polarization Using Bayesian Networks

Abstract

AbstractBelief 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.

Countries
United Kingdom, Australia
Keywords

2805 Cognitive Neuroscience, /dk/atira/pure/core/keywords/tedcog, Consensus, Climate Change, Culture, Bayesian updating, 1702 Artificial Intelligence, Models, Psychological, Bayes' theorem, 1709 Human-Computer Interaction, Memory, Surveys and Questionnaires, Climate change, Humans, Belief polarization, Rationalization, 3205 Experimental and Cognitive Psychology, Australia, Bayes Theorem, TeDCog, United States, 3310 Linguistics and Language, Perception, /dk/atira/pure/core/keywords/psyc_memory

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
213
Top 1%
Top 10%
Top 1%
bronze