Reducing belief polarization under a Bayesian framework
It is possible using Bayesian Networks (Bayes Nets) to simulate belief polarization, where two people receiving the same evidence update their beliefs in opposite directions. I have developed a quantitative model using Bayes Nets to simulate belief polarization with full Bayesian updating. The model assumes that worldview and trust in science are major drivers in how people process evidence that pertains to their worldview. Experimental data measuring change in belief in response to scientific evidence for worldview relevant issues such as climate change and evolution will be used to determine whether belief polarization is possible under a rational Bayesian framework. The model predicts certain outcomes for various interventions designed to increase trust in science or reduce the influence of worldview which can be tested experimentally.
This research will pinpoint specific interventions that are effective in reducing the influence of worldview in biasing how people process scientific evidence.