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The answer to the question of how many people will die in developed countries is a daunting figure: 30 million, according to the World Health Organization. In both networked groups where participants shared answers, everyone's responses to the question improved dramatically. Just by talking in a social network, participants came away with a much better understanding of their own smoking risks, which is a key indicator of a smoker's intention to quit.
"At the individual level, people often aren't incentivized to change their beliefs," Guilbeault says, "but if you show them that other people think differently, it can encourage belief change under the right conditions."
After completing the three rounds of questions about smoking risks, participants were then asked to complete a survey about their experience. The survey showed that when people were in networks where they could see that others were smokers and non-smokers, they were the most likely to report having improved their opinion about the other group. If they were a smoker, they now thought more favorably of non-smokers, and vice versa.
"Most people think that when someone encounters an outgroup member, they are more likely to become entrenched in their position," Centola says, "but in this study with smokers and non-smokers, we found that they actually become more receptive to one another's points of view and developed mutual respect for each other."
Centola and his lab, the Network Dynamics Group, which includes Guilbeault, have tested their idea of networked collective intelligence on a variety of topics like climate change, immigration, and gun control. This study, published today in PLOS ONE , is the first to demonstrate the power of networked collective intelligence for public health. Source:
University of Pennsylvania Journal reference:
Guilbeault, D & Centola, D. (2020) Networked collective intelligence improves dissemination of scientific information regarding smoking risks. PLOS ONE . doi.org/10.1371/journal.pone.0227813 .
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