New research on collective intelligence demonstrates an innovative approach to combating belief polarization. The core idea of collective intelligence is that collaboration can improve both individual and group performance, depending on characteristics of group members and on conditions that facilitate (or inhibit) the exchange of information. In a recent study, researchersfrom the University of Pennsylvania’s Annenberg School for Communication set up social networks and found that they could eliminate belief polarization between liberals and conservatives on the issue of climate change. The key to the change was keeping people’s political affiliation hidden, a finding consistent with the idea that collective intelligence is greatest under conditions that promote the free exchange of alternative viewpoints. The study provides a proof-of-concept for how social media can be used constructively to improve people’s understanding of climate change and other urgent issues, while eliminating polarization.
Each participant was randomly assigned to a control condition, or to one of three bipartisan “network” conditions that included an equal number of liberals and conservatives. In the control condition, participants worked on the task in isolation. In the network conditions, after providing their initial estimate, participants were exposed to information about their four network “neighbors,” two of whom were conservative and two of whom were liberal. In the first of these conditions, along with their own estimate, participants saw on their screens the average of their neighbors’ estimates. In the second condition, which primed participants to think about politics, these two values were displayed above logos of the Democratic Party and Republican Party—an elephant and a donkey. In the final network condition, which primed participants to think specifically about their neighbors’ politics, the values were presented along with the neighbors’ screennames and political affiliation (conservative or liberal).
In the control condition, liberals gave more accurate estimates in the first round than did conservatives (74% vs. 61%), and neither group improved much across rounds. By contrast, in the network condition with no political cues, accuracy was higher for liberals than for conservatives in the first round, but both groups improved to greater than 85% by the third round, eliminating the difference between the groups. In the two other network conditions, which included political cues, both groups showed improvement across rounds. However, belief polarization persisted, with liberals outperforming conservatives in all rounds. Taken together, the results indicate that collective intelligence can emerge in bipartisan social networks, especially in the absence of political identifiers.
In this study, which focused on climate change, liberals were initially more accurate in their interpretation of evidence than conservatives. However, for other issues, just the opposite may be true. For example, some of the most strident opposition to vaccination comes from liberals. For these issues, there is every reason to think that bipartisan networks can be as effective in eliminating belief polarization as they appear to be for climate change.
As a 2018 report by the United Nations’ Intergovernmental Panel on Climate Change implored, rapid, far-reaching changes in human behavior are needed to limit global warming. In the United States and elsewhere, the polarization of people’s beliefs over the cause of global warming is a major impediment to implementing and sustaining such changes. This new study shows how social media can be used to harness the power of collective intelligence to combat this pernicious problem. The more general message of this study is one that we would do well to remember at this particular moment in history: When we set aside politics, there can be real value to having conversations with people with whom we disagree.
