There are two kinds of polarization that the media and the public often get confused... One type is issue polarization: “how much people disagree on policies like ‘What should be the tax rates?’, or ‘What should be the laws to regulate guns?’.” Those divisions have been widening of late. But they aren’t nearly as incendiary as social or “affective” polarization, which is about anger, distrust, resentment, tribal identity, and mutual loathing (see Figure, click to enlarge).
Figure Legend - Recent years have seen a marked rise in “affective” polarization, a feeling of mutual dislike and mistrust between the two sides. The trend is illustrated in data from the American National Election Survey: People's feeling of warmth toward members of their own party (green) has held steady since 1980, whereas their feelings toward members of the other party (purple) have dropped. The difference (black) is a measure of affective polarization.
[There are] ...at least four basic strategies for studying any form of polarization...The classic method is observation: using surveys and historical data to track how polarization has increased or decreased over time and which issues have been the most divisive...A second, newer strategy is to analyze the tsunami of data now available from the Internet...very useful for studying things that you cannot learn from a survey...like exactly who listens to whom and how ideas spread through the resulting social network like a contagion...Then there is the experimental approach: watching how polarization develops among volunteers in a laboratory setting. These experiments allow you to control the conditions, separate the signal from the noise, and tease out what’s cause and what’s effect...finally...models in the form of mathematical equations or computer simulations can help researchers explore the sometimes surprising outcomes of simple starting conditions or assumptions.Several different kinds of modeling work are described in the article. Here is just the first:
Axelrod, a political scientist at the University of Michigan in Ann Arbor, "wasn't interested in the left-to-right kind of differences, so I treated ‘culture’ simply as a list of arbitrary features that were observable, like ‘What kind of hat do you wear?’ or ‘What ethnicity are you?’” Next, Axelrod modeled people as independent snippets of code, or “agents,” that could move around a simulated landscape, and gave each agent some initial set of cultural features. Then he set them to interacting with their neighbors according to two simple rules. First, the more items of culture agents share, the more likely they are to interact. And second, if agents do interact, they adopt some feature of the agent they’re interacting with...In sum, says Axelrod, the model was nothing but assimilation plus homophily: “Like gravity, it's all pulling together, right? There's nothing but attraction.” Yet the result wasn’t anything like global consensus. Instead, says Axelrod, the model consistently locked itself into a patchwork resembling the multiple language regions of Europe—or those filter bubbles on Facebook. “It’s what I call local convergence and global polarization,” he says: Cultures do indeed tend toward consensus within a finite region. But at the boundaries, the differences eventually become so stark that the agents on either side quit interacting at all. “So they never talk to each other again,” says Axelrod, “and that's why it freezes.”Subsequent models take on emotions, show the critical role played by negative emotions, and show how groups tend to split into two cliques. The last section of the article discusses interventions to reduce polarization.