The wisdom of partisan crowds

被引:50
作者
Becker, Joshua [1 ,2 ,3 ]
Porter, Ethan [4 ]
Centola, Damon [1 ,5 ]
机构
[1] Univ Penn, Annenberg Sch Commun, Philadelphia, PA 19104 USA
[2] Northwestern Univ, Kellogg Sch Management, Evanston, IL 60626 USA
[3] Northwestern Univ, Northwestern Inst Complex Syst, Evanston, IL 60626 USA
[4] George Washington Univ, Sch Media & Publ Affairs, Washington, DC 20052 USA
[5] Univ Penn, Sch Engn, Philadelphia, PA 19104 USA
关键词
collective intelligence; polarization; networks; the wisdom of crowds; deliberative democracy; INFORMATION; BIAS;
D O I
10.1073/pnas.1817195116
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme-and less accurate-when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains.
引用
收藏
页码:10717 / 10722
页数:6
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