Opinion polarization by learning from social feedback

被引:84
作者
Banisch, S. [1 ]
Olbrich, E. [1 ]
机构
[1] Max Planck Inst Math Sci, Inselstr 22, D-04103 Leipzig, Germany
基金
欧盟地平线“2020”;
关键词
Opinion Formation; Polarization; Reinforcement Learning; Social Feedback; Computational Sociology; BIASED ASSIMILATION; DYNAMICS; MODEL; ATTITUDE; COORDINATION; CONVERGENCE; CULTURE;
D O I
10.1080/0022250X.2018.1517761
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.
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页码:76 / 103
页数:28
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