Modeling Dynamic Identities and Uncertainty in Social Interactions

Modeling Dynamic Identities and Uncertainty in Social Interactions
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对社交互动中的动态身份和不确定性进行建模

DOI:
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发表时间:
2016
期刊:
影响因子:
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通讯作者:
Kimberly B. Rogers
Kimberly B. Rogers
中科院分区:
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文献类型:
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作者:
T. Schröder;J. Hoey;Kimberly B. Rogers

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基于贝叶斯概率论,我们提出了一种情感控制理论(BayesACT),该理论更好地解释了自我和他人在互动过程中身份意义的动态波动,阐明了人们如何通过社会经验推断和调整意义,并展示了个体对身份的不确定感知如何产生稳定的互动模式。通过模拟,我们说明了这种概括如何通过平衡文化共识与个人对共同意义的偏差,平衡意义验证与反映变化的学习过程,以及考虑沟通身份中的噪音,来解决社会学和社会心理学中具有理论意义的几个问题。我们还展示了该模型如何解释关于自我核心特征的争论,这些特征可以被理解为稳定而又可塑,连贯而又由可能具有相互竞争意义的多个身份组成。我们讨论了该模型在社会学不同领域的应用,对理解身份和社会互动的影响,以及社会行为计算模型的理论基础。
Drawing on Bayesian probability theory, we propose a generalization of affect control theory (BayesACT) that better accounts for the dynamic fluctuation of identity meanings for self and other during interactions, elucidates how people infer and adjust meanings through social experience, and shows how stable patterns of interaction can emerge from individuals’ uncertain perceptions of identities. Using simulations, we illustrate how this generalization offers a resolution to several issues of theoretical significance within sociology and social psychology by balancing cultural consensus with individual deviations from shared meanings, balancing meaning verification with the learning processes reflective of change, and accounting for noise in communicating identity. We also show how the model speaks to debates about core features of the self, which can be understood as stable and yet malleable, coherent and yet composed of multiple identities that may carry competing meanings. We discuss applications of the model in different areas of sociology, implications for understanding identity and social interaction, as well as the theoretical grounding of computational models of social behavior.
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