Modeling dynamic identities and uncertainty in social interaction

  • 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 ofDrawing 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.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Tobias SchröderORCiDGND, Jesse HoeyORCiD, Kimberly B. Rogers
DOI:https://doi.org/10.1177/0003122416650963
ISSN:0003-1224
Titel des übergeordneten Werkes (Englisch):American Sociological Review
Untertitel (Englisch):Bayesian affect control theory
Verlag:Sage
Verlagsort:Thousand Oaks
Dokumentart:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Veröffentlichung (online):22.05.2017
Jahr der Erstveröffentlichung:2016
Datum der Freischaltung:09.06.2017
GND-Schlagwort:Sozialwesen; Zusammenarbeit; Bayes-Verfahren; Soziologie; Psychologie
Jahrgang:81
Ausgabe / Heft:4
Erste Seite:828
Letzte Seite:855
Fachbereiche und Zentrale Einrichtungen:Forschungs- und An-Institute / Inst. für angewandte Forschung Urbane Zukunft (IaF)
DDC-Klassifikation:300 Sozialwissenschaften
Lizenz (Deutsch):License LogoCC - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen
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