Modelling social norms: an integration of the norm-utility approach with beliefs dynamics.

behaviour beliefs cultural evolution game theory mathematical modelling social evolution

Journal

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
ISSN: 1471-2970
Titre abrégé: Philos Trans R Soc Lond B Biol Sci
Pays: England
ID NLM: 7503623

Informations de publication

Date de publication:
11 Mar 2024
Historique:
medline: 21 1 2024
pubmed: 21 1 2024
entrez: 20 1 2024
Statut: ppublish

Résumé

We review theoretical approaches for modelling the origin, persistence and change of social norms. The most comprehensive models describe the coevolution of behaviours, personal, descriptive and injunctive norms while considering influences of various authorities and accounting for cognitive processes and between-individual differences. Models show that social norms can improve individual and group well-being. Under some conditions though, deleterious norms can persist in the population through conformity, preference falsification and pluralistic ignorance. Polarization in behaviour and beliefs can be maintained, even when societal advantages of particular behaviours or belief systems over alternatives are clear. Attempts to change social norms can backfire through cognitive processes including cognitive dissonance and psychological reactance. Under some conditions social norms can change rapidly via tipping point dynamics. Norms can be highly susceptible to manipulation, and network structure influences their propagation. Future models should incorporate network structure more thoroughly, explicitly study online norms, consider cultural variations and be applied to real-world processes. This article is part of the theme issue 'Social norm change: drivers and consequences'.

Identifiants

pubmed: 38244599
doi: 10.1098/rstb.2023.0027
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

20230027

Auteurs

Sergey Gavrilets (S)

Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996, USA.
Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA.
Center for the Dynamics of Social Complexity, University of Tennessee, Knoxville, TN 37996, USA.

Denis Tverskoi (D)

Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA.
Center for the Dynamics of Social Complexity, University of Tennessee, Knoxville, TN 37996, USA.

Angel Sánchez (A)

Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matemáticas Universidad Carlos III de Madrid, Leganés, Madrid 28911, Spain.
Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza, Zaragoza 50018, Spain.

Classifications MeSH