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      Agent-based models of the cultural evolution of occupational gender roles

      research-article
      1 , 2 , 3
      Royal Society Open Science
      The Royal Society
      gender, gender roles, segregation, stereotypes, models, social learning

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          Abstract

          The causes of sex differences in human behaviour are contested, with ‘evolutionary’ and ‘social’ explanations often being pitted against each other in the literature. Recent work showing positive correlations between indices of gender equality and the size of sex differences in behaviour has been argued to show support for ‘evolutionary’ over ‘social’ approaches. This argument, however, neglects the potential for social learning to generate arbitrary gender segregation. In the current paper we simulate, using agent-based models, a population where agents exist as one of two ‘types’ and can use social information about which types of agents are performing which ‘roles’ within their environment. We find that agents self-segregate into different roles even where real differences in performance do not exist, if there is a common belief (modelled as priors) that group differences may exist in ‘innate’ competence. Facilitating role changes such that agents should move without cost to the predicted highest-rewards for their skills (i.e. fluidity of the labour market) reduced segregation, while forcing extended exploration of different roles eradicated gender segregation. These models are interpreted in terms of bio-cultural evolution, and the impact of social learning on the expression of gender roles.

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            Cognitive culture: theoretical and empirical insights into social learning strategies.

            Research into social learning (learning from others) has expanded significantly in recent years, not least because of productive interactions between theoretical and empirical approaches. This has been coupled with a new emphasis on learning strategies, which places social learning within a cognitive decision-making framework. Understanding when, how and why individuals learn from others is a significant challenge, but one that is critical to numerous fields in multiple academic disciplines, including the study of social cognition. Copyright © 2010 Elsevier Ltd. All rights reserved.
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              The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education

              The underrepresentation of girls and women in science, technology, engineering, and mathematics (STEM) fields is a continual concern for social scientists and policymakers. Using an international database on adolescent achievement in science, mathematics, and reading ( N = 472,242), we showed that girls performed similarly to or better than boys in science in two of every three countries, and in nearly all countries, more girls appeared capable of college-level STEM study than had enrolled. Paradoxically, the sex differences in the magnitude of relative academic strengths and pursuit of STEM degrees rose with increases in national gender equality. The gap between boys' science achievement and girls' reading achievement relative to their mean academic performance was near universal. These sex differences in academic strengths and attitudes toward science correlated with the STEM graduation gap. A mediation analysis suggested that life-quality pressures in less gender-equal countries promote girls' and women's engagement with STEM subjects.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Software
                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                June 28, 2023
                June 2023
                June 28, 2023
                : 10
                : 6
                : 221346
                Affiliations
                [ 1 ] School of Psychology & Neuroscience, University of St Andrews, St Mary's College, , St Andrew, Fife KY16 9AZ, UK
                [ 2 ] Department of Psychology, Durham University, , South Road, Durham DH1 3LE, UK
                [ 3 ] School of Computer Science, University of St Andrews, , Jack Cole Building, North Haugh, St Andrews, Fife KY16 9SX, UK
                Author notes
                [ † ]

                These authors made equal contributions.

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.6700187.

                Author information
                http://orcid.org/0000-0001-8110-8408
                http://orcid.org/0000-0001-6660-5828
                http://orcid.org/0000-0003-2979-5989
                Article
                rsos221346
                10.1098/rsos.221346
                10300665
                37388313
                5e5ba3be-72c0-45e8-a6e5-5b1c6383b139
                © 2023 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : October 19, 2022
                : June 1, 2023
                Categories
                1001
                205
                70
                Psychology and Cognitive Neuroscience
                Research Articles

                gender,gender roles,segregation,stereotypes,models,social learning

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