Agent-Based Models of Gender Inequalities in Career Progression
Authored by John Bullinaria
Date Published: 2018
DOI: 10.18564/jasss.3738
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Abstract
An agent-based simulation framework is presented that provides a
principled approach for investigating gender inequalities in
professional hierarchies such as universities or businesses. Populations
of artificial agents compete for promotion in their chosen professions,
leading to emergent distributions that can be matched to real-life
scenarios, and allowing the influence of socially or genetically
acquired career preferences to be explored. The aim is that such models
will enable better understanding of how imbalances emerge and evolve,
facilitate the identification of specific signals that can indicate the
presence or absence of discrimination, and provide a tool for
determining how and when particular intervention strategies may be
appropriate for rectifying any inequalities. Results generated from a
representative series of abstract case studies involving innate or
culturally-acquired gender-based ability differences, gender-based
discrimination, and various forms of gender-specific career preferences,
demonstrate the power of the approach. These simulations will hopefully
inspire and facilitate better approaches for dealing with these issues
in real life.
Tags
Agent-based models
Evolution
Management
Performance
stereotypes
discrimination
Science
Women
Self-efficacy
Sex-differences
Life-history evolution
Preference
Gender inequalities
Career preferences
Social
learning
Computer-science
Leaky
pipeline
Mathematics
Leaky pipeline