In this project, we use agent-based computational simulation models to investigate the role of social network mechanisms in bringing about various social inequalities, addressing questions such as why some songs become immensely popular while most receive minimal attention, why certain individuals accumulate significantly more wealth than others, and why different social groups exhibit varying vaccination rates. Although it is widely accepted that social networks contribute to increasing inequality, the specific mechanisms involved remain underexplored. One network feature that has garnered increasing attention in the network science literature is multiplexity—the notion that individuals are embedded in multiple social contexts simultaneously. However, the impact of multiplexity on social inequality is not well understood. This project seeks to fill this gap. Building on seminal theoretical work by scholars like Peter Blau and Scott Feld, and based on large-scale empirical evidence from Sweden, we will use agent-based computational models to examine how the ways in which social contexts intersect and create higher-order network structures affect exposure and diffusion dynamics, leading to social inequalities. The complexity of the simulation model and the extensive parameter space necessitate considerable computation time, motiving this application.