# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
###### Abstract  
Diversity and inclusion (D&I) is a topic of increasing relevance across virtually all sectors of our society, with the potential for a significant impact on corporations and more broadly on our economy and society. While people are typically the most valuable asset of every organization, human resources (HR) in general, and D&I in particular, are dominated by qualitative approaches. This paper introduces an agent-based simulation that can quantify the impact of certain aspects of D&I on corporate performance. The simulation provides a parsimonious and compelling explanation of the impact of hiring and promotion biases on the resulting corporate gender balance, accurately replicating real-world data about gender imbalances across multiple industry sectors. In addition, the paper shows that the simulation can be used to predict the likely impact of different D&I interventions. Specifically, once a company has become imbalanced, even removing all promotion biases is not sufficient to rectify the situation, and it can take decades to undo the imbalances initially created by these biases. These and other results demonstrate that agent-based simulation is a powerful approach for managing D&I in corporate settings and could become an invaluable tool for the strategic and tactical management of human resources. diversity and inclusion; gender; agent-based simulation; agent-based modeling; applied complexity science; corporate management; people analytics 2023  
Chip  
The complexity that so challenges workforce analytics, we believe, is also the reason why certain D&I initiatives that seem to work within one organization often fail to produce results--or even backfire--at other organizations; each organization is a unique \"ecosystem\" whose macroscopic behavior emerges from the complex web of interactions among its staff, leadership, customers, suppliers and partners. Borrowing a D&I initiative from another company and hoping that it will have a positive impact on our company is analogous to copying a specific advertisement that was successful for another company in a different sector and expecting it will sell our products. When you also consider that the impact of personnel initiatives can take months or years to be observed, and that missteps can be extremely costly, it is no wonder that leaders are reluctant to take decisive action. What is needed is a quantitative approach that can capture and analyze the behavior of complex systems. We have successfully begun using agent-based simulation [13], one of the primary tools of Complexity Science [14], to replicate the complex behaviors of people within organizations. In this paper, we show the results of applying an agent-based simulation to a particular aspect of corporate gender imbalance: by simulating the hiring and career advancement of employees at typical companies, we can analyze the impact of introducing gender biases. Under reasonable assumptions, we find that gender biases in promotions can yield the kinds of gender imbalances that are typical of many companies, with a decreased representation of women at higher corporate ranks. We also find that by adjusting gender biases in hiring as well as promotions, it is possible to simulate gender imbalances that match the real-world patterns observed in different industries. Agent-based simulations capture the causal relationships that link individual behaviors and interactions to the resulting system-level behaviors [15]. As a result, once an agent-based simulation has been shown to capture a real-world phenomenon with some fidelity, it can be used to predict the likely impact of different initiatives, even novel initiatives for which no data are available. Here, we use our simulation to test the likely impact of removing all promotion biases in a company that is already gender-imbalanced. Under a variety of scenarios, we find that simply removing biases is not a very effective strategy, as it can take much longer to eliminate the gender biases than it took to establish them in the first place.