Self-disclosure refers to the process of revealing information about oneself to another and has been shown to have influence on individual well-being and interpersonal relationships. Most research on self-disclosure behavior focuses on intrapersonal processes and dyadic interactions without actively examining large-scale patterns that emerge from these small-scale events. Our study aims to fill this gap by examining the social dynamics of self-disclosure using agent-based modeling (ABM). ABM is a computational modeling approach in which rules for simulated agents’ actions and interactions are defined and then examined for their connection to the overall social system. Based on relevant theoretical assumptions and empirical findings on self-disclosure from the literature, we developed an ABM, which we then analyzed using social network analysis.

Our research reveals how systematic, dynamic, large-scale patterns and self-stabilizing social networks can emerge from simple decision-making and self-disclosure strategies. On this basis, we provide insights into how, within these networks, different agents can either end up in upward spirals towards intimate, self-disclosure-friendly relationships or in downward spirals towards isolation and concealment. We conclude that interventions seeking to improve individual well-being and interpersonal relationships through self-disclosure benefit from considering and targeting larger elements of the surrounding social network.

We discuss the methodological opportunities and potential pitfalls associated with utilizing ABMs as a theoretical tool. We explore their capability to understand the interaction of individual behavior and social dynamics, and how psychological processes associated with self-disclosure can better understood through their embeddedness in a larger social network.