Organizational and psychological science have historically relied on experimental and correlational research. Computational modeling and simulation have emerged as a “third” way of doing science, though the use of modeling is inconsistent across subfields of psychology. Organizational psychology, the psychological study of individuals, groups, and work organizations, is a subdiscipline of psychology that has seen limited use of computational modeling. This dissertation constructs a series of agent-based models applied to topics in the organization sciences and psychology, motivated by research challenges encountered as an applied researcher in the organization sciences. Three models – a spatial ABM, a network ABM, and a population ABM – explore manager-subordinate proximity, formal organization hierarchy and informal networks and IQ-environment feedback loops, respectively. Model 1, “A Spatial Agent-Based Model of Manager-Subordinate Proximity” uses ABM to explore the extent to which a spatial model can quantify the spontaneous encounters between managers and subordinates based on their relative locations in a multi-floor office setting. Results demonstrate that subordinates located on a different floor than their manager are substantially less likely to have even a single spontaneous encounter with their manager in a workday, despite relatively short geographic separation. Imposing top-down requirements to travel between floors may do surprisingly little to abate this problem. The model suggests several implications for how managers and subordinates are co-located, how certain co-locations can result in fewer unplanned encounters between managers and subordinates, and the effect of closed office versus open office seating on serendipitous contact frequency with team members. Potential extensions and applications germane to post-COVID19 return to office (RTO) efforts as well as possible uses for future infectious disease issues in work settings are discussed. Model 2, “A Network Agent-Based Model of Formal Organization and Informal Networks” uses agent-based modeling to explore the extent to which informal networks affect organizational performance when added to a formal organization hierarchy, using an information-seeking task as part of a work process. Results indicate that the addition of informal network ties to a formal organization can improve overall organization performance, but there is a point of diminishing returns beyond which the presumed cost of additional informal ties and maintenance of those ties may exceed the benefit. Model 3, “A Population Agent-Based Model Exploring the Flynn Effect” uses a population agent-based model to explore aspects of the reciprocal, gene-environment correlation formal model proposed by William Dickens and James Flynn in 2001 to explain the phenomenon of rising IQ in the 20th century known as the Flynn Effect. While the model does not reproduce the full FE gains, emergence of segregation by IQ and assortative mating were observed, and the model demonstrates the value of computational approaches for studying process dynamics for phenomena that are otherwise generally limited to infrequent, point-in-time measurement or static snapshots, as well as the need for continued modeling and research in this area as the possible complex environmental causes of the FE remain elusive. The model also leverages a twin-based study design, operationalizing in silico the study of identical twin agents raised apart.