EMERGENT BEHAVIOR OF THE US GOVERNMENT WORKFORCE: AN AGENT-BASED MODEL OF WORKER DEPARTURE Open Access
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Over the past few decades, the public sector has seen significant change in the political and economic environment due to greater interdependencies between agencies and the private sector, globalization, and rapid technological change. The resulting uncertainty introduces a number of challenges such as demands for greater efficiency and effectiveness, and a need to move towards more entrepreneurial practices. Using the interdisciplinary approach of computational organization theory, organizational theories of worker satisfaction and turnover were evaluated through the use of agent-based modeling and validated with thirteen years of US government employment data. In contrast to a traditional system engineering approach of decomposition, the complex adaptive system approach focuses on the emergent behavior of individual agents self-organizing and adapting. The model demonstrates an evolutionary process of origination, diffusion, and retention. Initially, the workforce population is unable to sustain small groups resulting in high attrition rates across all population segments. As the average group size increases the retirement eligible segment begins to balance with the onboarding of entry-level workers creating stability. The model phenomenon confirms that groups of a critical size improves employee satisfaction and lowers employee turnover.Exhibiting the characteristics of a complex adaptive system, the emergent behavior of group formation and the balancing of minimally satisfied and highly satisfied workers reveals the impact of worker attributes and workforce composition. The incompatibility of small group sizes with the demographics of the US government workforce establishes the importance of understanding workforce composition and organizational design.