An agent-based approach for modeling and simulation of human resource management as a complex system: Management strategy evaluation

Abstract

Human resources management (HRM) is the strategic approach of managing people in organizations. HRM can be naturally represented as a complex system of autonomous human agents, whose interactions are influenced by mutually reinforcing HRM strategies, policies, and practices. Stochastic and dynamic human interactions could lead to emergent phenomena of organizational performance in the long-term. Therefore, decision-making in the HRM complex system could be challenging for the human mind due to its limited capacity to map multiple relationships, execute stochastic and dynamic interactions, consider trade-offs arising from competing objectives, integrate intermediate information generated by feedback loops, and visualize the impact on multiple performance metrics. Existing HRM decision-making models rely on analytical methods, survey-based statistical approaches, and aggregate rate-based System Dynamics simulation, assuming homogeneous populations, which leads to limitations in capturing dynamics of heterogeneous populations arising from micro-level interactions. Therefore, we propose an agent-based methodology to model HRM as a complex system, in which alternate management strategies could be simulated by way of agent interactions while observing the emergence of organizational performance. The model has been simulated to observe the long-term influences of alternate recruitment strategies on workforce fluctuations, demographics, skill profile, loyalty, and costs under distinct employee turnover rates. We propose that an agent-based methodology is more realistic in modeling micro-level employee interactions of an HRM system and possibly the ideal avenue to rehearse management strategy when experimentation on the real system is constrained. Fundamentally, the proposed model serves as a decision support system for HRM decision-making.