Evolutionary modeling reveals that value-oriented knowledge creation behaviors reinvent jobs

Recently, the strong artificial intelligence-based augmented capability enables the autonomous completion of traditional jobs devoid of human intervention, impacting labor markets. However, the underlying mechanisms have not been explored enough in prior research. In this research, we propose a computational model, focusing on the interplay between world knowledge networks and organizational knowledge sets, along with external labor market conditions. This model incorporates dynamic knowledge creation behaviors and is validated using a substantial dataset from a leading online recruitment platform in China, featuring over 20 million job postings and 1 million skill-related keywords. The results demonstrate that swift knowledge search and emulating knowledge within existing jobs are the main methods in the early developmental stage of organizations, accounting for about 75% of all simulation samples and forming the initial job evolution. As organizations progress, although fine-tuning the knowledge within existing jobs still remains significant, the intensity of knowledge search declines significantly, and the intensity of knowledge reuse surpasses that of knowledge search, reaching ~1.5 times its intensity during the stable phase. We also perform several parameter experiments and a case study to illustrate how jobs evolve in the labor market with different characteristics. The robustness tests demonstrate the model’s resilience across different simulation environments and organization strategies. Our study underlies the mechanisms of job evolution from the organizational level and provides empirical evidence and insights into the job evolution dynamics within knowledge networks.