Fig. 1: Modeling job evolution from a knowledge creation perspective.

a Organizations in the labor market disseminate job postings. Each job posting entails several knowledge requirements, and the collective knowledge needed for all positions forms a small-world network. The processes of job posting and knowledge acquisition for organizations are dynamically evolving. b Main steps of our research: STEP 1, Building simulation environment: global knowledge network with small-world traits, intra-organizational knowledge network, and job-specific knowledge subsets. Agent Action Space involves knowledge search, reuse, job transfer, and knowledge permutation. STEP 2, Designing agent dynamics: agent actions have unique costs, and jobs generate diverse values. Agent dynamics aims to maximize individual profit, defined as the difference between accrued values and costs. STEP 3, Doing a statistical analysis of authentic data: we consider enterprises as quasi-agents, while each job description represents a job. Skill-related keywords are extracted from these job descriptions and endowed with the role of knowledge nodes. STEP 4, Validating simulation effectiveness: comparative analysis with real-world data confirms simulation efficacy and conclusion accuracy for our model.