Simulated artist management with iterative decision-making: a qualitative approach to studying managerial judgment

Platformization has shifted music-industry gatekeeping from label A&R departments to streaming algorithms and curation, weakened labels' artist-development function, and made managers increasingly central to emerging artists' professionalisation. However, how managers form their judgments, what criteria they use, and when they change their minds are not well known; existing research only relies on retrospective case studies and cannot observe the criteria and iterations of decision-making at the time. This paper puts forward a qualitative method called simulated artist management with iterative decision-making; the researcher acts as a simulated manager and, at each decision node, records the evaluation criteria and their sources, the alternatives compared and the reasons for revision. With AI proliferation in mind, this paper takes the fictional cross-border artist Lina Yue as the research subject and explores the judgment logic at four stages: training and debut, identity positioning, debut single, and contract structure. A multi-case matrix of three styles is also used to test transferability. The demonstration shows that the evaluation criteria can be set and tracked and the judgment chain follows the node order; the structural criteria (rights ownership, debut-single function) stay the same, but high AI saturation raises the demand for live performance and authenticity. The function of a manager is to manage risk by selecting criteria, not to forecast outcomes; artistic agency and catalog depth predict a long career beyond the reach of marketing resources alone, and retained rights are a long-term asset option, with value tied to publishing income.