Managing academic performance by optimal resource allocation

Abstract In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, (iii) renewable allocation maximizing the total output of research unit. Suggested guarantees optimal under strong economic assumptions: agents are rational, collaborative have no incentives to behave selfishly. reality, however, often play strategically their own utilities, e.g., resources assigned them. This strategic behavior is typically mitigated by implementation performance-driven or uniform schemes. Next presentation, address cost such mitigation.