Managing academic performance by optimal resource allocation

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, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

Automated summary

This paper presents a complex data-driven framework for human resource management that focuses on academic talent recognition, researcher performance measurement, and renewable resource allocation to maximize research output. While the suggested resource allocation guarantees optimal output under strong economic assumptions, it acknowledges that agents often engage in strategic behavior to maximize their own utilities in reality. Strategic behaviors are typically mitigated through performance-driven or uniform resource allocation schemes, with the paper also addressing the cost of such mitigation strategies.