Modeling QS Ranking Indicator Formation Processes for Strategic Management of Higher Education Institutions
Abstract. This paper presents an approach to the strategic management of higher education institutions (HEIs) through agent-based modeling of QS World University Rankings (QS-WUR) indicator formation processes, considering limited resources and high competition in the international arena. Improving a university’s position in the ranking requires effective management decisions. The authors analyze contemporary scientific approaches to resource allocation in multi-agent systems and propose a simulation model that accounts for dynamic interactions between participants in the educational process, external factors, and institutions. The agent-based approach enables modeling the behavior of individual system participants, considering their actions and their impact on ranking indicators. The proposed model aims to forecast the consequences of managerial decisions and support strategic planning in HEIs. Future work includes validation using retrospective data from Ukrainian universities and the development of a recommendation system to assist HEI leadership in effective resource allocation for improving international ranking positions.
In the current context of globalization and continuous transformation of the educational landscape, HEIs face increasing challenges in enhancing their global competitiveness. One of the key factors determining the international reputation of HEIs is their position in the authoritative QS World University Rankings (QS-WUR). Improving a university’s QS-WUR position demands an effective approach to managing internal processes, resources, and the development of core activity areas. At the same time, universities operate under resource constraints, prompting the need to model development strategies focused on improving key ranking indicators.
Researchers are actively exploring resource allocation in complex systems, particularly within multi-agent and computational environments. In [1], a non-homogeneous Markov chain model is proposed for the dynamic allocation of heterogeneous resources in complex systems, based on a modified AIMD algorithm. The authors of [2] address resource management in dynamic multi-agent systems using the MG-RAO algorithm, which incorporates reinforcement learning elements. Study [3] introduces an online optimization approach for resource allocation in open multi-agent systems, accounting for dynamic agent population changes. Work [4] discusses a decentralized constraint-solving method for multicore systems based on an agent-oriented model, showing improved computational efficiency. In [5], agent-based modeling is applied to analyze resource distribution in emergency departments, helping to identify bottlenecks and improve system performance. These approaches confirm the effectiveness of agent-based modeling and optimization methods for supporting decision-making in systems with limited resources and high dynamics. The study in [6] proposed a model for the formation of QS-WUR indicators, enabling the analysis of ranking formation mechanisms and the identification of dependencies between internal HEI metrics and their QS-WUR positions.
Currently, an agent-based model is being developed to simulate QS-WUR indicator formation processes, considering interactions among educational stakeholders and external factors such as international reputation, scientific output, academic mobility, and international student recruitment—all of which influence university ranking changes. The agent-based approach allows consideration of individual characteristics, behavioral strategies, and dynamic changes of each agent over time.
The model simulates a complex, multi-level system in which each agent’s decisions contribute to key indicators reflected in QS-WUR. It enables the analysis of long-term impacts of managerial decisions in a changing environment and supports the identification of the most effective ways to improve institutional metrics under resource constraints. The proposed model can serve as a decision-support tool for HEI leadership in strategic university management aimed at enhancing ranking positions.
Future research will involve experiments based on retrospective data from Ukrainian universities, which will be used to develop a recommendation system for resource allocation [7]. This will provide strategic decision-making support for HEI development, targeting higher positions in international rankings.
Література
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