The Role of Reinforcement Learning in Optimizing Managerial Decision-Making
Keywords:
Reinforcement learning, managerial decision-making, artificial intelligence, optimization, thematic analysis, organizational learningAbstract
This study aimed to systematically review the literature to identify and explain the role of reinforcement learning (RL) in optimizing managerial decision-making processes across organizational contexts. This research adopted a qualitative systematic review design based on thematic content analysis. Data were collected exclusively from scientific databases including Scopus, Web of Science, IEEE Xplore, and Google Scholar. After applying inclusion and exclusion criteria, 15 peer-reviewed articles were selected. Textual data were analyzed using NVivo 14 software through open, axial, and selective coding. Themes were inductively derived until theoretical saturation was achieved, resulting in a comprehensive conceptual framework describing the interaction between RL mechanisms and managerial decision-making. The analysis revealed that reinforcement learning significantly enhances the efficiency, adaptability, and accuracy of managerial decisions by enabling systems to learn through rewards, penalties, and continuous feedback. Three major themes emerged: (1) RL as a tool for decision optimization, (2) integration of RL into intelligent management systems, and (3) managerial outcomes of RL adoption. RL-based decision support systems improved resource allocation, reduced cognitive bias, enhanced organizational agility, and supported data-driven governance. Reinforcement learning provides a transformative paradigm for intelligent and adaptive managerial decision-making. It enables the transition from intuition-driven to evidence-based and self-learning management systems. Effective implementation requires robust data infrastructures, ethical transparency, and a culture of organizational learning to ensure sustainable human–AI collaboration in managerial environments.
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References
Buşoniu, L., Babuška, R., De Schutter, B., & Ernst, D. (2010). Reinforcement learning and dynamic programming using function approximators. CRC Press.
Chen, X., Huang, S., & Li, Y. (2022). Reinforcement learning-based human resource allocation for improving organizational performance. Expert Systems with Applications, 198, 116888.
Dehghani, M., Khosravi, A., & Nahavandi, S. (2022). Reinforcement learning in supply chain management: A systematic review. Computers & Industrial Engineering, 167, 107981.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 57, 102297.
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3–4), 219–354.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.
Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2018). Optimal and autonomous control using reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2042–2062.
Lai, Y., Li, H., & Chen, J. (2022). Reinforcement learning for adaptive supply chain optimization under pandemic disruption. Omega, 112, 102720.
Li, Y. (2021). Deep reinforcement learning: An overview. Neural Networks, 131, 219–239.
Li, Z., & Shi, Y. (2023). Ethical transparency and interpretability in reinforcement learning-based management systems. Journal of Business Research, 172, 114203.
March, J. G., & Simon, H. A. (1958). Organizations. Wiley.
Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
Nguyen, T., Nguyen, N., & Pham, H. (2020). Reinforcement learning for dynamic decision-making in intelligent management systems. Information Sciences, 512, 682–702.
Rahman, M. S., Alam, M. J., & Ahmed, R. (2022). Explainability in reinforcement learning-based business decisions. Decision Support Systems, 157, 113762.
Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20, 5–14.
Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Simon, H. A. (1977). The new science of management decision. Prentice Hall.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
Vinyals, O., Babuschkin, I., Czarnecki, W. M., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354.
Wang, Y., Zhang, X., & Zhou, D. (2023). Intelligent management decision-making via hybrid deep reinforcement learning. Computers in Industry, 150, 103997.
Yang, H., & Wang, J. (2022). Reinforcement learning in organizational management: Emerging trends and challenges. Journal of Management Analytics, 9(4), 513–531.
Zhang, T., Liu, W., & Xu, X. (2023). Organizational learning and reinforcement learning integration: Toward adaptive managerial decision-making. Technological Forecasting and Social Change, 187, 122259.
Zheng, Y., Chen, R., & Xu, D. (2022). Reinforcement learning in operations management: Recent advances and applications. European Journal of Operational Research, 299(3), 1189–1204.
