Product Price Optimization in Microeconomics: Exploring the Role of Artificial Intelligence Algorithms – A Literature Review
Abstract
In today’s data-driven economy, pricing strategies have become increasingly critical amid rapidly evolving market conditions. The integration of artificial intelligence (AI) offers new opportunities to optimize pricing decisions and strengthen competitive advantage. This study investigates the use of AI algorithms in optimizing product pricing within microeconomic contexts. Using a qualitative method and systematic literature review, it draws on publications from the past decade indexed in Scopus, DOAJ, and Google Scholar. The findings highlight that AI-based price optimization is shaped by several key factors: data availability, algorithm complexity, and the alignment of AI systems with existing business models. However, major challenges such as data bias, limited computational resources, and insufficient organizational readiness often hinder successful implementation. Despite these barriers, AI shows great promise in enhancing pricing accuracy, efficiency, and adaptability to market fluctuations. This research offers a comprehensive overview of the limitations and potential of AI in price optimization, emphasizing the importance of addressing technical and organizational challenges. It contributes to a deeper understanding of how AI can transform traditional pricing strategies and encourages further empirical research to explore its real-world applications within dynamic microeconomic settings.
Keywords - Artificial Intelligence, Microeconomics, Pricing Algorithms, Price Optimization.
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C. Dirican, “The Impacts of Robotics, Artificial Intelligence On Business and Economics,” Procedia - Soc. Behav. Sci., vol. 195, pp. 564–573, 2015, doi: 10.1016/j.sbspro.2015.06.134.
S. L. Wamba-Taguimdje, S. F. Wamba, J. R. K. Kamdjoug, and C. E. T. Wanko, “Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects,” Bus. Process Manag. J., vol. 26, no. 7, pp. 1893–1924, 2020, doi: 10.1108/BPMJ-10-2019-0411.
E. Brynjolfsson, D. Rock, and C. Syverson, “Artificial Intelligence and the Modern Productivity Paradox,” in The Economics of Artificial Intelligence, 2019, pp. 23–60. doi: 10.7208/chicago/9780226613475.003.0001.
M. Chen and Z. L. Chen, “Recent developments in dynamic pricing research: Multiple products, competition, and limited demand information,” Prod. Oper. Manag., vol. 24, no. 5, pp. 704–731, 2015, doi: 10.1111/poms.12295.
H. Chung and K. Shin, “Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction,” Neural Comput. Appl., vol. 32, no. 12, pp. 7897–7914, 2020, doi: 10.1007/s00521-019-04236-3.
R. Gupta and C. Pathak, “A machine learning framework for predicting purchase by online customers based on dynamic pricing,” in Procedia Computer Science, 2014, pp. 599–605. doi: 10.1016/j.procs.2014.09.060.
S. Sengupta et al., “A review of deep learning with special emphasis on architectures, applications and recent trends,” Knowledge-Based Syst., vol. 194, 2020, doi: 10.1016/j.knosys.2020.105596.
Y. Subbarayudu, G. V. Reddy, M. V. K. Raj, K. Uday, M. D. Fasiuddin, and P. Vishal, “An efficient novel approach to E-commerce retail price optimization through machine learning,” in E3S Web of Conferences, 2023. doi: 10.1051/e3sconf/202339101104.
N. Kockmann, T. Schindler, and L. Urbas, “AI in Process Industries – Incubator Labs and Use Cases,” Chemie-Ingenieur-Technik, vol. 95, no. 7. p. 963, 2023. doi: 10.1002/cite.202370702.
Z. Zhao, “The application of AI marketing in enterprise management analysis,” BCP Bus. Manag., vol. 34, pp. 548–553, 2022, doi: 10.54691/bcpbm.v34i.3063.
M. Nagahisarchoghaei et al., “An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives,” Electron., vol. 12, no. 5, 2023, doi: 10.3390/electronics12051092.
R. Damasevicius, “Artificial Intelligence Techniques in Economic Analysis,” Econ. Anal. Lett., 2023, doi: 10.58567/eal02020007.
Y. Aruka, Y. Nakajima, and N. Mori, “An examination of market mechanism with redundancies motivated by Turing’s rule selection,” Evol. Institutional Econ. Rev., vol. 16, no. 1, pp. 19–42, 2019, doi: 10.1007/s40844-018-0115-8.
T. Gramespacher and J. A. Posth, “Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio,” Front. Artif. Intell., vol. 4, 2021, doi: 10.3389/frai.2021.693022.
A. Gautier, A. Ittoo, and P. Van Cleynenbreugel, “AI algorithms, price discrimination and collusion: a technological, economic and legal perspective,” Eur. J. Law Econ., vol. 50, no. 3, pp. 405–435, 2020, doi: 10.1007/s10657-020-09662-6.
R. Amirzadeh, A. Nazari, and D. Thiruvady, “Applying Artificial Intelligence in Cryptocurrency Markets: A Survey,” Algorithms, vol. 15, no. 11, 2022, doi: 10.3390/a15110428.
B. Oancea, “Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics,” Mathematics, vol. 11, no. 7, 2023, doi: 10.3390/math11071588.
M. Jin, R. Tang, Y. Ji, F. Liu, L. Gao, and D. Huisingh, “Impact of advanced manufacturing on sustainability: An overview of the special volume on advanced manufacturing for sustainability and low fossil carbon emissions,” J. Clean. Prod., vol. 161, pp. 69–74, 2017, doi: 10.1016/j.jclepro.2017.05.101.
J. Gerlick and S. M. Liozu, “A Conceptual Framework of Ethical Considerations and Legal Constraints in the Algorithm-Driven Pricing Function,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3454123.
Syaharuddin, Fatmawati, and H. Suprajitno, “Accuracy rate of ANN back propagation architecture with modified algorithm: A meta-analysis,” in AIP Conference Proceedings, 2023. doi: 10.1063/5.0137185.
J. N. Chukwunweike, A. N. Anang, A. A. Adeniran, and J. Dike, “Enhancing manufacturing efficiency and quality through automation and deep learning : addressing redundancy , defects , vibration analysis , and material strength optimization,” World J. Adv. Res. Rev., vol. 23, no. 3, pp. 1272–1295, 2024, doi: https://doi.org/10.30574/wjarr.2024.23.3.2800.
H. B. Rao, N. B. Sastry, R. P. Venu, and P. Pattanayak, “The role of artificial intelligence based systems for cost optimization in colorectal cancer prevention programs,” Frontiers in Artificial Intelligence, vol. 5. 2022. doi: 10.3389/frai.2022.955399.
T. F. Morris et al., “Strengths and limitations of Nitrogen rate recommendations for corn and opportunities for improvement,” Agronomy Journal, vol. 110, no. 1. pp. 1–37, 2018. doi: 10.2134/agronj2017.02.0112.
F. Sabry, W. Labda, A. Erbad, and Q. Malluhi, “Cryptocurrencies and artificial intelligence: Challenges and opportunities,” IEEE Access, vol. 8, pp. 175840–175858, 2020, doi: 10.1109/ACCESS.2020.3025211.
M. Kozlova, D. Kozhemyakin, O. Sergacheva, and A. Bortenev, “The influence of digital platforms and algorithms on legal regulation of competition,” SHS Web Conf., vol. 109, p. 01020, 2021, doi: 10.1051/shsconf/202110901020.
Syaharuddin, D. Pramita, T. Nusantara, Subanji, and H. R. P. Negara, “Analysis of accuracy parameters of ANN backpropagation algorithm through training and testing of hydro-climatology data based on GUI MATLAB,” in IOP Conference Series: Earth and Environmental Science, 2020. doi: 10.1088/1755-1315/413/1/012008.
F. Schmiegelow and F. C. L. Melo, “A market research on challenges influencing artificial intelligence adoption,” Bus. Theory Pract., vol. 24, no. 1, pp. 250–257, 2023, doi: 10.3846/btp.2023.17655.
M. Owczarczuk, “Ethical and regulatory challenges amid artificial intelligence development: an outline of the issue,” Ekon. i Prawo, vol. 22, no. 2, pp. 295–310, 2023, doi: 10.12775/eip.2023.017.
Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage., vol. 57, 2021, doi: 10.1016/j.ijinfomgt.2019.08.002.
A. Holzinger, A. Saranti, C. Molnar, P. Biecek, and W. Samek, “Explainable AI Methods - A Brief Overview,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, pp. 13–38. doi: 10.1007/978-3-031-04083-2_2.
H. Roberts, J. Cowls, J. Morley, M. Taddeo, V. Wang, and L. Floridi, “The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation,” AI Soc., vol. 36, no. 1, pp. 59–77, 2021, doi: 10.1007/s00146-020-00992-2.
DOI: http://dx.doi.org/10.36722/sst.v10i2.3770
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