• Loso Judijanto IPOSS Jakarta, Indonesia
  • Bahrun Thalib Fakultas Ekonomi dan Bisnis, Universitas Khairun
  • Haryanto Universitas Dharma AUB Surakarta
  • Al-Amin Universitas Airlangga, Surabaya, Indonesia

Kata Kunci:

AI-Based Decision Making, Macroeconomics, Microeconomics, Optimal Efficiency.


In the last decade, Artificial Intelligence (AI) has moved from being a futuristic concept to a critical component of economic decision-making. The use of AI has been extended to various aspects of the economy, ranging from strategic decision-making at the firm level to macroeconomic policy at the government level. This study aims to examine the impact of AI on decision-making in macro and microeconomics, and understand how optimal efficiency can be achieved through the implementation of this technology. The study conducted in this research utilizes the literature research method. The results of this study show that AI has the potential to increase economic growth due to increased productivity and operational efficiency. At the macro level, AI contributes to more accurate policy planning and efficient resource management. At the micro level, AI supports businesses in gaining competitive advantage through supply chain optimization, personalization of service offerings, and better customer data management. However, the findings also emphasize the importance of addressing ethical, privacy, and accessibility challenges to ensure that the benefits of AI are widely and equitably enjoyed.


Abdussamad, Z. (2022). Buku Metode Penelitian Kualitatif. Query date: 2024-05-25 20:59:55.

Adlini, M. N., Dinda, A. H., Yulinda, S., Chotimah, O., & Merliyana, S. J. (2022). Metode Penelitian Kualitatif Studi Pustaka. Edumaspul: Jurnal Pendidikan, 6(1), 974–980.

Afiyanti, Y. (2008). Focus Group Discussion (Diskusi Kelompok Terfokus) sebagai Metode Pengumpulan Data Penelitian Kualitatif. Jurnal Keperawatan Indonesia, 12(1), 58–62.

Alexander, F. J., Reyes, K.-R., Varshney, L. R., & Yoon, B.-J. (2023). AI for Optimal Experimental Design and Decision-Making. Artificial Intelligence for Science, Query date: 2024-06-07 06:45:39, 609–625.

Alhelou, H. H., & Golshan, M. E. H. (2020). Decision-making-based optimal generation-side secondary-reserve scheduling and optimal LFC in deregulated interconnected power system. Decision Making Applications in Modern Power Systems, Query date: 2024-06-07 06:45:39, 269–299.

Alkadash, T. M., Allaymouni, M., Almuslemani, A. K., & Ebrahim, Y. S. (2023). Maximizing Organizational Efficiency Through HR Information Systems: A Focus on Decision-Making in Tech Firms. AI and Business, and Innovation Research: Understanding the Potential and Risks of AI for Modern Enterprises, Query date: 2024-06-07 06:45:39, 431–439.

Arend, R. J. (2024). Optimal Uncertainty (in Decision-Making). Uncertainty in Strategic Decision Making, Query date: 2024-06-07 06:45:39, 137–143.

Bellos, I. (2022). Multicriteria Decision-Making Methods for Optimal Treatment Selection in Network Meta-Analysis. Medical Decision Making, 43(1), 78–90.

Bentley, A., & Bradford, R. (2023). MSR94 Harnessing AI in Health Economics: Enhancing Efficiency, Accuracy, and Decision-Making. Value in Health, 26(12).

Berber, A. (2023). Automated decision-making and the problem of evil. AI & SOCIETY, Query date: 2024-06-07 06:45:39.

Braaten, D. (2022). Decision letter for ‘Predictive models for human–AI nexus in group decision making’. Query date: 2024-06-07 06:45:39.

Caro-Burnett, J., & Kaneko, S. (2022). Is Society Ready for AI Ethical Decision Making? Lessons from a Study on Autonomous Cars. Journal of Behavioral and Experimental Economics, 98(Query date: 2024-06-07 06:45:39), 101881–101881.

Chernega, O., Yakovenko, U., Chepurnova, A., & Makieieva, O. (2021). 1 Features of making managerial decisions in a crisis at the micro and macro levels. Econometric Modeling of Managerial Decisions at the Macro and Micro Levels, Query date: 2024-06-07 06:45:39, 3–21.

Chia, Y. F. (2021). The Economics of Sanction Regimes and Criminal Decision-Making. Economics in Practice, Query date: 2024-06-07 06:45:39, 217–237.

Cummings, M. L. (2024). A Taxonomy for AI Hazard Analysis. Journal of Cognitive Engineering and Decision Making, Query date: 2024-06-07 06:45:39.

Facchini, F., Digiesi, S., & Mossa, G. (2020). Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making. International Journal of Production Economics, 219(Query date: 2024-06-07 06:45:39), 164–178.

Farhadi, A. (2024). Awareness-based Choice Selection:Improving the Decision-making Efficiency by Using Known Information. Query date: 2024-06-07 06:45:39.

Gachot, C. (2020). Decision letter for ‘Friction characteristics of moving joint surface from the micro and macro scale’. Query date: 2024-06-07 06:45:39.

George, N., Kadan, A. B., & Vijayan, V. P. (2023). Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization. IAES International Journal of Artificial Intelligence (IJ-AI), 12(2), 678–678.

Ghanvatkar, S., & Rajan, V. (2024). Evaluating Explanations from AI Algorithms for Clinical Decision-Making: A Social Science-based Approach. Query date: 2024-06-07 06:45:39.

Guercini, S. (2023). Decision making based on heuristics in the marketing literature. Marketing Automation and Decision Making, Query date: 2024-06-07 06:45:39, 13–37.

Guo, Y., Huang, C., Sheng, Y., Zhang, W., Ye, X., Lian, H., Xu, J., & Chen, Y. (2023). Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology. Query date: 2024-06-07 06:45:39.

Hajisafi, A. (2023). Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles. Query date: 2024-06-07 06:45:39.

Hasannejadasl, H., Offermann, C., Essink, E., Dekker, A., Roumen, C., & Fijten, R. (2023). Patients’ Attitudes Towards the Use of AI-Based Decision Aids for Breast Cancer Treatment Decision-Making: A Qualitative Study. Query date: 2024-06-07 06:45:39.

Hsieh, F.-S. (2023). Improve Decision Making Efficiency in Ridesharing Systems through a Hybrid Firefly-PSO algorithm. 2023 IEEE World AI IoT Congress (AIIoT), Query date: 2024-06-07 06:45:39.

Huang, Y., & Wang, M. (2023). Heterogeneous Multi-Attribute Group Decision Making Based on a Fuzzy Data Envelopment Analysis Cross-Efficiency Model. Query date: 2024-06-07 06:45:39.

Hüllmann, J. A. (2022). Explainable AI in Farming: Configurations of Human-AI Joint Decision-Making. SSRN Electronic Journal, Query date: 2024-06-07 06:45:39.

Jangra, G., Irfan, M., Jangra, M., & Verma, C. (2024). Artificial Intelligence Approach to Portfolio Management. Issues of Sustainability in AI and New-Age Thematic Investing, Query date: 2024-06-07 06:45:39, 59–73.

Kapkanshchikov, S. G. (2022). Evidential policy in the management decision making mechanism at the macro level. Mezhdunarodnaja Jekonomika (The World Economics), 10, 743–756.

Khavrova, K., Chernega, O., Lokhman, N., & Kolchuk, M. (2021). 8 Making optimal decisions based on the development of the innovative potential of personnel. Econometric Modeling of Managerial Decisions at the Macro and Micro Levels, Query date: 2024-06-07 06:45:39, 151–164.

Kim, H. (2023). Suggestions for Ethical Decision-Making Model through Collaboration between Human and AI. J-INSTITUTE, 8(Query date: 2024-06-07 06:45:39), 12–22.

Lane, P., Lafferty, R., Jackson, E., & Lafferty, C. (2023). CHANGING EDUCATION WITH AI: MACRO TO MICRO. ICERI2023 Proceedings, Query date: 2024-06-07 06:45:39.

Latypova, V. (2023). Decision-Making Support in Optimal Multicriteria Peer Reviewer Selection in Scientific Conference Organization. 2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Query date: 2024-06-07 06:45:39.

Lestari, R., Windarwati, H. D., & Hidayah, R. (2024). AI-Driven Decision-Making Applications in Higher Education. Advances in Media, Entertainment, and the Arts, Query date: 2024-06-07 06:45:39, 246–268.

Limsiritong, T., Furutani, T., & Limsiritong, K. (2021). A Challenge of Macro-Meso-Micro Analysis Impacts on Multiracial Nationality Decision Making :Multiracial Thai-Japanese in Bangkok. Inclusive Society and Sustainability Studies, 1(2), 10–23.

Liu, X., & Tang, J. (2021). Network representation learning: A macro and micro view. AI Open, 2(Query date: 2024-06-07 06:45:39), 43–64.

Liu, Y., Zhao, L., Tian, J., Liu, J., & Wang, L. (2024). AI-Based Personalised Clinical Decision Making for Localised Prostate Cancer Patients: Surgery Versus Radiotherapy. Query date: 2024-06-07 06:45:39.

Machlup, F. (2020). Micro- and Macro-Economics. Economic Semantics, Query date: 2024-06-07 06:45:39, 97–144.

Mashunin, Y. (2023). Digital Transformation of Optimal Decision-making in Economic and Engineering Systems Based on the Theory and Methods of Vector Optimization. Modern Intelligent Times, Query date: 2024-06-07 06:45:39.

May, B., Milne, R., Shawyer, A., Meenaghan, A., & Ribbers, E. (2022). Identifying Challenges to Critical Incident Decision-Making through a Macro-, Meso-, Micro- Lens: A Systematic Synthesis and Holistic Narrative Analysis. Query date: 2024-06-07 06:45:39.

Mayer, B., Fuchs, F., & Lingnau, V. (2023). Decision-making in the era of AI support—How decision environment and individual decision preferences affect advice-taking in forecasts. Journal of Neuroscience, Psychology, and Economics, 16(1), 1–11.

Mökander, J., & Axente, M. (2021). Ethics-based auditing of automated decision-making systems: Intervention points and policy implications. AI & SOCIETY, 38(1), 153–171.

Morgan, D., Hashem, Y., Straub, V. J., & Bright, J. (2022). High-stakes team based public sector decision making and AI oversight. Query date: 2024-06-07 06:45:39.

Omerali, M., & Kaya, T. (2023). Energy Efficiency Optimization Application in Food Production Using IIOT Based Machine Learning. Applied Innovation and Technology Management, Query date: 2024-06-07 06:45:39, 185–204.

Orlova, E. V. (2022). Design Technology and AI-Based Decision Making Model for Digital Twin Engineering. Future Internet, 14(9), 248–248.

Pirrone, A., Reina, A., & Gobet, F. (2021). Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making. Judgment and Decision Making, 16(5), 1221–1233.

Pitkäranta, T., & Pitkäranta, L. (2024). Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach. Proceedings of the 26th International Conference on Enterprise Information Systems, Query date: 2024-06-07 06:45:39.

Pryimak, N., Khavrova, K., Kravtsov, A., & Klevtsov, E. (2021). 9 Making optimal strategic decisions in conditions of weakly structured systems using cognitive modeling techniques. Econometric Modeling of Managerial Decisions at the Macro and Micro Levels, Query date: 2024-06-07 06:45:39, 165–181.