Organizational Systems Control Model with Bounded Rationality of the Control Subjects

This article proposes a model for evaluating bounded rationality in real production and socio-economic systems. The proposed model is relevant for a wide range of different production and socio-economic systems. By applying such a model, researchers and practitioners can reduce the negative impact of bounded rationality on the adequacy of decision support systems, which will solve the problem underlying the research. In the framework of the study, the control object is represented by a set of attributes that together form the phase space of the control object states (each attribute is one dimension of this space). The practical application of the proposed model can be found in the field of forecasting the range of consequences of control decisions in innovative projects. The use of the model may improve the accuracy of forecasting in decision support models, which may increase the efficiency of control processes in such systems. Obviously, the presented solution will mathematically expand the range of possible outcomes in the implementation of predictive planning, which means reducing the number of cases of erroneous forecasting and difficulty reading the forecasting results. Practitioners can use it to gain a more adequate understanding of the possible consequences of their decisions. In addition, the model requires a certain level of formalization of control objects, which contributes to the systematization and concretization of information about enterprises.

Bounded Rationality; Control Sciences; Operational Research; Organizational Systems

Abas, A., Arifin, K., Ali, M. A. M., & Khairil, M. (2023). A systematic literature review on public participation in decision-making for local authority planning: A decade of progress and challenges. Environmental Development, 46, 100853. https://doi.org/10.1016/j.envdev.2023.100853

Akbulut, O., Cavus, M., Cengiz, M., Allahham, A., Giaouris, D., & Forshaw, M. (2024). Hybrid intelligent control system for adaptive microgrid optimization: Integration of rule-based control and deep learning techniques. Energies, 17(10), 2260. https://doi.org/10.3390/en17102260

Albats, E., Alexander, A. T., & Cunningham, J. A. (2022). Traditional, virtual, and digital intermediaries in university-industry collaboration: Exploring institutional logics and bounded rationality. Technological Forecasting and Social Change, 177, 121470. https://doi.org/10.1016/j.techfore.2022.121470

Andronchev, I. K., Dmitriev, A. Y., & Mitroshkina, T. A. (2024). Improving the assessment of the quality of expertise of an innovative project. Competence, (8), 4–9. https://doi.org/10.24412/1993-8780-2024-8-04-09

Babaei, F., Boozarjomehry, R. B., Ravandi, Z. K., & Pishvaie, M. R. (2025). An information integration framework toward cross-organizational management of integrated energy systems. Journal of Industrial Information Integration, 44, 100791. https://doi.org/10.1016/j.jii.2025.100791

Baranovskiy, A. A., & Romanova, Y. A. (2023). Risk assessment of innovative projects. Economics of Construction, (10), 27–31. https://doi.org/10.1515/mper-2015-0032

Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://doi.org/10.3389/fhumd.2024.1421273

Collins, A. G., & Shenhav, A. (2022). Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47 (1), 104–118. https://doi.org/10.1038/s41386-021-01126-y

Dhami, S., & Sunstein, C. R. (2022). Bounded rationality: Heuristics, judgment, and public policy. MIT Press. https://doi.org/10.7551/mitpress/14401.003.0002

Diks, C., & Dindo, P. (2008). Informational differences and learning in an asset market with boundedly rational agents. Journal of Economic Dynamics & Control, 32, 1432–1465. https://doi.org/10.1016/j.jedc.2007.06.003

Du, J., Cui, J., Ren, G., Thompson, R. G., & Zhang, L. (2025). Cascading failures and resilience evolution in urban road traffic networks with bounded rational route choice. Physica A: Statistical Mechanics and its Applications, 664, 130456. https://doi.org/10.1016/j.physa.2025.130456

Eigner, E., & Händler, T. (2024). Determinants of LLM-assisted decision-making. arXiv preprint. https://doi.org/10.48550/arXiv.2402.17385

Ghasemieh, H., Ghodsi, M., Mahini, H., & Ali Safari, M. (2013). Pricing in population games with semi-rational agents. Operations Research Letters, 41, 226–231. https://doi.org/10.1016/j.orl.2013.01.008

Giarlotta, A., & Petralia, A. (2024). Simon’s bounded rationality: A. giarlotta, a. petralia. Decisions in Economics and Finance, 47(1), 327–346. https://doi.org/10.1007/s10203-024-00436-2

Kuznetsov, O. P. (2019). Bounded rationality and decision-making. Artificial Intelligence and Decision-Making, (1), 3–15. https://doi.org/10.14357/20718594190101

Li, Y., Kou, G., Li, G., & Peng, Y. (2022). Consensus reaching process in large-scale group decision making based on bounded confidence and social network. European Journal of Operational Research, 303(2), 790–802. https://doi.org/10.1016/j.ejor.2022.03.040

Liu, J., & Zhou, X. (2016). Capacitated transit service network design with boundedly rational agents. Transportation Research Part B, 93, 225–250. https://doi.org/10.1016/j.trb.2016.07.015

Lomakin, N., Kulachinskaya, A., Tsygankova, V., Kosobokova, E., Minaeva, O., & Trunina, V. (2023). Forecast of stability of the economy of the Russian Federation with the AI-system “decision tree” in a cognitive model. International Journal of Technology, 14(8), 1800–1809. https://doi.org/10.14716/ijtech.v14i8.6848

Masters, P., & Sardina, S. (2021). Expecting the unexpected: Goal recognition for rational and irrational agents. Artificial Intelligence, 297, 103490. https://doi.org/10.1016/j.artint.2021.103490

Matiashvili, V. M. (2015). The concept of “bounded rationality” in the context of organizational change control. Bulletin of Nizhny Novgorod N. I. Lobachevsky University. Series: Social Sciences, 1(37), 148–154. https://doi.org/10.18413/2073-507x-2015-1-37-148-154

Novikov, D. A. (2022). Bounded rationality and control. Mathematical Game Theory and its Applications, 14(1), 49–84. https://doi.org/10.17076/mgta_2022_1_44

Petracca, E. (2021). Embodying bounded rationality: From embodied bounded rationality to embodied rationality. Frontiers in Psychology, 12, 710607. https://doi.org/10.3389/fpsyg.2021.710607

Puranam, P., Stieglitz, N., Osman, M., & Pillutla, M. M. (2015). Modelling bounded rationality in organizations: Progress and prospects. Academy of Management Annals, 9 (1), 337–392. https://doi.org/10.5465/19416520.2015.1024498

Sabara, Z., Afiah, I. N., & Umam, R. (2022). Integration of green ergonomics in robust decision making (RDM) approach in water resources control in Makassar city. International Journal of Technology, 13(2), 264–273. https://doi.org/10.14716/ijtech.v13i2.5113

Sharma, S., Islam, N., Singh, G., & Dhir, A. (2022). Why do retail customers adopt artificial intelligence (AI) based autonomous decision-making systems? IEEE Transactions on Engineering Management, 71, 1846–1861. https://doi.org/10.1109/TEM.2022.3157976

Sieg, G. (2001). A political business cycle with boundedly rational agents. European Journal of Political Economy, 17, 39–52. https://doi.org/10.1016/S0176-2680(00)00027-6

Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943. https://doi.org/10.1016/j.ijhm.2021.102943

Tabilo Alvarez, J., & Ramírez-Correa, P. (2023). A brief review of systems, cybernetics, and complexity. Complexity, 2023(1), 8205320. https://doi.org/10.1155/2023/8205320

Thaler, R. H. (2000). From homo economicus to homo sapiens. Journal of Economic Perspectives, 14(1), 133–141. https://doi.org/10.1257/jep.14.1.133

Thorstad, D. (2024). Why bounded rationality (in epistemology)?. Philosophy and Phenomenological Research, 108(2), 396–413. https://doi.org/10.1111/phpr.12978

Upravitelev, A. A. (2017). Bounded rationality in various fields of economic theory. Perspectives of Science and Education (PSE), 1(61), 58–61. https://doi.org/10.31618/esu.2413-530X.2017.1.53.179

Wajanawichakon, K., Srisurin, K., & Ongkunaruk, P. (2025). Multi-criteria decision analysis for sustainable crop selection in northeast Thailand: An analytical hierarchy process approach. International Journal of Technology, 16(3), 780–795. https://doi.org/10.14716/ijtech.v16i3.7118

Wang, B., Li, Z., Wang, S., Li, M., & Ji, A. (2022). Modeling bounded rationality in discretionary lane change with the quantal response equilibrium of game theory. Transportation Research Part B: Methodological, 164, 145–161. https://doi.org/10.1016/j.trb.2022.08.008

Yun, J. J., Ahn, H. J., Lee, D. S., Park, K. B., & Zhao, X. (2022). Inter-rationality; modeling of bounded rationality in open innovation dynamics. Technological Forecasting and Social Change, 184, 122015. https://doi.org/10.1016/j.techfore.2022.122015

Zhang, C., Ding, J., Zhan, J., Sangaiah, A. K., & Li, D. (2022). Fuzzy intelligence learning based on bounded rationality in IoMT systems: A case study in Parkinson’s disease. IEEE Transactions on Computational Social Systems, 10(4), 1607–1621. https://doi.org/10.1109/TCSS.2022.3221933

Zhang, X., Li, Y., Dong, S., Di, C., & Ding, M. (2023). The influence of user cognition on consumption decision-making from the perspective of bounded rationality. Displays, 77, 102392. https://doi.org/10.1016/j.displa.2023.102392