Understanding Informational Bottlenecks in Complex Systems: Strategies for Enhancing Decision Quality in Financial and Non-Financial Markets
DOI:
https://doi.org/10.62802/h60edh17Keywords:
Informational Bottlenecks, Complex Systems, Decision-Making, Financial Markets, Non-Financial Systems, Data Overload, Machine Learning, Cognitive Biases, System ResilienceAbstract
Informational bottlenecks in complex systems represent a critical challenge, influencing the quality and timeliness of decision-making across financial and non-financial markets. This research investigates the origins and dynamics of informational bottlenecks, exploring their impact on system efficiency, resilience, and decision quality. By examining theoretical models and real-world case studies, the study identifies key mechanisms through which bottlenecks emerge, including data overload, communication inefficiencies, and cognitive biases. Additionally, it evaluates strategies for mitigating these bottlenecks through advanced analytics, machine learning, and organizational design interventions. Special attention is given to the role of technology in enabling effective data flow and enhancing decision-making processes. This interdisciplinary analysis spans financial markets, where rapid information processing is vital, and non-financial systems such as supply chains and healthcare networks, highlighting universal challenges and tailored solutions. The findings contribute to a deeper understanding of how informational bottlenecks disrupt system performance and propose actionable strategies for fostering robust and adaptive decision-making frameworks in complex environments.
References
Arhinful, R., Mensah, L., Amin, H. I. M., & Obeng, H. A. (2024). The influence of cost of debt, cost of equity and weighted average cost of capital on dividend policy decision: Evidence from non-financial companies listed on the Frankfurt Stock Exchange. Future Business Journal, 10(1), 99.
Artene, A. E., Domil, A. E., & Ivascu, L. (2024). Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems. Electronics (2079-9292), 13(15).
Erben Yavuz, A., Kocaman, B. E., Doğan, M., Hazar, A., Babuşcu, Ş., & Sutbayeva, R. (2024). The Impact of Corporate Governance on Sustainability Disclosures: A Comparison from the Perspective of Financial and Non-Financial Firms. Sustainability, 16(19), 8400.
Monteiro, A. P., Vale, J., Leite, E., & Lis, M. (2024). Linking quality of accounting information system and financial reporting to non-financial performance: The role women managers. International Journal of Accounting Information Systems, 54, 100692.
Nuzhna, O., Derevianko, S., Hurenko, T., Sydorenko, R., & Grudzevych, Y. (2024). Accounting, analytical and financial support for making management decisions of the enterprise. Cadernos de Educação Tecnologia e Sociedade, 17(se3), 306-316.
Sarioguz, O., & Miser, E. (2024). Integrating AI in financial risk management: Evaluating the effects of machine learning algorithms on predictive accuracy and regulatory compliance.
Siraj, M. L., Syarifuddin, S., Tadampali, A. C. T., Zainal, H., & Mahmud, R. (2024). Understanding Financial Risk Dynamics: Systematic Literature Review inquiry into Credit, Market, and Operational Risks:(A Long-life Lesson From Global Perspective to Indonesia Market Financial Strategy). Atestasi: Jurnal Ilmiah Akuntansi, 7(2), 1186-1213.
Sulastri, R., Janssen, M., van de Poel, I., & Ding, A. (2024). Transforming towards inclusion-by-design: Information system design principles shaping data-driven financial inclusiveness. Government Information Quarterly, 41(4), 101979.
Vatankhah, S., Bamshad, V., Lohmann, G., & Shneikat, B. (2024). Airline business models as complex systems: assessing component interdependencies through interpretive structural modelling. Journal of Hospitality and Tourism Insights.
Zhao, S., Zhang, T., & Li, N. (2024). Machine learning analysis of key features in household financial decision-making. Academic Journal of Science and Technology, 12(2), 1-6.
