Abstract

This comprehensive paper delves into the intricate challenges faced by intelligent decision-making systems, concentrating primarily on three pivotal aspects: the integrity of data quality, the efficacy of algorithms, and the real-world practical limitations these systems encounter. It methodically identifies and categorizes a range of problems inherent in the decision-making process, with a special focus on how the quality of data and the selection of algorithms significantly influence the outcomes of decisions. This analysis is crucial as it sheds light on the often-underestimated impact of foundational data and algorithmic strategies on decision accuracy and reliability. Moreover, the paper provides a critical examination of the current limitations of prevalent algorithms in the field. In response, it advocates for a dual approach: firstly, enhancing data management practices to ensure a robust foundation for decisions; secondly, developing algorithms that are not only transparent but also highly adaptable to varying scenarios. This approach is essential for maintaining trust and understanding in decision-making processes. The paper further proposes specific strategies to improve the systems. Enhancing algorithmic transparency is highlighted as a key tactic to build trust and provide clarity in how decisions are derived. Additionally, the paper suggests the adoption of cross-validation methods as a robust measure against overfitting, ensuring models remain accurate and applicable when faced with new data. Emphasizing the need for interdisciplinary approaches, the paper argues that integrating knowledge from diverse fields is vital for the practical applicability and effectiveness of intelligent decision-making systems across various sectors. This holistic approach is proposed as a way to bridge the gap between theoretical development and real-world application. In summary, this research significantly contributes to the field of intelligent decision-making. It offers valuable insights and practical strategies for the advancement and application of these systems, serving as a guiding framework for researchers, practitioners, and policymakers engaged in the development and implementation of advanced decision-making technologies.