Decision-Making in the Age of AI: A Review of Theoretical Frameworks, Computational Tools, and Human-Machine Collaboration

作者：Jian Wei, Qi Sun, Wanjiang Wang, Li Jiang, Huihui Gao, Feng Zhao, Khalil AL-Bukhaiti, Anping Wan · 发表于：Contemporary Mathematics · 年份：2025 · DOI：10.37256/cm.6220256459 · 被引用次数：14 · 研究领域：Big Data and Business Intelligence

Decision-making, a critical process across all disciplines, is undergoing significant transformation with the integration of advanced computational techniques. This review examines the evolution of decision-making, from its theoretical foundations to the implementation of optimization algorithms, neural networks, and artificial intelligence (AI). The review first explores the contrasting paradigms of normative and descriptive decision-making theories. Normative theories propose ideal decision-making processes based on rational calculations, while descriptive theories aim to explain real-world human decision-making, often incorporating cognitive biases and heuristics. Subsequently, the review investigates optimization techniques focusing on neural networks and deep learning. These powerful tools enable machines to learn complex patterns from data, facilitating tasks such as classification, prediction, and decision-making. Various neural network architectures, including feedforward networks, convolutional networks, and recurrent networks, are examined, highlighting their distinctive strengths and limitations. The review further explores the integration of AI in decision-making processes, examining its impact on organizational structures and the inherent challenges of human-machine collaboration within uncertain environments. While AI excels at analyzing vast datasets and identifying patterns, human judgment remains indispensable for strategic decisions, particularly in areas re...