Download PDFOpen PDF in browser"Enhancing Human-Machine Interaction Through Cybernetic Theory: Improving System Stability and Adaptive Behavior in Decision-Making Algorithms"EasyChair Preprint 1436011 pages•Date: August 9, 2024AbstractThis paper explores the application of cybernetic theory to enhance human-machine interaction, with a focus on improving system stability and adaptive behavior in decision-making algorithms. By integrating principles of feedback control, self-regulation, and adaptive learning from cybernetics, we propose novel methodologies to refine the interaction dynamics between humans and intelligent systems. Our approach addresses key challenges such as mitigating decision-making biases, increasing algorithmic robustness, and fostering more intuitive user interfaces. Through a combination of theoretical analysis and empirical studies, we demonstrate how cybernetic frameworks can be employed to create more resilient and responsive decision-making algorithms. The findings highlight significant improvements in system stability and adaptability, offering valuable insights for the design of next-generation human-machine interfaces. Keyphrases: Algorithms, Enhancement, intersection
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