"Enhancing Human-Machine Interaction Through Cybernetic Theory: Improving System Stability and Adaptive Behavior in Decision-Making Algorithms"

EasyChair Preprint 14360

Abstract

This 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

@booklet{EasyChair:14360,
  author    = {Oluwaseun Abiade},
  title     = {"Enhancing Human-Machine Interaction Through Cybernetic Theory: Improving System Stability and Adaptive Behavior in Decision-Making Algorithms"},
  howpublished = {EasyChair Preprint 14360},
  year      = {EasyChair, 2024}}