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The increasing complexity of human-technology interactions has led to a growing concern about the potential risks associated with their systemic instability. Traditional approaches to managing these interactions have focused on short-term fixes and reactive strategies, which can exacerbate the problem. This research presents a novel predictive model that addresses this critical issue by providing a framework for achieving systemic equilibrium in human-technology interactions. The rapid growth of technology has led to an unprecedented level of interconnectedness among humans, technology, and the environment. While this growth has numerous benefits, it also creates new challenges, such as system crashes, cyber attacks, and information overload. The lack of a comprehensive understanding of human-technology interactions has hindered the development of effective management strategies, leading to systemic instability and chaos. Our research employed a mixed-methods approach, combining quantitative and qualitative data collection and analysis techniques. We developed a predictive model that integrates elements of system dynamics, chaos theory, and human-centered design to capture the complexity of human-technology interactions. The model consists of three main components: human dynamics, technology systems, and environment interactions. We used simulation modeling to test the predictive capabilities of the model and validated its results against real-world case studies. Our research demonstrates that the predictive model can accurately forecast the behavior of human-technology interactions under various scenarios, including system crashes, cyber attacks, and information overload. The model also provides insights into the underlying causes of systemic instability and identifies effective strategies for achieving equilibrium. This research contributes to the development of a new paradigm for managing human-technology interactions, one that prioritizes systemic equilibrium and stability. Our predictive model offers a powerful tool for policymakers, industry leaders, and researchers to anticipate and mitigate potential risks associated with these interactions. By adopting a predictive approach, we can create more resilient, adaptive, and human-centered technology systems that promote well-being and prosperity.

Copyright © 2025 Akshaj Khetarpal. This is an open access article distributed under the Creative Commons Attribution License.