CULTURE AS AN INTERFACE BETWEEN COMMUNICATION AND COMPARISON THE CONCEPT OF CULTURE FROM THE SYSTEMS THEORY PERSPECTIVE

This research direction explores the application of systems theory to model communication and comparison interfaces within distributed embedded systems and Edge AI networks. Drawing conceptual parallels from systems theory—where 'culture' acts as an interface facilitating communication and comparison—this project structures a framework for multi-agent edge node interaction. The methodology focuses on designing an abstract interface layer that standardizes how heterogeneous edge devices compare local datasets and communicate inference results without centralized coordination. Through system-level simulation and modeling, the project provides guidance on optimizing bandwidth utilization and reducing latency during peer-to-peer node comparisons. The research offers implementation support for evaluating the interface's robustness under varying communication constraints, establishing a structured approach

to decentralized decision-making. By analyzing the system from a holistic systems theory perspective, this work assists in the architectural review of self-organizing edge networks, providing a foundation for future deployment in collaborative Edge AI environments. Ultimately, the framework serves as a reference model for structuring adaptive communication protocols in resource-constrained embedded environments. This approach enables robust comparison of localized machine learning models across distributed nodes, ensuring high system reliability.

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