Information Dynamics and Learning in Complex Adaptive Systems: Toward a Transdisciplinary Framework

ABSTRACT This article develops a framework for understanding learning and adaptation in complex adaptive systems. Drawing from neuroscience, systems theory, information theory and quantum field theory, it examines how information processing, plasticity and systemic coherence emerge from distributed, nonlinear and feedback‐driven interactions. It argues that neural and organizational systems share core features of adaptive complexity, suggesting a common architecture of informational dynamics. Through analogical mapping and conceptual synthesis, it identifies isomorphisms linking synaptic plasticity to organizational learning, neural connectivity to communication networks and phase transitions to innovation tipping points. A multilayered account shows how informational flows shape emergent behaviour across micro, meso and macro levels, emphasizing the recursive nature of learning. Quantum field models extend systems thinking toward field‐based explanations of coherence. Although theoretical, the study outlines pathways for operationalization through computational modelling and network analysis. It advances a transdisciplinary epistemology framing learning as a systemic, relational process enabling adaptive coherence.