Complexity Condensation Through Adaptive Information Exchange

An emergent complexity field governing information exchange is the central theme of this work. To explore this idea, we propose a model of an adaptive dynamical network in which both the interaction weights and the adaptive coupling strengths are determined by finite-time information production rates that quantify the dynamical complexity of individual subsystems. Collective organization in complexity space emerges through a feedback mechanism between the microscopic dynamics and the resulting complexity-dependent interactions. Using numerical simulations, we demonstrate the emergence of a phenomenon that we term \emph{complexity condensation}, in which subsystem complexities become strongly localized despite the absence of complete state synchronization. The degree of condensation is found to be maximal at an intermediate adaptation strength, reflecting a balance between selective information exchange and network fragmentation. These results reveal a mechanism for complexity-mediated self-organization in nonlinear systems driven by adaptive information exchange.