Multifractal emergent processes: Multiplicative interactions override nonlinear component properties

Among the statistical models employed to approximate nonlinear interactions in biological and psychological processes, one prominent framework is that of cascades. Despite decades of empirical work using multifractal formalisms, a fundamental question has persisted: Do the observed nonlinear interactions across scales owe their origin to multiplicative interactions, or do they inherently reside within the constituent processes? This study presents the results of rigorous numerical simulations that demonstrate the supremacy of multiplicative interactions over the intrinsic nonlinear properties of component processes. To elucidate this point, we conducted simulations of cascade time series featuring component processes operating at distinct timescales, each characterized by one of four properties: multifractal nonlinearity, multifractal linearity (obtained via the Iterative Amplitude Adjusted Wavelet Transform of multifractal nonlinearity), phase-randomized linearity (obtained via the Iterative Amplitude Adjustment Fourier Transform), and phase- and amplitude-randomized (obtained via shuffling). Our findings unequivocally establish that the multiplicative interactions among components, rather than the inherent properties of the component processes themselves, decisively dictate the multifractal emergent properties. Remarkably, even component processes exhibiting purely linear traits can generate nonlinear interactions across scales when these interactions assume a multiplicative nature. In stark contrast, additivity among component processes inevitably leads to a linear outcome. These outcomes provide a robust theoretical underpinning for current interpretations of multifractal nonlinearity, firmly anchoring its roots in the domain of multiplicative interactions across scales within biological and psychological processes.