Analytical solution for the long- and short-range every-pair-interactions model

TL;DR

This paper derives an analytical model for pairwise interactions with power-law dependence in fractal structures, revealing different regimes of interaction strength and proposing methods to estimate fractal dimensions.

Contribution

It provides the first analytical solution for mean interaction fields in systems with power-law, fractal-based pair interactions, including corrections for discrete effects.

Findings

Mean interaction field scales with system size in long-range regime

Field saturates in short-range regime

Logarithmic behavior observed in intermediate range

Abstract

Many physical, biological, and social systems exhibit emergent properties that arise from the interactions between their components (cells). In this study, we systematically treat every-pair interactions (a) that exhibit power-law dependence on the Euclidean distance and (b) act in structures that can be characterized using fractal geometry. We analytically derive the mean interaction field of the cells and find that (i) in a long-range interaction regime, the mean interaction field increases following a power law with the size of the system, (ii) in a short-range interaction regime, the field saturates, and (iii) in the intermediate range it follows a logarithmic behaviour. To validate our analytical solution, we perform numerical simulations. In the case of short-range interactions, we observe that discreteness significantly impacts the continuum approximation used in the derivation, leading to incorrect asymptotic behaviour in this regime. To address this issue, we propose an expansion that substantially improves the accuracy of the analytical expression. Furthermore, our results motivate us to explore a framework for estimating the fractal dimension of unknown structures. This approach offers an alternative to established methods such as box-counting or sandbox methods. Overall, we believe that our analytical work will have broad applicability in systems where every-pair interactions play a crucial role. The insights gained from this study can contribute to a better understanding of various complex systems and facilitate more accurate modelling and analysis in a wide range of disciplines.