ln(3): A Universal Percolation Constant for Collective Dynamics on One-Dimensional Proximity Networks

TL;DR

This paper identifies and proves a universal constant, ln(3), that determines the onset of collective behavior in one-dimensional proximity networks, validated by empirical traffic datasets and linked to fundamental spatial symmetry principles.

Contribution

It introduces a model-independent, parameter-free universal constant, ln(3), governing bidirectional collective behavior in 1D proximity networks, supported by empirical validation.

Findings

The threshold for collective behavior is at topological density >= ln(3).

Empirical datasets confirm the theoretical threshold with significant reductions in speed variance.

The constant ln(3) is consistent across different physical and biological systems.

Abstract

We report the identification and proof of a universal constant, ln(3) = 1.09861, which governs the onset of bidirectional collective behavior in one-dimensional Poisson proximity networks. The constant - named the cooperative percolation constant and denoted by Lambda_c - is the unique positive solution to 2/(exp(x)-1) = 1 and equals the Shannon entropy of three equiprobable states. For agents distributed at intensity lambda and interacting within range L, bidirectional collective behavior is possible if and only if the topological density (lambda * L) >= ln(3). Below this threshold, no cooperative control policy can produce macroscopic coherence, as the proximity graph does not contain a bidirectional spanning cluster in expectation. The result is parameter-free and model-independent: the Poisson distribution is derived from memorylessness symmetry axioms, making ln(3) a fundamental consequence of spatial symmetry. The threshold is validated by two independent large-scale empirical datasets. Analysis of the Chengdu V2X OBU dataset (N = 19.7 million records) reveals a 1.60x reduction in speed variance at the predicted boundary. Furthermore, the highD German motorway dataset (N = 163,896 observations) yields a best-fit LWR exponent theta = 1.033 +/- 0.088, placing the theoretical value ln(3) within 0.75 sigma of naturalistic trajectory data (R^2 = 0.8631). The remarkable consistency across geographical and physical scales - from motorway traffic to documented thresholds in 1D biological signal transmission - suggests that ln(3) represents a fundamental topological rent for cooperative information exchange.