Emergent contagion complexity: Disentangling mechanistic complexity from correlated heterogeneity

2026-07-27

AI Summary

Complex contagion may arise either from genuine higher-order transmission mechanisms or from the emergent complexity generated by heterogeneous mixtures of simple contagion processes. This work proposes a nonparametric inference framework based on time-series data that models the mixture structure of contagion rules to effectively disentangle mechanistic complexity from observed apparent complexity. By explicitly treating heterogeneous simple contagion as an alternative explanation for complex contagion, the approach reframes the prevailing conceptual paradigm of complex contagion and offers a novel pathway for causal identification in existing studies.

Abstract

Simple and complex contagions differ mechanistically; multiple exposures act synergistically in the latter but independently in the former. Yet correlated mixtures of simple contagions may appear complex when inferring global contagion rules, a phenomenon we call "emergent complexity." We present a measure of contagion complexity and an inferential framework for estimating mixtures of nonparametric contagion rules from time-series data. Our work reframes past studies on complex contagion by offering heterogeneous mixtures of simple contagions as an alternative explanation.

Problem

• complex contagion
• simple contagion
• emergent complexity
• correlated heterogeneity
• contagion rules

Innovation

• emergent complexity
• contagion complexity
• heterogeneous mixtures
• nonparametric inference
• simple vs. complex contagion