Heterophily as a generative mechanism for self-organized synergistic interdependencies

TL;DR

This paper demonstrates that heterophily, as a local adaptive mechanism, can generate self-organized synergistic interdependencies in complex systems, with implications across social, neural, and biological domains.

Contribution

It introduces heterophily as a minimal mechanism for synergy emergence, supported by analytical and numerical evidence in adaptive interaction models.

Findings

Heterophily weakens pairwise dependencies and induces high-order dependencies.

The mechanism persists in large, heterogeneous, and dynamic systems.

Heterophily can disrupt polarization and enhance group-level influence in opinion dynamics.

Abstract

Understanding what and how causal dynamical mechanisms generate collective phenomena is a central challenge in complexity science. Recent studies have focused on identifying the mechanisms underlying the synergistic interdependencies that characterise these phenomena in systems with fixed interaction structures. Yet, real-world systems displaying collective phenomena, such as brains, societies, and ecosystems, are adaptive: interactions change in time. Here, we show that heterophily is a minimal local adaptive mechanism for the emergence of self-organized synergistic interdependencies. We study a paradigmatic spin-glass-like model with co-evolving couplings to show how heterophily generates the conditions for synergy to emerge. By solving the minimal $N=3$ case analytically, we reveal the precise mechanism: heterophily weakens pairwise dependencies while inducing high-order dependencies via geometric constraints on the configurations it selects. Together, these two effects underpin synergy. Numerical simulations confirm that this mechanism persists in large systems and that it is robust under parameter heterogeneities and dynamics. We demonstrate the applicability of our results by showing how heterophily can disrupt polarization while promoting synergistic information dynamics of opinions, where individuals' opinions are better explained by group-level influences than by pairwise ones. These results offer a parsimonious route to self-organized synergistic interdependencies in information-processing systems, with potential applications in computational social science, neuroscience, and biology.