From Cooperation to Hierarchy: A Study of Dynamics of Hierarchy Emergence in a Multi-Agent System

TL;DR

This study uses an agent-based model to explore how hierarchy emerges in multi-agent systems, highlighting the roles of individual differences and mutation rates in the development of structured inequality.

Contribution

It identifies minimal conditions for hierarchy emergence, emphasizing mutation amplitude's influence over initial heterogeneity in dynamic multi-agent systems.

Findings

Hierarchies reliably emerge when mutation amplitude is high.

Initial heterogeneity affects early hierarchy formation but not long-term stability.

Simple interaction rules can produce persistent hierarchical structures.

Abstract

A central premise in evolutionary biology is that individual variation can generate information asymmetries that facilitate the emergence of hierarchical organisation. To examine this process, we develop an agent-based model (ABM) to identify the minimal conditions under which hierarchy arises in dynamic multi-agent systems, focusing on the roles of initial heterogeneity and mutation amplitude across generations. Hierarchical organisation is quantified using the Trophic Incoherence (TI) metric, which captures directional asymmetries in interaction networks. Our results show that even small individual differences can be amplified through repeated local interactions involving reproduction, competition, and cooperation, but that hierarchical order is markedly more sensitive to mutation amplitude than to initial heterogeneity. Across repeated trials, stable hierarchies reliably emerge only when mutation amplitude is sufficiently high, while initial heterogeneity primarily affects early formation rather than long-term persistence. Overall, these findings demonstrate how simple interaction rules can give rise to both the emergence and persistence of hierarchical organisation, providing a quantitative account of how structured inequality can develop from initially homogeneous populations.