Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence

2026-08-07

AI Summary

This study investigates how local mobility, bounded memory, and network structure jointly shape the critical conditions and time scales under which committed minorities drive shifts in group conventions. To this end, we develop a transparent agent-based model that simulates the dynamics of individuals switching between two behavioral states. Departing from prior work focused solely on whether convention change occurs, this paper systematically links structural and behavioral factors to the temporal dynamics of convention shift and introduces a unified predictive model that quantifies their influence on the time required for full adoption. Simulation results demonstrate that convention change ultimately occurs across most configurations, with mobility acting as the dominant accelerator of convergence; memory length and network connectivity further modulate convergence speed in predictable ways, enabling the model to accurately forecast the time to full adoption.

Abstract

Tipping-point dynamics describe the critical conditions under which a committed minority drives a population to abandon an established convention in favor of a new one. We present a transparent agent-based model of this process, in which agents hold one of two behavioral states and a mobile committed minority attempts to overturn the incumbent convention. Our goal was to examine how localized mobility, bounded agent memory, and network topology jointly influence the tipping threshold. Using a custom agent-based simulation framework, we found that in many configurations, tipping becomes effectively inevitable: given sufficient time, the population always converges to the minority state. This observation motivated a complementary analysis focused on the pace of convergence rather than its feasibility. We introduce a unified predictive model that accurately estimates how structural and behavioral parameters determine the time required for complete adoption, showing that mobility is the dominant accelerator while memory and connectivity modulate convergence in systematic ways. Together, these results extend classical tipping-point research by linking structural and behavioral factors not only to the likelihood of convention change but also to the timescale on which it unfolds. While we frame the model in terms of convention-like binary behavioral adoption, the same mechanisms bear on norm change and other contagion-like social processes.

Problem

• tipping-point dynamics
• agent-based model
• convention change
• network structure
• bounded memory

Innovation

• agent-based modeling
• tipping-point dynamics
• localized mobility
• bounded memory
• convergence time