A Model of Synchronization for Self-Organized Crowding Behavior

TL;DR

This paper introduces a general model for synchronized crowding behavior, quantifying synchronization levels and identifying conditions leading to self-amplifying, unstable crowd phenomena, with applications to financial market dynamics.

Contribution

The paper presents a novel model linking agent activity and volatility to synchronization, providing insights into crowd behavior and market phenomena.

Findings

Synchronization increases with active, volatile agents

A tipping point causes crowd instability and self-amplification

Model successfully simulates financial bubbles and market volatility patterns

Abstract

This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A tipping point is identified when crowd becomes self-amplifying and unstable. By applying this model, financial bubbles, market momentum and volatility patterns are simulated.