Complex Systems Modeling via Agent-Based Modeling and Simulation (ABMS)

What Are Complex Systems?

A complex system consists of many interacting components (agents) whose collective behavior cannot be directly inferred from the properties of the individual parts. Such systems exhibit emergence, adaptation, and nonlinearity, making them fundamentally different from merely complicated or mechanistic systems.

“More is different.” — P. W. Anderson (1972)

The study of complex systems seeks to explain how order, patterns, and intelligence emerge spontaneously through local interactions and feedback loops. Examples appear across disciplines—from biological and ecological networks to social, economic, and technological systems.

Key characteristics of complex systems include:

Nonlinearity: small changes can lead to large effects.
Feedback loops: agents continuously influence and adapt to each other.
Emergence: global patterns arise without centralized control.
Self-organization: spontaneous formation of structures or behaviors.
Adaptivity: agents learn, evolve, or respond to changing environments.

Agent-Based Modeling and Simulation (ABMS) is a powerful approach to study such systems. It provides a computational microscope that allows researchers to observe how simple, local interactions among individual agents give rise to emergent collective behavior and large-scale dynamics.

Examples of Complex Systems

Social systems: opinion dynamics, cooperation, cultural diffusion.
Economic systems: market fluctuations, consumer behavior, supply networks.
Biological systems: ecosystems, immune networks, collective animal motion.
Urban and mobility systems: pedestrian flow, traffic dynamics, city growth.
Technological systems: distributed robotics, the Internet, AI agent ecosystems.

Prototype Models — Complex Systems via ABMS

Pedestrian Crossing Behavior

Models how heterogeneous pedestrians decide when and where to cross based on local visibility, vehicle flow, and social influence. The simulation explores how cooperative and risky behaviors collectively shape overall traffic efficiency and safety.

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Customer Purchase Behavior

Simulates how individual customer decisions—driven by peer influence, product visibility, and marketing—lead to emergent demand patterns. Demonstrates adaptive markets and feedback-driven popularity cycles.

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City Simulation Model

Agent-based simulation of city dynamics—showing how urban systems emerge from many interacting agents.

🚧 Coming soon