Self-Organization in Systems Science


Self-organization is a fundamental concept in systems science that describes the spontaneous emergence of order, patterns, or structures within a system without external intervention or centralized control. It is a process where local interactions among components give rise to global organization, leading to the formation of complex and adaptive systems.

Key Characteristics:

Local Interactions:

  • Self-organization occurs through local interactions between components of a system, often following simple rules or principles.

Emergence of Order:

  • The collective behavior of components leads to the emergence of order or patterns at the global level, giving the system coherence and structure.


  • Self-organizing systems are often adaptive, capable of adjusting their structure and behavior in response to changes in the environment or internal dynamics.


Social Networks:

  • Online social networks exhibit self-organization as users interact locally by forming connections, leading to the emergence of global network structures.

Ant Colonies:

  • Ant colonies exemplify self-organization, where individual ants follow local rules, resulting in the coordinated behaviors of the entire colony, such as foraging and nest-building.

Traffic Flow:

  • Traffic flow can self-organize as drivers locally adjust their speed and spacing, leading to the emergence of traffic patterns and waves.
The self-organizing city

Mathematical Representation:

Mathematically representing self-organization often involves models based on principles of nonlinear dynamics, cellular automata, or agent-based modeling. These models capture the local interactions and rule-based behaviors that lead to global patterns.

Significance in Systems Thinking:

Understanding self-organization is crucial in systems thinking as it emphasizes the decentralized nature of order in complex systems. It challenges traditional top-down approaches and underscores the importance of local interactions in shaping global system behavior.



  • The inherent complexity of self-organizing systems can make them challenging to predict, requiring a focus on understanding principles rather than precise outcomes.

Balance with Stability:

  • Achieving a balance between self-organization and stability is a challenge, as overly chaotic systems may lack coherence, while overly ordered systems may stifle adaptability.

Application in Various Disciplines:

  • Biology: Self-organization is observed in biological systems, such as the formation of spatial patterns in animal coat markings or the development of organ structures.
  • Physics: Complex patterns in physical systems, like the formation of convection cells in fluids, often result from self-organization.
  • Information Technology: Self-organization is applied in network design, where devices autonomously adapt their connections to optimize communication.


  1. Camazine, S., Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2003). Self-organization in biological systems.
  2. Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution.

This wiki entry provides an overview of the concept of self-organization in systems science, outlining its key characteristics, examples, significance in systems thinking, challenges, potential mathematical representation, and applications across various disciplines.