Emergence in Systems Science


Emergence in systems science refers to the phenomenon where complex patterns, structures, or behaviors arise in a system that are not explicitly present in its individual components. Instead, these emergent properties result from the interactions and relationships among the system’s elements.

Key Characteristics:

  1. Non-Summative: Emergence challenges the reductionist perspective by emphasizing that the whole system’s behavior cannot be simply deduced from the sum of its individual parts.
  2. Novelty: Emergent properties often manifest as novel characteristics that are not apparent in the system’s components. These properties can only be observed at the systemic level.
  3. Dynamic and Context-Dependent: Emergence is dynamic, changing over time as the interactions within the system evolve. Additionally, emergent properties can be context-dependent, varying under different conditions.
  4. Holistic Perspective: Understanding emergence requires a holistic perspective that considers the interactions, relationships, and feedback loops within a system.


  1. Consciousness: In cognitive science, consciousness is considered an emergent property of the brain’s complex neural networks. While individual neurons have specific functions, consciousness arises from their intricate interactions.
  2. Traffic Flow: The flow patterns in traffic, such as traffic jams or the formation of coherent traffic waves, are emergent phenomena resulting from the interactions between individual vehicles.
  3. Ecological Systems: Biodiversity, ecosystem stability, and the emergence of new species are examples of emergent properties in ecological systems, where the interactions between various species lead to novel ecosystem characteristics.

Significance in Systems Thinking:

Understanding emergence is crucial in systems thinking as it encourages a shift from focusing solely on individual components to recognizing the importance of relationships and interactions within a system. It highlights the need to consider the system as a whole to comprehend its unique properties and behaviors.


  1. Prediction Difficulty: Predicting emergent properties can be challenging due to the non-linear and context-dependent nature of complex systems.
  2. Scale Sensitivity: Emergence is often scale-sensitive, meaning that properties observed at one scale may not be applicable or predictable at another.

Application in Various Disciplines:

  • Physics: Emergence is evident in the study of phase transitions and the behavior of matter at different scales.
  • Social Sciences: Social phenomena like culture, norms, and societal structures often exhibit emergent properties arising from the interactions of individuals.
  • Computer Science: Artificial intelligence and machine learning systems can exhibit emergent behavior as they learn and adapt from the data they process.


  1. Holland, J. H. (1998). Emergence: From chaos to order.
  2. Goldstein, J. (1999). Emergence as a construct: History and issues.
  3. Waldrop, M. M. (1992). Complexity: The emerging science at the edge of order and chaos.

This wiki entry provides an overview of the concept of emergence in systems science, highlighting its characteristics, examples, significance in systems thinking, challenges, and applications across various disciplines.