Entropy in Systems Science


In systems science, entropy refers to a measure of the disorder or randomness within a system. It is a concept borrowed from thermodynamics but has been adapted to describe the level of unpredictability or uncertainty in the organization and behavior of complex systems.

Key Characteristics:

Degree of Disorder:

  • Entropy in systems indicates the degree of disorder, randomness, or unpredictability within the system.
  • High entropy suggests a more disordered state, while low entropy indicates a more organized and predictable state.

Information Theory Connection:

  • Entropy is closely related to information theory, where it represents the amount of uncertainty or surprise associated with a set of possible outcomes.

Dynamic Nature:

  • Entropy is dynamic and can change over time as a system evolves.
  • Changes in entropy reflect shifts in the organization and patterns of a system.

Mathematical Representation:

In information theory, entropy (H) is often represented mathematically as:

where ( P(x_i) ) is the probability of occurrence of each possible outcome ( x_i ).


Biological Systems:

  • In biological systems, entropy may relate to the diversity and randomness of genetic information, reflecting the adaptability of a population to environmental changes.

Economic Systems:

  • In economic systems, entropy may be used to describe the level of uncertainty in market dynamics, influenced by factors such as consumer behavior and external shocks.

Ecological Systems:

  • Entropy can describe the diversity and distribution of species in an ecosystem, providing insights into its resilience and adaptability.

Significance in Systems Thinking:

Understanding entropy in systems is vital in systems thinking as it provides a quantitative measure of the system’s organization and predictability. It encourages the exploration of how systems adapt and evolve over time in response to internal and external influences.


Context Sensitivity:

  • Interpreting entropy requires consideration of the specific context and goals of the system under analysis.

Data Quality and Resolution:

  • The accuracy and resolution of data used to calculate entropy can impact the reliability of the assessment.

Application in Various Disciplines:

  • Physics and Thermodynamics: Entropy’s roots lie in thermodynamics, where it characterizes the energy dispersion in physical systems.
  • Information Theory: Entropy is a fundamental concept in information theory, describing the uncertainty associated with random variables.
  • Complex Systems Analysis: Entropy is applied in analyzing complex systems to understand their adaptive capacity and the dynamics of disorder and order.


  1. Shannon, C. E. (1948). A Mathematical Theory of Communication.
  2. Prigogine, I., & Stengers, I. (1984). Order out of Chaos: Man’s New Dialogue with Nature.

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