taxonomy - chunhualiao/public-docs GitHub Wiki

Taxonomy is a way of organizing concepts into categories based on their relationships and characteristics. Below is a simplified taxonomy that categorizes concepts like science, technology, and related fields, including artificial intelligence and methodologies like Monte Carlo Tree Search (MCTS).

Taxonomy of Concepts

Level 1 Level 2 Level 3 Level 4
Knowledge Science Natural Sciences Physics, Chemistry, Biology
Formal Sciences Mathematics, Logic, Statistics
Social Sciences Psychology, Sociology, Economics
Technology Information Technology Software, Hardware, Networking
Engineering Civil, Mechanical, Electrical
Applied Sciences Medicine, Environmental Science
Methodologies Artificial Intelligence Machine Learning Supervised Learning, Unsupervised Learning, Reinforcement Learning
Search Algorithms A* Search, Genetic Algorithms, MCTS
Robotics Autonomous Systems, Control Theory
Data Science Data Analysis, Big Data, Visualization

Explanation of Categories:

  • Knowledge: The broadest category encompassing all forms of understanding and information.

  • Science: Systematic study of the natural and physical world through observation and experimentation.

    • Natural Sciences: Focus on the physical universe and living organisms.
    • Formal Sciences: Concerned with abstract concepts and logical reasoning.
    • Social Sciences: Study of human behavior and societies.

  • Technology: Application of scientific knowledge for practical purposes.

    • Information Technology: Focus on computing and communication technologies.
    • Engineering: Application of scientific principles to design and build structures, machines, and systems.
    • Applied Sciences: Use of scientific principles in practical applications, such as medicine and environmental science.

  • Methodologies: Specific approaches or techniques used to solve problems or conduct research.

    • Artificial Intelligence: Subfield of computer science focused on creating systems that can perform tasks that typically require human intelligence.
    • Machine Learning: A subset of AI that involves training algorithms to learn from and make predictions based on data.
    • Search Algorithms: Techniques for finding solutions in large search spaces, including MCTS.

This taxonomy provides a structured way to understand the relationships between different fields and methodologies, including where MCTS fits within the broader context of science and technology.

Reviews and Comments

This taxonomy, while structured, has some limitations and potential improvements to consider:

  1. Hierarchical Consistency Issues:
  • Artificial Intelligence and Machine Learning are currently listed under "Methodologies," but they are more accurately subfields of Computer Science (which is missing from this taxonomy)
  • The placement of "Methodologies" as a top-level category is problematic because methodologies exist within and across all disciplines
  • Search Algorithms are too specific to be at the same level as AI and ML - they are techniques used within both fields
  1. Scope and Coverage Gaps:
  • Humanities are entirely missing (arts, philosophy, literature, etc.)
  • Mathematics isn't clearly placed (it could fit under Formal Sciences but isn't explicitly listed)
  • Applied Sciences are placed under Technology, but many applied sciences like psychology or ecology don't fit neatly there
  1. Structural Improvements Needed:
  • The relationship between Science and Technology needs more nuance - they're currently shown as parallel categories, but technology often represents the practical application of scientific knowledge
  • Some categories like "Knowledge" are too broad to be useful in classification
  • Missing important interdisciplinary fields and emerging areas

Here's a suggested revision:

  1. Pure Sciences

    • Natural Sciences (Physics, Chemistry, Biology, etc.)
    • Formal Sciences (Mathematics, Logic, Theoretical Computer Science)
    • Social Sciences (Psychology, Sociology, Economics)

  2. Applied Sciences & Technology

    • Engineering Disciplines
    • Information & Computer Sciences
    • Medical & Health Sciences
    • Environmental & Earth Sciences

  3. Humanities & Arts

    • Philosophy
    • Literature & Languages
    • Visual & Performing Arts
    • History & Cultural Studies

  4. Interdisciplinary Fields

    • Cognitive Science
    • Data Science
    • Environmental Studies
    • Biotechnology

This revision:

  • Better reflects the interconnected nature of knowledge domains
  • Provides clearer categorization of emerging fields
  • Maintains logical grouping while acknowledging overlap
  • Includes humanities and arts
  • Places methodologies within their respective fields rather than as a separate category

The key is to recognize that any taxonomy of knowledge is inherently imperfect due to the interconnected nature of disciplines and the constant evolution of fields. A good taxonomy should be flexible enough to accommodate new developments while maintaining logical structure.