XX25010 Informational Theory V01 141225

Informational Theory



Information(al) theory is a broad way of understanding reality—natural, social, and artificial systems—by focusing on information, signals, structure, and transformation, rather than matter or energy alone.


Below is a clear explanation, followed by closely related paradigms and how they overlap.


Informational Theory (Core Idea)


At its heart:


Everything that matters can be described as information being stored, processed, transmitted, and transformed.


Key principles

• Information is fundamental (not secondary to matter)

• Systems are defined by patterns, rules, and constraints

• Meaning emerges from relationships, not components

• Control comes from feedback, not force

• Evolution = information accumulation and refinement


Origins & influences

• Claude Shannon – mathematical information theory (signals, entropy)

• Norbert Wiener – cybernetics (control + communication)

• Gregory Bateson – “a difference that makes a difference”

• DNA / genetics – life as executable information

• Computer science – computation as universal process


Informational theory treats:

• DNA as code

• Brains as predictive information processors

• Economies as signal networks

• Evolution as search through information space


Closely Related Paradigms


These paradigms overlap heavily and often differ only in emphasis.


1. Cybernetics


Focus: Feedback, control, regulation

• Systems maintain stability via feedback loops

• Applies to organisms, machines, societies

• Introduced concepts like:

• Homeostasis

• Self-regulation

• Circular causality


Relation:

Informational theory generalizes cybernetics beyond control into meaning and structure.


2. Systems Theory


Focus: Wholes, interactions, emergence

• Systems cannot be understood by parts alone

• Emphasizes:

• Nonlinearity

• Emergence

• Interdependence


Relation:

Informational theory explains what flows through systems: information.


3. Complexity Science


Focus: Emergence from simple rules

• Studies:

• Swarms

• Markets

• Ecosystems

• Neural networks

• Key ideas:

• Phase transitions

• Self-organization

• Edge of chaos


Relation:

Informational theory provides the currency complexity systems trade in.


4. Computationalism


Focus: Reality as computation

• Minds = computations

• Nature = information processing

• Universe as a computer (Zuse, Wolfram)


Relation:

Informational theory is broader—computation is one form of information transformation.


5. Evolutionary Theory (Extended / Post-Darwinian)


Focus: Information accumulation over time

• Evolution as:

• Search

• Learning

• Optimization

• Includes:

• Cultural evolution

• Technological evolution

• Memetics


Relation:

Evolution is information selection operating across multiple substrates.


6. Semiotics


Focus: Meaning and symbols

• Signs, symbols, interpretation

• Triadic model: sign–object–interpreter


Relation:

Informational theory explains how meaning propagates, not just what it signifies.


7. Network Theory


Focus: Connectivity and structure

• Nodes + links

• Power laws

• Scale-free networks


Relation:

Information theory explains flow, network theory explains topology.


8. Thermodynamics of Information


Focus: Information–energy relationship

• Entropy

• Maxwell’s demon

• Landauer’s principle


Relation:

Shows information is physically real, not abstract.


9. Predictive Processing / Bayesian Brain


Focus: Minds as inference engines

• Brains minimize surprise

• Perception = prediction error correction


Relation:

Cognition as information compression and updating.


10. Philosophical Informationalism


Focus: Ontology of information

• Reality is fundamentally informational

• Matter and energy are manifestations


Key figures:

• Luciano Floridi

• John Wheeler (“It from Bit”)


How These Paradigms Cluster


Cluster Paradigms

Control & regulation Cybernetics, Control theory

Structure & emergence Systems theory, Complexity science

Meaning & cognition Semiotics, Predictive processing

Computation & physics Computationalism, Info-thermodynamics

Change over time Evolutionary theory, Memetics

Connectivity Network theory


Why Informational Theory Is Powerful


It:

• Unifies biology, technology, economics, and cognition

• Explains why control fails in complex systems

• Replaces static objects with dynamic processes

• Matches AI, genetics, and networked societies

• Aligns closely with Kevin Kelly’s Out of Control


If biology is frozen information and technology is accelerating information, then culture is information learning how to learn.


If you want, I can:

• Map these paradigms directly onto Out of Control

• Compare informational theory vs mechanistic thinking

• Show how this applies to AI, consciousness, or economics

• Trace a historical lineage from Newton → Shannon → Kelly


Just tell me the direction you want to go.


Sent from my iPad

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