View source for General Theory of Thinking Systems

==Meme==
Knowledge can be viewed as Static (Stored) or Active (in-motion) structured data.

==Context==
There are (at least) two views about a [[General Theory of Thinking Systems]]
# A brain or AI is construed of very similar computational elements that can be put token in dynamic ways depending the the problem at hand. (Call it Hume's Approach)
# A brain or AI is construed of very different modules which are constructed to perform very specific tasks, like data storage, image analysis, or speaking from memory. (Call it Chomsky's Approach).<ref>Noam Chomsky, ''On Language''</ref>

==Breakdown==
A “General Theory of Thinking Systems” isn’t a single, monolithic doctrine defined by one author or school of thought. Rather, it’s an interdisciplinary framework—a synthesis of ideas from general systems theory, cognitive science, cybernetics, and artificial intelligence—that aims to explain how various systems (whether human, animal, or artificial) generate, process, and use information to “think” or behave in adaptive ways. Here’s a detailed overview of the key elements and principles that many proponents would agree are part of a general theory of thinking systems:

1. Systems as Hierarchical, Interconnected Networks
Modularity and Hierarchy: Thinking is seen as arising from a network of interacting modules or subsystems. At the most basic level, individual components (whether neurons in a brain or processing units in an artificial network) manage simple tasks. Higher-order cognition emerges from the recursive combination and reorganization of these simpler processes. Think of it as layers: low-level perception feeds into higher-level reasoning, which in turn may inform decision-making and action.

Emergence: A core tenet borrowed from general systems theory is that the whole often exhibits properties that its parts do not have in isolation. In thinking systems, this means that complex phenomena such as consciousness, intuition, or creative problem-solving can emerge through the dynamic interplay of simpler processes—even if no single component “knows” the whole picture.

2. Dynamic Feedback and Self-Organization
Feedback Loops: Just as in cybernetic systems, thinking systems depend on feedback. Information flows continuously between different levels: sensory inputs, internal representations, and motor outputs feed back to alter ongoing computations. This feedback is essential for error correction, adaptation, and learning, allowing the system to self-regulate and maintain what might be called cognitive homeostasis.

Self-Organization: These systems are not static; they evolve as they interact with their environment. Through processes analogous to biological evolution or adaptive learning in neural networks, a thinking system can reorganize its internal structure to better respond to novel situations or challenges. This self-organization is a key mechanism by which both natural intelligence (like the brain) and artificial intelligence (such as deep learning networks) improve over time.

3. Representation, Abstraction, and Symbol Manipulation
Data Encoding and Internal Models: A central question for any thinking system is how it represents and manipulates information. Cognitive science tells us that effective problem solving and decision-making depend on forming internal models or abstractions of the world. These representations may be symbolic (as in traditional rule-based artificial intelligence) or sub-symbolic (as in neural networks and connectionist models), but in either case they provide a substrate on which operations such as reasoning, inference, and planning occur.

Generative Capacity: Generative theories—like those seen in generative grammar for language—show that a finite set of rules or operations can create an infinite variety of outputs. In thinking systems, this idea translates into the notion that a limited—and perhaps innate—set of cognitive rules can be composed and recomposed to solve novel problems or generate creative ideas.

4. Learning, Memory, and Adaptation
Experience-Driven Modification: Central to any theory of thinking is the system’s ability to learn from its past. In biological systems, this is achieved via neuroplasticity and memory formation; in artificial systems, learning algorithms (such as reinforcement learning or backpropagation in neural networks) adjust weights and connections based on feedback from the environment. This continuous learning process is what allows a system to refine its internal models and improve its performance over time.

Memory as a Scaffold for Thought: Systems use memory not just to store data but also to build context—integrating past experiences to better interpret current inputs. Whether through episodic memory (remembering specific events) or semantic memory (storing generalized knowledge), memory plays a crucial role in shaping the reasoning process.

5. Decision-Making and Effecting Change
Cognition to Action Transformation: A thinking system must ultimately translate its internal deliberations into external actions. Decision-making processes, which weigh probabilities, expected outcomes, and potential costs or benefits, are integral. The theoretical framework combines elements of control theory (common in cybernetics) with approaches from economics and psychology that explain how decisions arise from conflicting goals and uncertainties.

Conscious vs. Unconscious Processing: Many modern theories incorporate the idea that some thinking occurs at an unconscious level—rapid, heuristic-based responses—while other processes require deliberate, conscious reasoning. A comprehensive theory must address both kinds of processing and explain how they interact and sometimes compete within a single system.

6. Interdisciplinary Foundations and Ongoing Debates
Influences from Multiple Disciplines:

General Systems Theory: Provides the overarching insight that complex behavior can emerge from simple interactions within a system.

Cybernetics: Emphasizes feedback, control, and adaptive behavior.

Cognitive Science & Psychology: Offer empirical frameworks (like the information-processing model) detailing how humans think, reason, and solve problems.

Artificial Intelligence & Computational Models: Supply algorithms and architectures—ranging from symbolic AI (logic-based systems) to connectionist models (neural networks)—that simulate aspects of thought.

No Final Consensus: Although many contributors have proposed models for understanding thinking, from early symbolic systems put forth by Newell and Simon to contemporary deep learning approaches, there remains debate about how best to capture the full richness of thought in theoretical or computational terms. This continuing dialogue is one reason there isn’t a single, universally accepted “General Theory of Thinking Systems.”

Conclusion
A general theory of thinking systems seeks to synthesize how information is received, processed, and output by systems—whether in human cognition or machine intelligence. It posits that through structured, yet adaptable, networks of interconnected components (subject to feedback and self-organization), a limited set of rules and representations can give rise to an almost limitless variety of behaviors and outputs. While the theory draws on principles from systems theory, cybernetics, cognitive science, and AI, it remains an evolving framework—constantly refined in light of new research and technological advances.

Would you like to explore specific models (such as Minsky’s Society of Mind, or modern neural network approaches) that illustrate these principles in action, or additional case studies comparing natural and artificial thinking systems? There’s a rich palette of interdisciplinary work that further elaborates on these foundational ideas.

==References==
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===Other Material===
* For the way that information interacts in a complete body seem the wiki [[General Theory of Livinging Systems]]

[[Category: Language]]