What question did this study set out to answer?

To explore how human intuition can be understood as a computational process that simplifies complex information.

March 24, 2026Open Access

Intuition and Complexity Theory

Key Points

To explore how human intuition can be understood as a computational process that simplifies complex information.
Proposed a computational framework for intuition as a 'lossy compression algorithm'.
Identified intuition's function as pattern-based compression for subconscious information.
Distinguished between epistemic and ontological complexity in systems.
Showed the brain compresses 11 million bits of subconscious data to 40 bits of conscious output.
Demonstrated that many complex systems lack patterns, challenging traditional views.
Proposed a Circle of Logic that generates testable predictions in cognitive science and machine learning.

Abstract

The connection between complexity of the universe and human intuitionAuthor : Ayman ZadeThis paper presents a novel computational framework proposing that intuition operates as the brain's lossy compression algorithm, processing ~11 million bits/second of subconscious data into ~40 bits/second of conscious prediction. The framework integrates three core discoveries: intuition as pattern-based compression with an "A to Z" cognitive jump, the distinction between epistemic and ontological complexity showing most "complex" systems are merely unpatterned, and prediction as strategic "time warfare"—the only mechanism to gain effective temporal advantage. These elements synthesize into a self-optimizing Circle of Logic with testable predictions across cognitive science, Machine Learning, and strategic theory.

Intuition and Complexity Theory

Key Points

Abstract

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