Answer extracted from the SmarterMarkets podcast — listen to the full episode below.
How do higher dimensions function as compression mechanisms for information?
Individual words alone carry limited information, but when arranged into sequences and organized into story arcs, they unlock lossless compression that preserves all meaning while encoding it in a higher dimension. The human prefrontal cortex—the sensory organ for language and narrative—can store and retrieve far more information when it is woven into stories than when scattered as isolated words.
The key insight is that dimensionality itself functions as a storage and transmission mechanism. As Ben Hunt explains in the episode, this is not a lossy compression where information is sacrificed for space. Rather, information transitions into another dimension of representation—the semantic dimension—where it can be held and recalled more densely by the human brain.
Words, sequence, and narrative as dimensional layers
A single word contains a specific quantity of information. String those words together in sequence and time becomes the organizing principle, adding a new dimension that allows meaning to emerge. But push the sequence further into a narrative arc—with causality, tension, and resolution—and you enter an entirely different order of semantic compression.
This is why stories stick in memory where raw facts do not. The narrative form is not merely a communication device; it is a dimensional encoding that the human brain is natively equipped to process and retain, as Hunt details in his work on the semantic dimension. The information is still there—nothing is lost—but it now exists in a structure that human cognition can absorb and store at vastly greater density.
"Individual words contain limited information, but when arranged in sequence they unlock meaning through time, and when organized into story arcs they convey semantic meaning."
Ben Hunt — President and Co-Founder of Perscient, author of Epsilon Theory. Hunt conducted his dissertation at Harvard in the late 1980s on inference using large unstructured data sets under Gary King at the Social Science Research Center, and has spent approximately 40 years studying how mathematical approaches can illuminate language, meaning, and ideas across markets and beyond.
For those seeking a deeper understanding of how artificial intelligence maps these narrative structures, Hunt's work on using large language models to track semantic signatures across global data reveals the practical implications of this dimensional compression in real time.
The prefrontal cortex as a semantic sense organ
The human prefrontal cortex does not process stories the way it processes lists. Narrative activates a different storage and retrieval architecture in the brain, one that can hold exponentially more information without proportional increase in cognitive load. This is why markets themselves—and the human behavior within them—are fundamentally driven by stories rather than statistics.
Understanding this compression mechanism explains why identical data, presented as isolated facts versus embedded in narrative, produces entirely different outcomes in human decision-making and belief formation.
Key takeaways
- Higher dimensions enable lossless information compression by recoding data into narrative structure rather than losing it.
- Time acts as a fourth dimension organizing word sequences; narrative arcs add a fifth semantic dimension where meaning densifies.
- The human prefrontal cortex stores and retrieves information with vastly greater efficiency when it is encoded as story rather than isolated facts.
- This dimensional compression principle underlies how meaning propagates through markets, media, and human belief systems.
See also
What is the semantic dimension and how does it relate to Plato's allegory of the cave?
The semantic dimension is a fifth dimension of information, meaning, and ideas that exists outside our three-dimensional world, just as time exists as a fourth dimension. It is the realm where language, narrative, and human meaning-making operate.
How can large language models be used to measure and track the spread of ideas through text and narrative?
LLMs function as semantic calculators native to the semantic dimension, allowing measurement of how ideas spread and propagate through text using the same mathematical approaches that govern information flow in higher-dimensional space.