Philosophy of Observation

Observational Grammar

A constitution for evidence derived from sensor physics, independent of external incentives

OG-001

Observational Grammar (OG) is the idea that sensors — satellites, radar, spectrometers, thermal cameras — can form a language of evidence about reality that operates independently of human bias, market incentives, or bureaucratic approval chains. Just as grammar gives structure to language, OG gives structure to what instruments can claim about the physical world. It is M33's foundational concept: build systems that let reality set the table, then let markets and decisions work within those constraints, rather than the other way around.

Why It Matters

Every day, thousands of satellites observe Earth. They measure electromagnetic reflectance, radar backscatter, thermal emission, gravitational anomalies, and atmospheric chemistry. Each measurement is a claim about reality — a statement made by physics, not by a person with an agenda.

But between the sensor and the decision-maker, something happens. Data gets filtered through procurement pipelines, formatted for specific use cases, interpreted through institutional lenses, and delivered in reports that serve the needs of whoever commissioned them. The physics remains, but the direct line from observation to understanding gets tangled.

A coastal development gets approved because the regulatory studies — designed to answer a specific bureaucratic question — concluded it was compliant. Meanwhile, the satellites recorded salinity trends, erosion rates, storm frequency patterns, and groundwater changes that told a different story. The instruments saw the failure coming. The decision pipeline didn't ask them the right question.

Observational Grammar is the framework for closing that gap. It asks: what if we started with what the sensors actually see, formed a composite understanding of reality from multiple independent observations, and then let human needs, markets, and policy operate within those physical constraints?

Fabric Connection

OG is a philosophy. Fabric is its implementation.

Fabric is M33's open-source geospatial data harmonization engine. It takes heterogeneous satellite and sensor data — different formats, projections, resolutions, calibrations — and transforms it into analysis-ready data that can be composed across sources. This is the mechanical realization of OG's semantic layer: making it possible for a SAR observation and an optical observation to be combined into a composite evidence statement with tracked provenance.

The three tiers of M33's architecture map directly to the three layers of OG:

Fabric → Syntax & Semantics — harmonizes sensor data into composable, analysis-ready formats with tracked transformations.

Iris → Pragmatics — the AI intelligence layer that applies context, detects patterns, and manages uncertainty in composite observations.

SEAM → Provenance & Trust — the security and compliance layer that ensures every claim in OG can be verified and every transformation audited.

When Fabric processes a fire detection pattern — combining Sentinel-2 thermal bands with burn severity indices and historical fire records — it is executing OG. The pattern report it produces is an OG statement: "These sensors observed these signals, combined they indicate a fire event at this location with this confidence level, and here is the complete chain of evidence."

Philosophical Thread

The living observer. Observational Grammar draws directly from Humberto Maturana's concept of autopoiesis — the idea that living systems are self-producing, self-maintaining organizations. OG is designed as an autopoietic system: it feeds its observations back through quality assessment loops, corrects its own calibration drift, and expands its vocabulary as new sensors and patterns emerge. It is not a static pipeline that processes data from A to B. It is an organism that observes, learns, and reorganizes itself.

Maturana argued that the observer is always part of the observation — that objectivity in the traditional sense is impossible because the instrument shapes what can be seen. OG accepts this. Rather than pretending sensors deliver raw truth, it explicitly models the limitations of each instrument and treats every observation as a claim made by a specific sensor under specific conditions, not as unmediated fact. This is what makes the grammar honest.

Yuval Noah Harari's Nexus provides the other key influence: his argument that the architecture of information networks determines whether they produce truth or delusion. OG's insistence on provenance and chain-of-custody is a direct response to this insight. The question is not just "what does the data say?" but "what kind of information architecture makes it possible to trust what the data says?" Fabric's provenance-first design is an answer to that question.

References

Maturana, H.R. & Varela, F.J. (1980). Autopoiesis and Cognition: The Realization of the Living. Dordrecht: D. Reidel. https://doi.org/10.1007/978-94-009-8947-4
Maturana, H.R. (1988). "Reality: The Search for Objectivity or the Quest for a Compelling Argument." The Irish Journal of Psychology, 9(1), 25–82.
Harari, Y.N. (2024). Nexus: A Brief History of Information Networks from the Stone Age to AI. New York: Random House.
McGilchrist, I. (2021). The Matter With Things: Our Brains, Our Delusions and the Unmaking of the World. London: Perspectiva Press.
Meadows, D.H. (2008). Thinking in Systems: A Primer. White River Junction: Chelsea Green Publishing.
ESA (2024). "Sentinel-2 User Handbook." European Space Agency. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi
CEOS (2023). "Analysis Ready Data for Land: Definition and Requirements." Committee on Earth Observation Satellites. https://ceos.org/ard/
M33 (2025). "Ontology of a Living Observer." Science, Again! https://scienceagain.com/ontology-of-a-living-observer/