Where the EPP propagates causal effects through a dynamic context, Deep Brain propagates knowledge through a dynamically evolving context; it is the epistemic counterpart of the EPP, built on the same process ontology — the philosophical commitment to treat reality as continuous becoming rather than as fixed substance.
The static-snapshot problem
Current database systems treat a knowledge representation as a discrete static snapshot in time. In practice, a knowledge corpus evolves continuously, with no natural snapshot boundaries. The mismatch between the static-snapshot assumption and the continuously evolving reality becomes acute in long-running research workflows where decisions, invalidations, and gaps must be tracked across years of evolution. Deep Brain investigates how a knowledge corpus can instead be represented as a continuously evolving process.
Design direction
The architectural direction treats decisions as first-class objects rather than implicit traces in a commit log; treats invalidations and characterised gaps as queryable structures alongside the knowledge they qualify; and treats the relationship between content and the conditions of its validity (temporal regime, domain, assumption, provenance, person) as a first-class structure in its own right. The goal is a knowledge substrate in which staleness is structurally impossible.
Current status
Deep Brain is presently an ongoing research program. A body of architectural specifications and design notes that articulate the proposal has been made publicly available on GitHub. The exploration of dynamic knowledge representation is the most aspirational of the three research areas, and implementation is a future undertaking. Insights from this area, once verified, are contributed back to the DeepCausality project.
Resources
- deep_brain on GitHub — architectural specifications and design notes
- Dynamic causality — the structural counterpart of this research area