Algorithmic capture is an author-defined concept in The Maha Principle. It describes a feedback relationship in which a digital system learns from behaviour, selects what appears next, and can gradually narrow the practical range of attention and choice. The term names a governance problem: the person supplies signals while the system controls the adaptation loop.
The feedback loop
The framework separates a single recommendation from capture. A recommendation becomes relevant to the concept only when observation, prediction, selection, and repeated response reinforce one another over time. This makes the object of analysis the relationship between a person and an adaptive environment, not an isolated item of content.
Related concepts
Attentional captivity concerns the loss of sustained attention. Digital sovereignty concerns control over infrastructure and data. Biological sovereignty supplies the normative boundary: the person’s cognitive and metabolic systems are not merely inputs for an optimization process.
Evidence boundary
This page defines the term as the author uses it. It does not establish a clinical disorder, prove that any named platform intentionally manipulates a user, or transfer the framework to a specific product without separate evidence about that product, population, and context.