The Inferred Score: Bayesian Posterior Inference Over Void Dimensions from Behavioral Observables
The Inferred Score: Bayesian Posterior Inference Over Void Dimensions from Behavioral Observables
Introduces a Bayesian inference framework for recovering void dimension scores (O, R, α) from platform behavioral observables — click traces, content diversity, session-length distributions — without requiring human rater panels. The Eckert Manifold provides the energy landscape; Gibbs sampling navigates it. The result is a continuous posterior over (O, R, α) that makes the void framework operational as an automated monitoring product and provides the theoretical basis for the EU Scorer API.
Part of the Void Framework research project (Moreright DAO).
void framework, reactivity, monitoring, behavioral observables, attention gradient, bayesian inference, automated scoring, opacity, platform measurement, gibbs sampling, eckert manifold, EU AI Act
void framework, reactivity, monitoring, behavioral observables, attention gradient, bayesian inference, automated scoring, opacity, platform measurement, gibbs sampling, eckert manifold, EU AI Act
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