retreivali

Retreivali is a theoretical construct in information theory and temporal data analysis describing retroactive evaluation processes in which the interpretation of past data points can be updated upon receiving new observations, while preserving causal consistency. The term blends retro- meaning backward and evaluare, reflecting its focus on value assignment across time.

In models of retreivali, data are organized along time indices within a causal graph. Update rules (often Bayesian) adjust beliefs about past states when later evidence arrives, without implying backward-in-time signaling.

It overlaps with backcasting, online learning with delayed feedback, and data provenance; retreivali emphasizes explicit retroactive evaluation of past records rather than merely updating predictions for the future.

Applications include theoretical analyses of temporal networks, versioned storage systems, forensic analytics, and time-aware databases.

Criticism and limitations: The notion is controversial because it can blur the line between evidence and history; critics warn about ambiguous semantics, dependence on model assumptions, and risk of paradoxes if constraints are lax.

See also temporal logic, retrocausality, Bayesian inference, data provenance.