Toward Operationalizing Rasmussen: Drift Observability on the Simplex for Evolving Systems

Abstract

Monitoring drift into failure is hindered by Euclidean anomaly detection that can conflate safe operational trade-offs with risk accumulation in signals expressed as shares, and by architectural churn that makes fixed schemas (and learned models) stale before rare boundary events occur. Rasmussen's dynamic safety model motivates drift under competing pressures, but operationalizing it for software is difficult because many high-value operational signals (effort, remaining margin, incident impact) are compositional and their parts evolve. We propose a vision for drift observability on the simplex: model drift and boundary proximity in Aitchison geometry to obtain coordinate-invariant direction and distance-to-safety in interpretable balance coordinates. To remain comparable under churn, a monitor would continuously refresh its part inventory and policy-defined boundaries from engineering artifacts and apply lineage-aware aggregation. We outline early-warning diagnostics and falsifiable hypotheses for future evaluation.